"Item type","Authors","Editors","Title","Journal","Full journal","Publication year","Volume","Issue","Pages","Folders filed in","Labels filed in","Institution","Number","Publisher","Address","Book title","Proceedings title","Conference location","Date published","Date accessed","ISBN","ISSN","ISSN (alt.)","URLs","DOI","PMID","Arxiv ID","Associated DOI","PMC ID","Abstract","Keywords","Notes","Copyright","Affiliation","Language","Sub-type","Series","Archive prefix","Eprint ID","Primary class","Page count","School","Source"
"Journal Article","Cantos VD,Rebolledo PA","","Structural Vulnerability to Coronavirus Disease 2019 (COVID-19) Among Latinx Communities in the United States","Clin. Infect. Dis.","Clinical infectious diseases: an official publication of the Infectious Diseases Society of America","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Oxford University Press","","","","","2020-09-12","2020-11-18","","1058-4838","","https://academic.oup.com/cid/advance-article-abstract/doi/10.1093/cid/ciaa1378/5904342;http://dx.doi.org/10.1093/cid/ciaa1378;https://academic.oup.com/cid/advance-article-pdf/doi/10.1093/cid/ciaa1378/33888318/ciaa1378.pdf?casa_token=wXGMXo-wPkQAAAAA:oySsq4dlCG8q0srAMbalCyXDdE5Iu1l17otZwqSLPFXCF1ywiSg_TBojCEcO4MAuW26TXjN0T-SV","10.1093/cid/ciaa1378","","","","","Coronavirus disease 2019 (COVID-19) is disproportionally impacting racial and ethnic minority groups, namely, Black, Latinx, and Native American communities, in","","","","","en","","","","","","","",""
"Review","Evans RM,Lippman SM","","Shining Light on the COVID-19 Pandemic: A Vitamin D Receptor Checkpoint in Defense of Unregulated Wound Healing","Cell Metab.","Cell metabolism","2020","32","5","704-709","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Elsevier","","","","","2020-11-03","","","1550-4131","1932-7420","http://dx.doi.org/10.1016/j.cmet.2020.09.007;https://www.ncbi.nlm.nih.gov/pubmed/32941797;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486067;https://linkinghub.elsevier.com/retrieve/pii/S1550-4131(20)30485-X;https://www.sciencedirect.com/science/article/pii/S155041312030485X?casa_token=HTn0iV1iqEQAAAAA:mdir7KBkHwRmZ_s7v_eXjgkG4AzOTCHwbnixD0s-BzHbbKnWGeW0jD1cc8oilKD6gti-Vx7v7g","10.1016/j.cmet.2020.09.007","32941797","","","PMC7486067","SARS-CoV-2 pneumonitis can quickly strike to incapacitate the lung, leading to severe disease and sometimes death. In this perspective, we suggest that vitamin D deficiency and the failure to activate the vitamin D receptor (VDR) can aggravate this respiratory syndrome by igniting a wounding response in stellate cells of the lung. The FDA-approved injectable vitamin D analog, paricalcitol, suppresses stellate cell-derived murine hepatic and pancreatic pro-inflammatory and pro-fibrotic changes. Therefore, we suggest a possible parallel program in the pulmonary stellate cells of COVID-19 patients and propose repurposing paricalcitol infusion therapy to restrain the COVID-19 cytokine storm. This proposed therapy could prove important to people of color who have higher COVID-19 mortality rates and lower vitamin D levels.","","","","Gene Expression Laboratory, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA. Electronic address: evans@salk.edu. Moores Cancer Center, UC San Diego School of Medicine, La Jolla, CA 92093, USA.","en","Review","","","","","","",""
"Journal Article","Blanchard J,Haile‐Mariam T,Powell N,et al.","","For us, COVID‐19 is personal","Academic","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","","","https://onlinelibrary.wiley.com/doi/abs/10.1111/acem.14016;https://onlinelibrary.wiley.com/doi/pdf/10.1111/acem.14016","","","","","","… is-infecting-killing-black-americans-an-alarmingly-high-rate-post-analysis-shows/. 3. American University. COVID Racial Data Tracker [Internet]. [Cited 2020 May 3]. Accessed from: https://covidtracking.com/race on May 2, 2020 …","","","","","","","","","","","","",""
"Website","Massetti E","","The effect of temperature on the incidence rate of COVID19","","","2021","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2021","2021-04-02","","","","https://www.researchsquare.com/article/rs-212460/latest.pdf","","","","","","… We use daily cases of COVID19 from the Atlantic COVID Tracking Project for states3 and The New York Times for counties,4 hourly weather from … 3 The Atlantic. The Covid Tracking Project , (2020). 4 The New York Times …","","","","","","","","","","","","",""
"Commentary","Struthers SA,Sanghavi SF","","Identifying Disparities in the COVID-19 Pandemic: The Quest for Data","Kidney Med","Kidney medicine","2020","2","5","517-519","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","kidneymedicinejournal.org","","","","","2020-09","","","2590-0595","","http://dx.doi.org/10.1016/j.xkme.2020.08.005;https://www.ncbi.nlm.nih.gov/pubmed/32895644;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462467;https://linkinghub.elsevier.com/retrieve/pii/S2590-0595(20)30183-7;https://www.kidneymedicinejournal.org/article/S2590-0595(20)30183-7/abstract;https://www.kidneymedicinejournal.org/article/S2590-0595(20)30183-7/pdf","10.1016/j.xkme.2020.08.005","32895644","","","PMC7462467","… Published July 5, 2020. Accessed July 26, 2020. https://www.nytimes.com/interactive/2020/ 07/05/us/coronavirus-latinos-african-americans-cdc-data.html. 2. The COVID Racial Data Tracker . The COVID Tracking Project. Accessed July 24, 2020. https://covidtracking.com/race …","","","","Louisiana State University School of Medicine, New Orleans, LA. VA Puget Sound Health Care System, Seattle, WA.","en","Commentary","","","","","","",""
"Preprint Manuscript","Pierson E","","Assessing racial inequality in COVID-19 testing with Bayesian threshold tests","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-11-02","","","","","http://arxiv.org/abs/2011.01179","","","2011.01179","","","There are racial disparities in the COVID-19 test positivity rate, suggesting that minorities may be under-tested. Here, drawing on the literature on statistically assessing racial disparities in policing, we 1) illuminate a statistical flaw, known as infra-marginality, in using the positivity rate as a metric for assessing racial disparities in under-testing; 2) develop a new type of Bayesian threshold test to measure disparities in COVID-19 testing and 3) apply the test to measure racial disparities in testing thresholds in a real-world COVID-19 dataset.","","","","","","","","arXiv","2011.01179","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Do WW","","Factors that contribute to increased risk","rivercountrynews.com","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://rivercountrynews.com/health-equity-considerations-and-racial-and-ethnic-minority-groups-p10800-615.htm","","","","","","… Other resources. The COVID Tracking Project's The COVID Racial Data Tracker external icon. Emory University's COVID-19 Health Equity Interactive Dashboardexternal icon. References. [1] US Department of Health and Human Services. Social Determinants of Health [online] …","","","","","","","","","","","","",""
"Preprint Manuscript","Cheng C,Zhou H,Weiss JC,Lipton ZC","","Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-09","","","","","http://arxiv.org/abs/2012.04825","","","2012.04825","","","Since the COVID-19 pandemic first reached the United States, the case fatality rate has fallen precipitously. Several possible explanations have been floated, including greater detection of mild cases due to expanded testing, shifts in age distribution among the infected, lags between confirmed cases and reported deaths, improvements in treatment, mutations in the virus, and decreased viral load as a result of mask-wearing. Using both Florida line-level data and recently released (but incomplete) national line level data from April 1, 2020 to November 1, 2020 on cases, hospitalizations, and deaths--each stratified by age--we unpack the drop in case fatality rate (CFR). Under the hypothesis that improvements in treatment efficacy should correspond to decreases in hospitalization fatality rate (HFR), we find that improvements in the national data do not always match the story told by Florida data. In the national data, treatment improvements between the first wave and the second wave appear substantial, but modest when compared to the drop in aggregate CFR. By contrast, possibly due to constrained resources in a much larger second peak, Florida data suggests comparatively little difference between the first and second wave, with HFR slightly increasing in every age group. However, by November 1st, both Florida and national data suggest significant decreases in age-stratified HFR since April 1st. By accounting for several confounding factors, our analysis shows how age-stratified HFR can provide a more realistic picture of treatment improvements than CFR. One key limitation of our analysis is that the national line-level data remains incomplete and plagued by artifacts. Our analysis highlights the crucial role that this data can play but also the pressing need for public, complete, and high-quality age-stratified line-level data for both cases, hospitalizations, and deaths for all states.","","","","","","","","arXiv","2012.04825","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Dwarakanathan H,Kakade O,Bhardwaj A,Holy C,Gurubaran A,Shah S,Coplan P","","PIN111 Predicting the Decline in Sars-COV-2 New Infections: A Modelling Analysis of US Counties","Value Health","Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research","2020","23","","S562","COVID Tracking Project","","","","Elsevier","","","","","2020-12-01","","","1098-3015","","https://doi.org/10.1016/j.jval.2020.08.952;http://dx.doi.org/10.1016/j.jval.2020.08.952;https://www.valueinhealthjournal.com/article/S1098-3015(20)33208-3/abstract","10.1016/j.jval.2020.08.952","","","","","","","","","","","","","","","","","",""
"Journal Article","Volkow ND,Gordon JA,Freund MP","","The Healthy Brain and Child Development Study—Shedding Light on Opioid Exposure, COVID-19, and Health Disparities","JAMA Psychiatry","JAMA psychiatry ","2020","","","","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-12-09","2020-12-15","","2168-622X","","https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2774000;http://dx.doi.org/10.1001/jamapsychiatry.2020.3803;https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2774000","10.1001/jamapsychiatry.2020.3803","","","","","This Viewpoint describes the planned Healthy Brain and Child Development Study, which will investigate the associations of parental substance abuse, coronavirus disease 2019 infection, and exposure to health disparities with their children’s health and well-being.","brain; opioids; health disparity; covid-19; child development; infections; substance abuse","","","","","","","","","","","",""
"Journal Article","Lemaitre M,Fouad F,Carrat F,Crépey P,Gaillat J,Gavazzi G,Launay O,Mosnier A,Levant MC,Uhart M","","PIN72 Burden of Influenza-Related and Associated Hospitalizations in France from Season 2010/11 to 2017/18","Value Health","Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research","2020","23","","S557","COVID Tracking Project","","","","Elsevier","","","","","2020-12-01","","","1098-3015","","https://doi.org/10.1016/j.jval.2020.08.913;http://dx.doi.org/10.1016/j.jval.2020.08.913;https://www.valueinhealthjournal.com/article/S1098-3015(20)33169-7/abstract","10.1016/j.jval.2020.08.913","","","","","","","","","","","","","","","","","",""
"Journal Article","Siedschlag A","","Pennsylvania’s COVID-19 Response vs. Homeland Security Frameworks and Research: Masking the Whole Community","hsaj.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.hsaj.org/resources/uploads/2020/12/hsaj_Covid192020_PennsylvaniaCOVID-19Response-.pdf","","","","","","This essay offers an intermediate discussion of select policy, strategic, operafional, and tacfical issues that demonstrate where and how the Commonwealth of Pennsylvania's novel coronavirus response on the one hand, and homeland security frameworks and research on the other, converge or—more often so—diverge, and how to narrow this gap. Although typically framed as a pandemic owned by the public health sector, the COVID-19 response falls directly within the homeland security mission space, whose core missions include …","","","","","","","","","","","","",""
"Journal Article","Price CC,Klima K,Propp AM,Colbert-Kelly S","","A Model of the Spread of the COVID-19 Pandemic During a Hurricane in Virginia","rand.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.rand.org/content/dam/rand/pubs/research_reports/RRA300/RRA323-2/RAND_RRA323-2.pdf","","","","","","The study underlying this report was conducted during the coronavirus disease 2019 pandemic to provide analysis of the implications of a hurricane during a pandemic for the Commonwealth of Virginia. The intent is that this analysis can be used to inform planning well in advance of a hurricane threatening Virginia and also in response to a specific storm with an estimated track through Virginia. This research was funded by the Virginia Department of Emergency Management and carried out within the Access and Delivery …","","","","","","","","","","","","",""
"Journal Article","Peitz GW,Seifi A","","Psychological Implications of Mandatory Testing for Severe Acute Respiratory Syndrome Coronavirus 2 During the Global COVID-19 Pandemic","J. Neurol. Res.","Journal of neurology research","2020","10","6","207-208","COVID Tracking Project","","","","neurores.org","","","","","2020-12-09","2020-12-15","","1923-2845","","https://www.neurores.org/index.php/neurores/article/view/634/617;http://dx.doi.org/10.14740/jnr.v10i6.634","10.14740/jnr.v10i6.634","","","","","Psychological Implications of Mandatory Testing for Severe Acute Respiratory Syndrome Coronavirus 2 During the Global COVID-19 Pandemic","","","","","en","","","","","","","",""
"Journal Article","Sisk B,Cull W,Harris JM,Rothenburger A,Olson L","","National Trends of Cases of COVID-19 in Children Based on US State Health Department Data","Pediatrics","Pediatrics","2020","146","6","","COVID Tracking Project","","","","Am Acad Pediatrics","","","","","2020-12","","","0031-4005","1098-4275","http://dx.doi.org/10.1542/peds.2020-027425;https://www.ncbi.nlm.nih.gov/pubmed/32994175;http://pediatrics.aappublications.org/cgi/pmidlookup?view=long&pmid=32994175;https://pediatrics.aappublications.org/content/146/6/e2020027425.abstract","10.1542/peds.2020-027425","32994175","","","","Skip to main content. Advertising Disclaimer ». Main menu. Journals: Pediatrics; Hospital Pediatrics; Pediatrics in Review; NeoReviews; AAP Grand Rounds; AAP News. Authors/Reviewers: Submit Manuscript; Author Guidelines; Reviewer …","","","","American Academy of Pediatrics, Itasca, Illinois; and. American Academy of Pediatrics, Itasca, Illinois; and wcull@aap.org. Children's Hospital Association, Washington, District of Columbia.","en","Research Article","","","","","","",""
"Journal Article","Waters R","","The Big Idea Behind A New Model Of Small Nursing Homes","Health Aff.","Health affairs","2021","40","3","378-383","COVID Tracking Project","","","","Health Affairs","","","","","2021-03-01","","","0092-8577","0278-2715","https://doi.org/10.1377/hlthaff.2021.00081;http://dx.doi.org/10.1377/hlthaff.2021.00081;https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2021.00081;https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2021.00081","10.1377/hlthaff.2021.00081","","","","","Long-term care facilities have been devastated by COVID-19, with one exception: a group of small facilities called Green Houses.","","","","","","","","","","","","",""
"Journal Article","Hsu P,Hayes-Bautista DE","","The Epidemiology of Diversity: COVID-19 Case Rate Patterns in California","J. Immigr. Minor. Health","Journal of immigrant and minority health / Center for Minority Public Health","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-02-23","","","1557-1912","1557-1920","http://dx.doi.org/10.1007/s10903-021-01159-x;https://www.ncbi.nlm.nih.gov/pubmed/33620661;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901169;https://dx.doi.org/10.1007/s10903-021-01159-x;https://link.springer.com/article/10.1007/s10903-021-01159-x","10.1007/s10903-021-01159-x","33620661","","","PMC7901169","California's diverse population provides a natural laboratory for understanding how diseases and conditions interact within different racial/ethnic groups. This report seeks to illustrate the differential effects of the COVID-19 pandemic in the state's \"majority-minority\" population and to discuss the resulting implications for public health. Laboratory-confirmed COVID-19 cases in California (disaggregated by race/ethnicity into mutually exclusive groups) were integrated with their respective population values to create case rates per 100,000 population, categorized by age group and race/ethnicity. The case rates within each non-White population, in almost every age group, were higher than the White Non-Hispanic population, ranging from one-and-a-half to nearly six times as high. Public health prevention measures such as sheltering-at-home rely on standard assumptions and models. The disparity in case rates found here suggests that alternative narratives such as the epidemiology of diversity may inform additional policies or measures.","Covid-19; Epidemiology; Morbidity; Race/ethnicity","","","Department of Epidemiology, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, 924 Westwood Blvd., Suite #200-R, Los Angeles, CA, 90024, USA. paulhsu@ucla.edu. Center for the Study of Latino Health and Culture, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Irons NJ,Raftery AE","","Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-22","","","","","http://arxiv.org/abs/2102.10741","","","2102.10741","","","There are many sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results come with a big delay. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effects of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining the main available data sources. It is based on a discrete-time SIR model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data of deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the USA. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress towards herd immunity.","","","","","","","","arXiv","2102.10741","stat.AP","","","arXiv [stat.AP]"
"Preprint Manuscript","Duff MC","","Challenges for Black Workers After 2020: Antiracism in the Gig Economy?","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-23","2021-04-02","","","","https://papers.ssrn.com/abstract=3791758;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3791758;http://dx.doi.org/10.2139/ssrn.3791758;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3791758","10.2139/ssrn.3791758","","","","","Black workers’ fortunes in the coming decades are tied to the expansion of the Gig economy, the impact of which is to destroy employee status. Because much antiracism law and policy has been transmitted to society through the medium of employment law, the disappearance of employee status should be of concern to all foes of racism. This short essay argues that Section 1981 of the Civil Rights Act of 1866 should be expanded to cover all forms of racist workplace conduct. Regulatory arbitrage will continue to challenge the definition of employment for the foreseeable future. It is fitting that one of the great antiracist laws in the history of the United States be modified to cut through the haze, ensuring that Black workers have remedies for racist workplace conduct, however the workplace may be fortuitously or strategically defined, now or in the future. Acceptable, but not quite as good, alternatives to expanding Section 1981 are to explicitly cover independent contractors with existing antiracist employment law (such as Title VII of the Civil Rights Act of 1964); or to embrace the “ABC” employment test, which makes it much more difficult for employers to inappropriately classify employees (entitled to the protections of antiracist and other employment laws) as independent contractors (who are not entitled to those protections).","Gig economy, Title VII, Antiracism, employee status, Proposition 22, independent contractor","","","","","","","","","","","","Available at SSRN 3791758"
"Preprint Manuscript","Hartl T","","Monitoring the pandemic: A fractional filter for the COVID-19 contact rate","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-19","","","","","http://arxiv.org/abs/2102.10067","","","2102.10067","","","This paper aims to provide reliable estimates for the COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. From observable data on confirmed, recovered, and deceased cases, a noisy measurement for the contact rate can be constructed. To filter out measurement errors and seasonality, a novel unobserved components (UC) model is set up. It specifies the log contact rate as a latent, fractionally integrated process of unknown integration order. The fractional specification reflects key characteristics of aggregate social behavior such as strong persistence and gradual adjustments to new information. A computationally simple modification of the Kalman filter is introduced and is termed the fractional filter. It allows to estimate UC models with richer long-run dynamics, and provides a closed-form expression for the prediction error of UC models. Based on the latter, a conditional-sum-of-squares (CSS) estimator for the model parameters is set up that is shown to be consistent and asymptotically normally distributed. The resulting contact rate estimates for several countries are well in line with the chronology of the pandemic, and allow to identify different contact regimes generated by policy interventions. As the fractional filter is shown to provide precise contact rate estimates at the end of the sample, it bears great potential for monitoring the pandemic in real time.","","","","","","","","arXiv","2102.10067","econ.EM","","","arXiv [econ.EM]"
"Journal Article","Kim H,Simpson J,Park B","","Predictive analyses of COVID‐19 case data to estimate the effectiveness of nationwide face cover","World Med. Health Policy","World medical & health policy","2021","","wmh3.399","","COVID Tracking Project","","","","Wiley","","","","","2021-02-17","","","2153-2028","1948-4682","https://onlinelibrary.wiley.com/doi/10.1002/wmh3.399;http://dx.doi.org/10.1002/wmh3.399;https://onlinelibrary.wiley.com/doi/abs/10.1002/wmh3.399?casa_token=vgwL5CEqDO8AAAAA:-I9_zqqWo2sw6SFipdLBS-gl-18E1-PuisuvrWCLIBKGrmcZp3_l9uSH2U8MRFs5JtrSUiZMdH3Eor_n;https://onlinelibrary.wiley.com/doi/pdf/10.1002/wmh3.399?casa_token=ESJkBjjWt9wAAAAA:VNSQxcK4bM8y4iLLyuQczCNMlJKvUrrA5k-8Yyi-fSzivFxWzlc72myhCh1lcwy2B5PS2r1NSUiDoTa3","10.1002/wmh3.399","","","","","This study provided comparisons of confirmed cases between face-cover-required states and partially or not-required states from a time-series analysis on effects of face mask use in public based on eight different states between March 1 and June 15, 2020. In comparing face-cover-required states and partially or not-required states, it was very encouraging that the slope of the daily case trends turned negative after face-cover requirements in statewide face-cover-required states, including New York, New Jersey, Pennsylvania, and Connecticut. However, the patterns of the daily case have been showing positive trends continuously in partially or not-required states, including California, Texas, Florida, and Virginia. Based on our prediction model, if nationwide face-cover requirements with social distancing were enacted on March 16, the estimated number of deaths would be about 15,600, which is 94,300 less than the actual number of deaths by June 15, 2020. We recommend that all states and the federal government require face coverings in order to reduce the risk of infectious diseases.","","","http://onlinelibrary.wiley.com/termsAndConditions#vor","","en","","","","","","","",""
"Journal Article","Tieskens K,Patil P,Levy JI,Brochu P,Lane KJ,Fabian MP,Carnes F,Haley BM,Spangler KR,Leibler JH","","Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts","Research Square","","2021","","","rs-3","COVID Tracking Project","","","","American Journal Experts","","","","","2021","","","","","https://www.researchsquare.com/article/rs-237622/latest.pdf","","","","","","… JAMA. 2020;323(19):1905-1906. doi:10.1001/jama.2020.6547 27. The Atlantic. The COVID Tracking Project . Published February 4, 2020. Accessed February 4, 2021. https://covidtracking.com/race/dashboard#state-ma 28. McMichael TM, Currie DW, Clark S, et …","","","","","","","","","","","","",""
"Journal Article","Do DP,Frank R","","Unequal burdens: assessing the determinants of elevated COVID-19 case and death rates in New York City's racial/ethnic minority neighbourhoods","J. Epidemiol. Community Health","Journal of epidemiology and community health","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","jech.bmj.com","","","","","2020-10-29","","","0141-7681","","http://dx.doi.org/10.1136/jech-2020-215280;https://www.ncbi.nlm.nih.gov/pubmed/33122256;https://jech.bmj.com/lookup/pmidlookup?view=long&pmid=33122256;https://jech.bmj.com/content/early/2020/10/29/jech-2020-215280?utm_campaign=jech&utm_content=consumer&utm_medium=cpc&utm_source=trendmd&utm_term=usage-042019","10.1136/jech-2020-215280","33122256","","","","BACKGROUND: The disproportionate burden of the COVID-19 pandemic on racial/ethnic minority communities has revealed glaring inequities. However, multivariate empirical studies investigating its determinants are still limited. We document variation in COVID-19 case and death rates across different racial/ethnic neighbourhoods in New York City (NYC), the initial epicentre of the U.S. coronavirus outbreak, and conduct a multivariate ecological analysis investigating how various neighbourhood characteristics might explain any observed disparities. METHODS: Using ZIP-code-level COVID-19 case and death data from the NYC Department of Health, demographic and socioeconomic data from the American Community Survey and health data from the Centers for Disease Control's 500 Cities Project, we estimated a series of negative binomial regression models to assess the relationship between neighbourhood racial/ethnic composition (majority non-Hispanic White, majority Black, majority Hispanic and Other-type), neighbourhood poverty, affluence, proportion of essential workers, proportion with pre-existing health conditions and neighbourhood COVID-19 case and death rates. RESULTS: COVID-19 case and death rates for majority Black, Hispanic and Other-type minority communities are between 24% and 110% higher than those in majority White communities. Elevated case rates are completely accounted for by the larger presence of essential workers in minority communities but excess deaths in Black neighbourhoods remain unexplained in the final model. CONCLUSIONS: The unequal COVID-19 case burden borne by NYC's minority communities is closely tied to their representation among the ranks of essential workers. Higher levels of pre-existing health conditions are not a sufficient explanation for the elevated mortality burden observed in Black communities.","Epidemics; Ethnicity; Health inequalities; Neighbourhood/place; Social inequalities","","","Public Health Policy & Administration, Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA dphuong@uwm.edu. Department of Sociology, Ohio State University, Columbus, Ohio, USA.","en","Research Article","","","","","","",""
"Journal Article","Do WW","","Health Equity Considerations and Racial and Ethnic Minority Groups Health Equity Considerations and Racial and Ethnic Minority Groups","cdc.gov","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-cov%2Fneedextra-precautions%2Fracial-ethnic-minorities.html","","","","","","Systemic health and social inequities have put people from racial and ethnic minority groups at increased risk from COVID-19. Take steps to reduce health disparities.","","","","","","","","","","","","",""
"Journal Article","Michaels D,Wagner GR","","Occupational Safety and Health Administration (OSHA) and Worker Safety During the COVID-19 Pandemic","JAMA","JAMA: the journal of the American Medical Association","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","jamanetwork.com","","","","","2020-09-16","","","0098-7484","1538-3598","http://dx.doi.org/10.1001/jama.2020.16343;https://www.ncbi.nlm.nih.gov/pubmed/32936212;https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2020.16343;https://jamanetwork.com/journals/jama/article-abstract/2770890;https://jamanetwork.com/journals/jama/fullarticle/2770890","10.1001/jama.2020.16343","32936212","","","","… Coronavirus disease 2019 (COVID-19): cases in the US. Accessed September 11, 2020. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html. 2. The COVID Racial Data Tracker website. Accessed August 11, 2020 …","","","","Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.","en","Research Article","","","","","","",""
"News","Jaklevic MC","","Researchers Strive to Recruit Hard-Hit Minorities Into COVID-19 Vaccine Trials","JAMA","JAMA: the journal of the American Medical Association","2020","324","9","826-828","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","jamanetwork.com","","","","","2020-09-01","","","0098-7484","1538-3598","http://dx.doi.org/10.1001/jama.2020.11244;https://www.ncbi.nlm.nih.gov/pubmed/32789501;https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2020.11244;https://jamanetwork.com/journals/jama/article-abstract/2769611?casa_token=lCntkxWALDkAAAAA:jIsz-mjj2QgpyIoWBYDOGhKdpUOyW40gk5ZYQsQrX0CAkwm9VJs4Es9hpKo1yaHcn4y6W-n0Lw;https://jamanetwork.com/journals/jama/fullarticle/2769611?casa_token=-yrwhlj8YD4AAAAA:c7TiYILSn_i4gGXMcujHyslQDdOTnHx9eVgYXHJW1P7D8rkqg_zD3RrNT1biNyqe8gWdFqjz5A","10.1001/jama.2020.11244","32789501","","","","… According to the COVID Racial Data Tracker , a collaboration between The Atlantic and Boston University's Center for Antiracist Research that compiles data from state and local health authorities, Black people die at nearly 2.5 times the rate of White people; Hispanic and …","","","","","en","News","","","","","","",""
"Journal Article","Yum S","","Informatics for COVID-19 in New York and California","Disaster Med. Public Health Prep.","Disaster medicine and public health preparedness","2021","","","1-14","COVID Tracking Project","","","","cambridge.org","","","","","2021-02-16","","","1935-7893","1938-744X","http://dx.doi.org/10.1017/dmp.2021.53;https://www.ncbi.nlm.nih.gov/pubmed/33588982;https://www.cambridge.org/core/product/identifier/S1935789321000537/type/journal_article;https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/informatics-for-covid19-in-new-york-and-california/0E1C448E2B01583D5809C71D98883058","10.1017/dmp.2021.53","33588982","","","","OBJECTIVE: This study explores how social networks for COVID-19 are differentiated by regions. METHODS: This study employs social network analysis for Twitter in New York and California. RESULTS: National key players play an important role in New York, while regional key players exert a significant impact on California. Some key players, such as the US president, play an essential role in both New York and California. Hispanic key players play a crucial role in California. Each group is more likely to show communication networks within groups in New York, while it is more apt to exhibit communication networks across groups in California. Government players play a different role in social networks according to regions. CONCLUSIONS: Governments should understand how social networks for COVID-19 are differentiated by regions to control the ongoing pandemic effectively.","COVID-19; California; Coronavirus; New York; social network analysis","","","Design, Construction, and Planning, University of Florida.","en","Research Article","","","","","","",""
"Journal Article","Levison ME","","HEALTH TOPICS","Update","Update ","2020","7","","30","COVID Tracking Project","","","","merckmanuals.com","","","","","2020","","","","","https://www.merckmanuals.com/home/resourcespages/covid-19-what-we-know-about-coronaviruses","","","","","","Merck Manual. Please confirm that you are not located inside the Russian Federation. Yes No. Leave this Site? The link you have selected will take you to a third-party website. We do not control or have responsibility for the content of any third-party site. Continue Cancel","","","","","","","","","","","","",""
"Journal Article","Hendl T,Chung R,Wild V","","Pandemic Surveillance and Racialized Subpopulations: Mitigating Vulnerabilities in COVID-19 Apps","J. Bioeth. Inq.","Journal of bioethical inquiry","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Springer","","","","","2020-08-25","","","1176-7529","","http://dx.doi.org/10.1007/s11673-020-10034-7;https://www.ncbi.nlm.nih.gov/pubmed/32840858;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7445800;https://dx.doi.org/10.1007/s11673-020-10034-7;https://link.springer.com/article/10.1007/s11673-020-10034-7?fbclid=IwAR2g9ghfutoQwW28gQwLlMnzHx_QBVgLxqYPrb4uJ3RIFuztEILtOBo07K8","10.1007/s11673-020-10034-7","32840858","","","PMC7445800","Debates about effective responses to the COVID-19 pandemic have emphasized the paramount importance of digital tracing technology in suppressing the disease. So far, discussions about the ethics of this technology have focused on privacy concerns, efficacy, and uptake. However, important issues regarding power imbalances and vulnerability also warrant attention. As demonstrated in other forms of digital surveillance, vulnerable subpopulations pay a higher price for surveillance measures. There is reason to worry that some types of COVID-19 technology might lead to the employment of disproportionate profiling, policing, and criminalization of marginalized groups. It is, thus, of crucial importance to interrogate vulnerability in COVID-19 apps and ensure that the development, implementation, and data use of this surveillance technology avoids exacerbating vulnerability and the risk of harm to surveilled subpopulations, while maintaining the benefits of data collection across the whole population. This paper outlines the major challenges and a set of values that should be taken into account when implementing disease surveillance technology in the pandemic response.","COVID-19; Digital health technologies; Equity; Justice; Pandemic disease surveillance; Racial inequality; Racialized subpopulations; Solidarity COVID-19 apps; Vulnerability","","","Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University in Munich, Lessingstr. 2, 80336, Munich, Germany. tereza.hendl@med.uni-muenchen.de. Department of Philosophy, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada. Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University in Munich, Lessingstr. 2, 80336, Munich, Germany.","en","Research Article","","","","","","",""
"Journal Article","Stokes EK,Zambrano LD,Anderson KN,Marder EP,Raz KM,El Burai Felix S,Tie Y,Fullerton KE","","Coronavirus Disease 2019 Case Surveillance - United States, January 22-May 30, 2020","MMWR Morb. Mortal. Wkly. Rep.","MMWR. Morbidity and mortality weekly report","2020","69","24","759-765","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-06-19","","","0149-2195","1545-861X","http://dx.doi.org/10.15585/mmwr.mm6924e2;https://www.ncbi.nlm.nih.gov/pubmed/32555134;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302472;https://doi.org/10.15585/mmwr.mm6924e2;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302472/","10.15585/mmwr.mm6924e2","32555134","","","PMC7302472","The coronavirus disease 2019 (COVID-19) pandemic resulted in 5,817,385 reported cases and 362,705 deaths worldwide through May, 30, 2020,† including 1,761,503 aggregated reported cases and 103,700 deaths in the United States.§ Previous analyses during February-early April 2020 indicated that age ≥65 years and underlying health conditions were associated with a higher risk for severe outcomes, which were less common among children aged <18 years (1-3). This report describes demographic characteristics, underlying health conditions, symptoms, and outcomes among 1,320,488 laboratory-confirmed COVID-19 cases individually reported to CDC during January 22-May 30, 2020. Cumulative incidence, 403.6 cases per 100,000 persons,¶ was similar among males (401.1) and females (406.0) and highest among persons aged ≥80 years (902.0). Among 599,636 (45%) cases with known information, 33% of persons were Hispanic or Latino of any race (Hispanic), 22% were non-Hispanic black (black), and 1.3% were non-Hispanic American Indian or Alaska Native (AI/AN). Among 287,320 (22%) cases with sufficient data on underlying health conditions, the most common were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Overall, 184,673 (14%) patients were hospitalized, 29,837 (2%) were admitted to an intensive care unit (ICU), and 71,116 (5%) died. Hospitalizations were six times higher among patients with a reported underlying condition (45.4%) than those without reported underlying conditions (7.6%). Deaths were 12 times higher among patients with reported underlying conditions (19.5%) compared with those without reported underlying conditions (1.6%). The COVID-19 pandemic continues to be severe, particularly in certain population groups. These preliminary findings underscore the need to build on current efforts to collect and analyze case data, especially among those with underlying health conditions. These data are used to monitor trends in COVID-19 illness, identify and respond to localized incidence increase, and inform policies and practices designed to reduce transmission in the United States.","","","","CDC COVID-19 Emergency Response.","en","Research Article","","","","","","",""
"Journal Article","Krieger N,Waterman PD,Chen JT","","COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020","Am. J. Public Health","American journal of public health","2020","110","12","1850-1852","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","ajph.aphapublications.org","","","","","2020-12","","","0090-0036","1541-0048","http://dx.doi.org/10.2105/AJPH.2020.305913;https://www.ncbi.nlm.nih.gov/pubmed/33058698;https://www.ajph.org/doi/10.2105/AJPH.2020.305913?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2020.305913;https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.2020.305913","10.2105/AJPH.2020.305913","33058698","","","","Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data.Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics.Results. At the surge peak (week 16, April 15-21), mortality rate ratios (comparing 2020 vs 2015-2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0).Conclusions. The COVID-19 mortality surge exhibited large inequities.Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.","","","","The authors are with the Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA.","en","Research Article","","","","","","",""
"Journal Article","Atherton R","","“Missing/Unspecified”: Demographic Data Visualization During the COVID-19 Pandemic","Journal of Business and Technical Communication","Journal of Business and Technical Communication","2021","35","1","80-87","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","SAGE Publications Inc","","","","","2021-01-01","","","1050-6519","","https://doi.org/10.1177/1050651920957982;http://dx.doi.org/10.1177/1050651920957982;https://journals.sagepub.com/doi/abs/10.1177/1050651920957982?casa_token=pUTnaEAqoWkAAAAA:CxkSyUXBYdiBsGgW8qZogOQ2QWe_b4EJ0wjsHA9BMIuW9Oq9QIjfLtuKD8eJ0C_7EHrmFwVJ-qL7;https://journals.sagepub.com/doi/full/10.1177/1050651920957982?casa_token=GNgKqRFQypEAAAAA:Kx9AWY8DSQ4A619cr3fnZ_smnmJZdjItPN8dW53OtTA-u1UWfqWAT6_TmpN3ugSXzX5NM2_Z66ld","10.1177/1050651920957982","","","","","While data1 has shown that COVID-19 disproportionately affects Black people, the CDC?s early data listed race as ?missing/unspecified? at high rates. Incomplete demographic data obscures the virus?s full impact on marginalized communities. Without more information about who the virus is affecting and how, we cannot protect our most vulnerable. This article demonstrates disconnects between reported datasets and data visualizations in public-facing COVID health and science communication and suggests steps that technical and professional communicators can take in creating or using data visualizations accurately and ethically to describe COVID conditions and impacts.","","","","","","","","","","","","",""
"Journal Article","Raine S,Liu A,Mintz J,Wahood W,Huntley K,Haffizulla F","","Racial and Ethnic Disparities in COVID-19 Outcomes: Social Determination of Health","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2020","17","21","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","mdpi.com","","","","","2020-11-03","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph17218115;https://www.ncbi.nlm.nih.gov/pubmed/33153162;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7663309;https://www.mdpi.com/resolver?pii=ijerph17218115;https://www.mdpi.com/1660-4601/17/21/8115;https://www.mdpi.com/1660-4601/17/21/8115/pdf","10.3390/ijerph17218115","33153162","","","PMC7663309","As of 18 October 2020, over 39.5 million cases of coronavirus disease 2019 (COVID-19) and 1.1 million associated deaths have been reported worldwide. It is crucial to understand the effect of social determination of health on novel COVID-19 outcomes in order to establish health justice. There is an imperative need, for policy makers at all levels, to consider socioeconomic and racial and ethnic disparities in pandemic planning. Cross-sectional analysis from COVID Boston University's Center for Antiracist Research COVID Racial Data Tracker was performed to evaluate the racial and ethnic distribution of COVID-19 outcomes relative to representation in the United States. Representation quotients (RQs) were calculated to assess for disparity using state-level data from the American Community Survey (ACS). We found that on a national level, Hispanic/Latinx, American Indian/Alaskan Native, Native Hawaiian/Pacific Islanders, and Black people had RQs > 1, indicating that these groups are over-represented in COVID-19 incidence. Dramatic racial and ethnic variances in state-level incidence and mortality RQs were also observed. This study investigates pandemic disparities and examines some factors which inform the social determination of health. These findings are key for developing effective public policy and allocating resources to effectively decrease health disparities. Protective standards, stay-at-home orders, and essential worker guidelines must be tailored to address the social determination of health in order to mitigate health injustices, as identified by COVID-19 incidence and mortality RQs.","COVID-19 pandemic; disadvantaged populations; gender; intersectionality; occupational risk; poverty; race; social determination of health","","","Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Davie, FL 33328, USA. Department of Internal Medicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Davie, FL 33328, USA.","en","Research Article","","","","","","",""
"Journal Article","Le TK,Cha L,Han HR,Tseng W","","Anti-Asian Xenophobia and Asian American COVID-19 Disparities","Am. J. Public Health","American journal of public health","2020","110","9","1371-1373","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","ajph.aphapublications.org","","","","","2020-09","","","0090-0036","1541-0048","http://dx.doi.org/10.2105/AJPH.2020.305846;https://www.ncbi.nlm.nih.gov/pubmed/32783714;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427240;https://www.ajph.org/doi/10.2105/AJPH.2020.305846?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2020.305846?casa_token=155AMj4dLzEAAAAA:R-RR__ZqvPMuufxYJB230XM4suIHqmUwTorHV2DK1_xOTE0LG5ysENpWN6QDlHA04zlbGngIKA;https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.2020.305846?casa_token=ZL2OvjxuZZ0AAAAA:BlOjLZleo7Nqg1f6xb4eoQfrEvoiE8tY46uEdtaCPysRIgbiVePlHLs2Q0fHGNzz-nILUpCRLw","10.2105/AJPH.2020.305846","32783714","","","PMC7427240","… National Asian Pacific Center on Aging Data Brief. 2013;1(3):1–8. Available at: https://www.napca. org/wp-content/uploads/2017/10/65-population-report-FINAL.pdf. Accessed May 17, 2020. Google Scholar. 5. The COVID Racial Data Tracker . The COVID Tracking Project. 2020 …","","","","Thomas K. Le is with the Johns Hopkins University School of Medicine, Baltimore, MD. Leah Cha is with the University of Wisconsin School of Medicine and Public Health, Madison. Hae-Ra Han is with the Johns Hopkins University School of Nursing and the Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD. Winston Tseng is with the University of California, Berkeley School of Public Health.","en","Research Article","","","","","","",""
"Journal Article","Townsend MJ,Kyle TK,Stanford FC","","Outcomes of COVID-19: disparities in obesity and by ethnicity/race","Int. J. Obes. ","International journal of obesity ","2020","44","9","1807-1809","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","nature.com","","","","","2020-09","","","0307-0565","1476-5497","http://dx.doi.org/10.1038/s41366-020-0635-2;https://www.ncbi.nlm.nih.gov/pubmed/32647359;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347050;https://doi.org/10.1038/s41366-020-0635-2;https://www.nature.com/articles/s41366-020-0635-2","10.1038/s41366-020-0635-2","32647359","","","PMC7347050","… 8. COVID racial data tracker 2020. COVID Tracking Project. 2020. https://covidtracking.com/ race. 9. Masri L. COVID-19 takes unequal toll on immigrants in Nordic region. Reuters. 2020. World News. 10. Tricco AC, Lillie E, Soobiah C, Perrier L, Straus SE …","","","","Harvard Medical School, Boston, MA, USA. ConscienHealth, Pittsburgh, PA, USA. Harvard Medical School, Boston, MA, USA. fstanford@mgh.harvard.edu. Division of Endocrinology-Neuroendocrine, Department of Medicine, Massachusetts General Hospital, MGH Weight Center, Boston, MA, USA. fstanford@mgh.harvard.edu. Department of Pediatrics, Division of Endocrinology, Nutrition Obesity Research Center at Harvard (NORCH), Boston, MA, USA. fstanford@mgh.harvard.edu.","en","Research Article","","","","","","",""
"Journal Article","Blumenthal D,Fowler EJ,Abrams M,Collins SR","","Covid-19 — Implications for the Health Care System","N. Engl. J. Med.","The New England journal of medicine","2020","383","15","1483-1488","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Massachusetts Medical Society","","","","","2020-10-08","","","0028-4793","","https://doi.org/10.1056/NEJMsb2021088;http://dx.doi.org/10.1056/NEJMsb2021088;https://www.nejm.org/doi/full/10.1056/NEJMsb2021088","10.1056/NEJMsb2021088","","","","","Covid-19 — Implications for the Health Care System The Covid-19 pandemic has exposed and exacerbated weaknesses in the US health care system. Many patients are losing their health insurance when ...","","","","","","","","","","","","",""
"Journal Article","Townsend MJ,Kyle TK,Stanford FC","","commentary: COVID-19 and Obesity: Exploring Biologic Vulnerabilities, Structural Disparities, and Weight Stigma","Metabolism","Metabolism: clinical and experimental","2020","110","","154316","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","metabolismjournal.com","","","","","2020-09","","","0026-0495","1532-8600","http://dx.doi.org/10.1016/j.metabol.2020.154316;https://www.ncbi.nlm.nih.gov/pubmed/32673650;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358173;https://linkinghub.elsevier.com/retrieve/pii/S0026-0495(20)30180-3;https://www.metabolismjournal.com/article/S0026-0495(20)30180-3/abstract;https://www.metabolismjournal.com/article/S0026-0495(20)30180-3/fulltext","10.1016/j.metabol.2020.154316","32673650","","","PMC7358173","Skip to Main Content …","Bias; COVID-19; Disparities; Obesity; Weight stigma","","","Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA. ConscienHealth, 2270 Country Club Dr, Pittsburgh, PA 15241, USA. Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Massachusetts General Hospital, MGH Weight Center, 50 Staniford St, Suite 430, Boston, MA 02114, USA; Massachusetts General Hospital, Department of Medicine-Division of Endocrinology-Neuroendocrine, 55 Fruit St, Boston, MA 02114, USA; Nutrition Obesity Research Center at Harvard (NORCH), 55 Fruit St, Boston, MA 02114, USA. Electronic address: fstanford@mgh.harvard.edu.","en","Research Article","","","","","","",""
"Journal Article","Krieger N","","ENOUGH: COVID-19, Structural Racism, Police Brutality, Plutocracy, Climate Change—and Time for Health Justice, Democratic Governance, and an Equitable, Sustainable Future","Am. J. Public Health","American journal of public health","2020","110","11","1620-1623","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","American Public Health Association","","","","","2020-11-01","","","0090-0036","","https://doi.org/10.2105/AJPH.2020.305886;http://dx.doi.org/10.2105/AJPH.2020.305886;https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2020.305886;https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.2020.305886","10.2105/AJPH.2020.305886","","","","","… Available at: https://tinyurl.com/y7qzot3l. Accessed June 21, 2020. Google Scholar. 19. The COVID Tracking Project, at The Atlantic. The COVID Racial Data Tracker . Available at: https://covidtracking.com/race. Accessed July 11, 2020. Google Scholar. 20 …","","","","","","","","","","","","",""
"Preprint Manuscript","Hu W,Hu A","","Preliminary Analysis of Racial Disparities in Georgia (US) COVID-19 Deaths","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-07-07","2020-11-18","","","","https://papers.ssrn.com/abstract=3649557;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3649557;http://dx.doi.org/10.2139/ssrn.3649557;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3649557","10.2139/ssrn.3649557","","","","","The 2019 novel coronavirus disease (COVID-19) has brought to the forefront racial disparities in health outcomes across the US, but there is limited formal analysis into factors associated with these disparities. In-depth examination of COVID-19 disparities has been challenging due to inconsistent case definition, isolation procedures, and incomplete racial and medical information. As of June 2020, over 14,000 (25%) confirmed COVID-19 cases in Georgia did not have racial information. However, nearly all COVID-19 deaths had racial and ethnic information for analysis. Using county-level information from the Georgia Department of Public Health and the national County Health Rankings & Roadmaps, we found that Black Americans represented 31.5% of all Georgia residents but 46% of COVID-19 deaths. In the metropolitan Atlanta area, this over-representation was most pronounced in Fulton County which houses the City of Atlanta. The opposite pattern – worse disparity in counties surrounding the central city-bearing county – was instead observed in Albany, Columbus, and Macon, with no significant disparity difference in counties surrounding Savannah. Principal component analysis of health-related outcomes and social determinants of health from these 46 counties identified 17 themes, with greater racial disparities in COVID-19 deaths associated with worse air pollution, more rural communities, and paradoxically greater adherence to guidelines for screening mammography. We conclude that factors associated with the virus responsible for COVID-19 and healthcare disproportionately impact Black Americans.","race, air pollution, rural, SARS-CoV-2, Black Americans","","","","","","","","","","","","Available at SSRN 3649557"
"Journal Article","Elson E","","Lessons to learn from Covid-19: How our students can reflect on the pandemic's exposure of America's racial inequities in order to envision a better future","Journal of Curriculum and Pedagogy","Journal of Curriculum and Pedagogy","2020","17","2","228-231","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Routledge","","","","","2020-05-03","","","1550-5170","","https://doi.org/10.1080/15505170.2020.1797954;http://dx.doi.org/10.1080/15505170.2020.1797954;https://www.tandfonline.com/doi/abs/10.1080/15505170.2020.1797954?casa_token=On5DDwYtgisAAAAA:9Tg-x5NgAegm3GOdvUqpQJt-QlTL_LQZYkhRSoAwuVn89AN9yw0reDHjVBz6146H634kO-bn8347;https://www.tandfonline.com/doi/pdf/10.1080/15505170.2020.1797954?casa_token=5_17n35nZDYAAAAA:-1XNh3ulpoeYQO-deRvbP2KirybdvgQwb7Hih4BdzYx849I4YajTrdK_Xame2RWHrVIaFliUj0DK","10.1080/15505170.2020.1797954","","","","","… They are expected. According to the COVID Racial Data Tracker , which traces the outcomes of COVID-19 according to race, Black people, Native people, Asian Americans, and Latinx were disproportionately dying of, or infected by COVID-19 …","","","","","","","","","","","","",""
"Journal Article","Williams CM,Chaturvedi R,Gabriel RA","","Policy and law changes to address healthcare inequities for minority populations during COVID-19","J Allergy Infect Dis","","","1","3","49-52","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","probiologists.com","","","","","","","","","","https://probiologists.com/Uploads/Articles/11_637361767614448090.pdf","","","","","","… underlying medical conditions [10]. Based on the COVID Racial Data Tracker , which measures data from the District of Columbia and 41 states, the Latinx community has been disproportionately testing positive as well. In 30 states …","","","","","","","","","","","","",""
"Journal Article","Soon NA,Akee R,Kagawa M,Morey BN,Ong E,Ong P,Ponce N,Samoa R,Tanjasiri SP","","Counting Race and Ethnicity for Small Populations during the COVID-19 Pandemic","academia.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://www.academia.edu/download/64631213/Akee_etal_2020_AAPINexus_smallpopulationsdatadisag.pdf","","","","","","… pj7m-y5uh (May 12, 2020). COVID Tracking Project. 2020. “The COVID Racial Data Tracker .” https:// covidtracking.com/race (August 15, 2020). Empowering Pacific Island Communities. 2014. “Policy Platform Blueprint for Native …","","","","","","","","","","","","",""
"Commentary","Thomopoulos C,Michalopoulou H","","Renin--angiotensin system blockers and the risk of critical or fatal coronavirus disease 2019 in African Americans","J. Hypertens.","Journal of hypertension","2020","38","12","2384-2386","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","journals.lww.com","","","","","2020-12","","","0263-6352","1473-5598","http://dx.doi.org/10.1097/HJH.0000000000002636;https://www.ncbi.nlm.nih.gov/pubmed/33149061;https://doi.org/10.1097/HJH.0000000000002636;https://journals.lww.com/jhypertension/Fulltext/2020/12000/Renin__angiotensin_system_blockers_and_the_risk_of.9.aspx","10.1097/HJH.0000000000002636","33149061","","","","You may be trying to access this site from a secured browser on the server. Please enable scripts and reload this page. Log in Your account has been temporarily locked Your account has been temporarily locked due to incorrect …","","","","Department of Cardiology, Helena Venizelou General Hospital. Department of Cardiology, Metaxa Cancer Hospital, Piraeus, Greece.","en","Commentary","","","","","","",""
"Journal Article","Link DG","","A New Normal","J. Nurse Pract.","The journal for nurse practitioners: JNP","2020","16","8","639-640","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","npjournal.org","","","","","2020-09","","","1555-4155","","http://dx.doi.org/10.1016/j.nurpra.2020.06.007;https://www.ncbi.nlm.nih.gov/pubmed/32837400;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320865;https://linkinghub.elsevier.com/retrieve/pii/S1555-4155(20)30338-X;https://www.npjournal.org/article/S1555-4155(20)30338-X/abstract;https://www.npjournal.org/article/S1555-4155(20)30338-X/fulltext","10.1016/j.nurpra.2020.06.007","32837400","","","PMC7320865","… low income. The fact that black Americans have died from COVID-19 at a rate of 2 times their share of the population is well documented. 1 The COVID Tracking Project The COVID Racial Data Tracker . https://covidtracking.com …","","","","","en","Research Article","","","","","","",""
"Journal Article","Guijarro C,Perez-Fernandez E,Gonzalez-Pineiro B,Melendez V,Goyanes MJ,Renilla ME,Casas ML,Sastre I,Velasco M,Investigators AC,Others","","Differential risk for COVID-19 in the first wave of the disease among migrants from several areas of the world living in Spain","medRxiv","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.25.20112185v4.full-text","","","","","","medRxiv - The Preprint Server for Health Sciences.","","","","","","","","","","","","",""
"Journal Article","Galea S,Keyes K","","Understanding the COVID-19 Pandemic Through the Lens of Population Health Science","Am. J. Epidemiol.","American journal of epidemiology","2020","189","11","1232-1237","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","academic.oup.com","","","","","2020-11-02","","","0002-9262","1476-6256","http://dx.doi.org/10.1093/aje/kwaa142;https://www.ncbi.nlm.nih.gov/pubmed/32666083;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454266;https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/kwaa142;https://academic.oup.com/aje/article-abstract/189/11/1232/5871493;https://academic.oup.com/aje/article-pdf/189/11/1232/34045692/kwaa142.pdf?casa_token=Xl6TJrnLgbMAAAAA:7y97uqWRoc_3QfxQhFTm7Mn3T62j_7RiHETbsAsIHWIJ0ybUNxcc1YxQ7Qj06nikNPmGrHP6RDcC","10.1093/aje/kwaa142","32666083","","","PMC7454266","In a few devastating short months in 2020, the coronavirus disease 2019 (COVID-19) pandemic changed global mobility and interaction in ways that were unimaginable to much of the world's population as recently as in 2019. More than 10 million people have, at this writing, been infected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) globally, and more than 850,000 have died of COVID-19. As our science progresses, it is becoming possible to apply the principles of population health science to help us better understand the pandemic. What does a formal approach to population health science teach us about COVID-19? Building on our previously published work about the foundations of population health, we offer a few observations-a first draft of population health science thinking-as it intersects with the COVID-19 pandemic. Of note, our collective understanding of the pathology and causes of COVID-19 are rapidly changing by the day, and thus we fully expect that this work will evolve and improve as science progresses.","epidemiology; pandemic; population health; theory","","","","en","Research Article","","","","","","",""
"Journal Article","Studt T","","Impacts of the COVID-19 Pandemic on Black Americans with Diabetes","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","digitalcommons.ursinus.edu","","","","","2020","2020-11-18","","","","https://digitalcommons.ursinus.edu/biology_sum/79/;https://digitalcommons.ursinus.edu/cgi/viewcontent.cgi?article=1144&context=biology_sum","","","","","","The COVID-19 pandemic has impacted all Americans in varying ways, but it has hit the American Black population particularly hard. It also impacts immunocompromised individuals, especially those with conditions that involve damage to or hindering of the cardiovascular or respiratory systems. I found myself asking several questions – how much did this affect individuals who suffer from such conditions, especially those who are Black Americans; how much does systemic racism play in the current pandemic; what could be done about all this; and so on. Since I myself have lived with type I diabetes for almost two decades at the time of writing, I am intimately familiar with the lifestyle one must undertake as a type I diabetic, the long-term impacts of the disease, and how that is perceived by myself and others. This research attempts to answer these questions as well as to determine what justice can be enacted and in what ways. The death and infection data relating to the COVID-19 pandemic comes from the COVID-19 Racial Data Tracker. My research examines other data, including recent papers on the COVID-19 pandemic, and papers over the last decades about how socioeconomic status correlates to health status. As to what can be done, I recommend that we turn food deserts into food oases as a population-wide intervention that would improve the average health of communities trapped in food deserts.","","","","","","","Biology Summer Fellows","","","","","",""
"Preprint Manuscript","Brodeur A,Gray DM,Islam A,Bhuiyan S","","A Literature Review of the Economics of Covid-19","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-06-29","2020-11-18","","","","https://papers.ssrn.com/abstract=3636640;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3636640;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3636640;https://www.econstor.eu/bitstream/10419/222316/1/GLO-DP-0601.pdf","","","","","","The goal of this piece is to survey the emerging and rapidly growing literature on the economic consequences of COVID-19 and government response, and to synthetize the insights emerging from a very large number of studies. This survey (i) provides an overview of the data sets used to measure social distancing and COVID-19 cases and deaths; (ii) reviews the literature on the determinants of compliance and effectiveness of social distancing; (iii) summarizes the literature on the socio-economic consequences of COVID-19 and government interventions, focusing on labor, health, gender, discrimination and environmental aspects; and (iv) discusses policy proposals.","COVID-19, coronavirus, employment, lockdowns","","","","","","","","","","","",""
"Journal Article","Goldberg DS","","Structural Stigma, Legal Epidemiology, and COVID-19: The Ethical Imperative to Act Upstream","kiej.georgetown.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/structural-stigma-covid-19-special-issue/","","","","","","… The statistics are horrifying (Table 1). Table 1 (data sourced from The COVID Racial Data Tracker , https://covidtracking.com/race). State, Black or African- American alone, % of Population, % of Positive Cases, % of Deaths. Michigan, 14.10, 39.02, 42.94. Louisiana, 32.70, –, 56.73 …","","","","","","","","","","","","",""
"Journal Article","Pilkington B,Campoverde A","","The Bioethics of Translation: Latinos and the Healthcare Challenges of COVID-19","American Catholic Studies","American Catholic Studies","2020","131","3","11-17","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","American Catholic Historical Society","","","","","2020","2020-11-18","","2161-8534","","https://muse.jhu.edu/article/766298/summary?casa_token=-wZ7n468D6kAAAAA:bhtlpGHynkg9OQCz05Qi5zyKvxU2w8x2CPM4nWv9nVNTX3nhaXVWw3rWI4DyJMCV0_elrBl42g;http://dx.doi.org/10.1353/acs.2020.0041;https://muse.jhu.edu/article/766298/pdf?casa_token=ui_nC_rxEw0AAAAA:m60dULO6ooYV6vVllJtKyZJu-jEim0a-85EsDQ7CCWdEcFRCYxkPXljhpsojyewAyd8w66Z4HA","10.1353/acs.2020.0041","","","","","… more-vulnerable. 20. Daily COVID-19 ethnic data compiled by The COVID Racial Data Tracker (https://covidtracking.com/race) and the US Census Bureau. Race categories may overlap with Hispanic/Latino ethnicity. Rates …","","","","","","","","","","","","",""
"Preprint Manuscript","Austin CC,Widyastuti A,El Jundi N,Nagrani R","","COVID-19 Surveillance Data and Models: Review and Analysis, Part 1","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","Preprint","","","","","","","","2020-09-18","2020-11-18","","","","https://papers.ssrn.com/abstract=3695335;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3695335;http://dx.doi.org/10.2139/ssrn.3695335;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3695335","10.2139/ssrn.3695335","","","","","BACKGROUND. Reliable COVID-19 data are critical for understanding the disease and spread of the pandemic, for decision-making, for developing and implementing public health measures, and for tracking the effectiveness of interventions. Currently, however, there is a confusing plethora of publicly available COVID-19 surveillance data resources. Relevant websites are frequently poorly designed making it extraordinarily time-consuming and frustrating to find and extract the relevant information. METHODS. A systemic search of government, official agency, and non-government sources of COVID-19 surveillance and related data, computer code, and forecasting models was conducted.RESULTS. A comprehensive compendium was built of COVID-19 surveillance data and models having worldwide national coverage, and some sources of particular interest having sub-national coverage. Hyperlinks are provided to download data or computer code from each of the resources. For each resource, a concise description of the data and metadata, including identification of the data sources used to compile the data is provided. The compendium is provided in the supplementary material, organized in nine tables: (1) COVID-19 surveillance datasets and sources; (2) Databases or catalogues of COVID-19 surveillance data; (3) Resources that provide a corpus of COVID-19 related text; (4) Resources that track COVID-19 government responses; (5) R code potentially useful for analysis of COVID-19 data; (6) COVID-19 related data analysis platforms; (7) COVID-19 models; (8) Useful visualizations of COVID-19 data that go beyond the usual ‘dashboards’; and, (9) Commercial sites that showcase their product with a COVID-19 use case. Selected examples of data resources and models are provided in two additional tables in the body of the text. CONCLUSION. There is no single source of truth for COVID-19 surveillance data. Government and non-government data were found to be fragmented and difficult to find and use. There is a need to implement the principles of Open Science and FAIRER (Findable, Accessible, Interoperable, Reusable, Ethical, and Reproducible) data. There is an urgent need to develop a common standard for reporting communicable disease surveillance data without which Open Science and FAIRER data will be difficult to achieve.","COVID-19, data, surveillance, model, sources, computer code, R, FAIRER data","","","","","","","","","","","",""
"Journal Article","Dominguez-Villegas R,Tomaskovic-Devey D","","Supplement A, October 2020","J. Food Prot.","Journal of food protection","2020","83","sp1","1-290","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","latino.ucla.edu","","","","","2020-10-01","","","0362-028X","1944-9097","http://dx.doi.org/10.4315/0362-028X-83.sp1.1;https://www.ncbi.nlm.nih.gov/pubmed/33196812;https://meridian.allenpress.com/jfp/article-lookup/doi/10.4315/0362-028X-83.sp1.1;https://latino.ucla.edu/wp-content/uploads/2020/10/LPPI-Latino-Wage-Gap-Report.pdf","10.4315/0362-028X-83.sp1.1","33196812","","","","… Source: Kaiser Family Foundation analysis of The COVID Tracking Project, COVID Racial Data Tracker … COVID-19 cases come from the Kaiser Family Foundation analysis of The COVID Tracking Project, COVID Racial Data Tracker …","","","","","en","Research Article","","","","","","",""
"Journal Article","Greene S,Turner MA,Rush C","","Creating Places of Opportunity for All","Washington, DC: Urban Institute","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","urban.org","","","","","2020","","","","","https://www.urban.org/sites/default/files/publication/102821/creating-places-of-opportunity-for-all_3.pdf","","","","","","… 10 “The COVID Racial Data Tracker ,” The Atlantic, https://covidtracking.com/race; Shena Ashley, Alena Stern, Steven Brown, Ajjit Narayanan, Tomas Monarrez, and Margery Austin Turner, “Tracking COVID-19's Effects by Race and Ethnicity,” Urban Institute, July 30, 2020, https …","","","","","","","","","","","","",""
"Miscellaneous","Covid OD","","Guidelines","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Melbourne:: Australian and New Zealand Intensive Care Society (ANZICS)","","","","","2020","","","","","https://oregonearlylearning.com/wp-content/uploads/2020/08/Health-and-Safety-Guidelines_August-14-2020_English_Web-2.pdf","","","","","","Purpose The purpose of Health and Safety Guidelines for Child Care and Early Education Operating During COVID-19 (replacing Safety Procedures and Guidance for Child Care Facilities and Other Early Learning Programs Operating During COVID-19) is to address …","","","","","","","","","","","","",""
"Journal Article","Batko S,Gerken M,Williams A,Greene S","","Testing the Emergency Rental Assistance Priority Index","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","urban.org","","","","","2020","","","","","https://www.urban.org/sites/default/files/publication/102968/testing-the-emergency-rental-assistance-priority-index.pdf","","","","","","… in their homes. Notes 1 COVID Tracking Project and Boston University Center for Antiracist Research, “The COVID Racial Data Tracker ,” Atlantic, accessed July 24, 2020, https://covidtracking.com/race. 2 Steven Brown, “The …","","","","","","","","","","","","",""
"Review","Ferriss JS,Rose S,Rungruang B,Urban R,Spencer R,Uppal S,Sinno AK,Duska L,Walsh C","","Society of Gynecologic Oncology recommendations for fellowship education during the COVID-19 pandemic and beyond: Innovating programs to optimize trainee success","Gynecol. Oncol.","Gynecologic oncology","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Elsevier","","","","","2020-10-17","","","0090-8258","1095-6859","http://dx.doi.org/10.1016/j.ygyno.2020.10.009;https://www.ncbi.nlm.nih.gov/pubmed/33077260;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568037;https://linkinghub.elsevier.com/retrieve/pii/S0090-8258(20)34017-8;http://www.sciencedirect.com/science/article/pii/S0090825820340178;https://www.sciencedirect.com/science/article/pii/S0090825820340178?casa_token=WSB-GB0ecOMAAAAA:xbI9oiXoyxykvkddblVAorMnBnN_ZO3RmzT_V8p5J4fXR3i4gd9aaAttIRgnjr-orajQfPqQGw","10.1016/j.ygyno.2020.10.009","33077260","","","PMC7568037","In approximately ten months' time, the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected over 34 million people and caused over one million deaths worldwide. The impact of this virus on our health, relationships, and careers is difficult to overstate. As the economic realities for academic medical centers come into focus, we must recommit to our core missions of patient care, education, and research. Fellowship education programs in gynecologic oncology have quickly adapted to the \"new normal\" of social distancing using video conferencing platforms to continue clinical and didactic teaching. United in a time of crisis, we have embraced systemic change by developing and delivering collaborative educational content, overcoming the limitations imposed by institutional silos. Additional innovations are needed in order to overcome the losses in program surgical volume and research opportunities. With the end of the viral pandemic nowhere in sight, program directors can rethink how education is best delivered and potentially overhaul aspects of fellowship curriculum and content. Similarly, restrictions on travel and the need for social distancing has transformed the 2020 fellowship interview season from an in-person to a virtual experience. During this time of unprecedented and rapid change, program directors should be particularly mindful of the needs and health of their trainees and consider tailoring their educational experiences accordingly.","","","","Kelly Gynecologic Oncology Service, Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: Jferris3@jhmi.edu. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Wisconsin, Madison, WI, USA. Medical College of Georgia at Augusta University, Augusta, GA, USA. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Washington School of Medicine, Seattle, WA, USA. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Miami, Miami, FL, USA. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Virginia, Charlottesville, VA, USA. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars Sinai Medical Center, Los Angeles, CA, USA.","en","Review","","","","","","",""
"Journal Article","Schwartz JA","","Our Defenses","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","policyintegrity.org","","","","","2020","","","","","https://policyintegrity.org/files/publications/Weakening_Our_Defenses_Covid_Deregulation_Report.pdf","","","","","","Page 1. NEW YORK UNIVERSITY SCHOOL OF LAW Weakening Our Defenses July 2020 Jason A. Schwartz How the Trump Administration's Deregulatory Push Has Exacerbated the Covid-19 Pandemic Page 2. Copyright © 2020 by the Institute for Policy Integrity …","","","","","","","","","","","","",""
"Journal Article","Pascoe J,Stripling M","","Tag","kiej.georgetown.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/tag/special-issue/","","","","","","Skip to content …","","","","","","","","","","","","",""
"Journal Article","Haley JM,Kenney GM,Pan CW,Wang R,Lynch V,Buettgens M","","Progress in children’s coverage continued to stall out in 2018","Washington (DC): Urban Institute. Forthcoming","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","urban.org","","","","","2020","","","","","https://www.urban.org/sites/default/files/publication/102983/progress-in-childrens-coverage-continued-stalling-out-in-2018.pdf","","","","","","Page 1. Jennifer M. Haley, Genevieve M. Kenney, Clare Wang Pan, Robin Wang, Victoria Lynch, and Matthew Buettgens October 2020 Decades of federal and state efforts to increase children's enrollment in Medicaid and the …","","","","","","","","","","","","",""
"Preprint Manuscript","Benfer EA,Greene SJ,Hagan M","","Approaches to Eviction Prevention","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-07-28","2020-11-18","","","","https://papers.ssrn.com/abstract=3662736;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3662736;http://dx.doi.org/10.2139/ssrn.3662736;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3662736","10.2139/ssrn.3662736","","","","","This Article provides a general overview of eviction prevention approaches and strategies that are currently being employed, or could be adapted, to prevent eviction and homelessness during the COVID-19 pandemic. This document provides an overview of strategies that could prevent or mitigate eviction for nonpayment of rent, including 1) eviction and foreclosure moratoria, 2) housing stabilization, 3) landlord relief programs, 4) equitable approaches to the eviction process, and 5) post-eviction mitigation measures. Many of these policies and interventions predate the COVID-19 pandemic, and were employed during the Great Recession of 2008, and could be adapted to the pandemic environment.","COVID-19, Pandemic, Housing, Eviction, Health, Foreclosure, Recession","","","","","","","","","","","","Available at SSRN 3662736"
"Journal Article","Colored AO","","UNITED STATES DISTRICT COURT FOR THE DISTRICT OF COLUMBIA","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","naacpldf.org","","","","","2020","","","","","https://www.naacpldf.org/wp-content/uploads/8-1-Memo-in-Support.pdf","","","","","","… 14, 2020), https://www.cdc.gov/mmwr/volumes/69/wr/mm6932e3.htm .....7 The COVID Tracking Project, The COVID Racial Data Tracker , https://covidtracking.com/race. ....7 …","","","","","","","","","","","","",""
"Journal Article","Calzadilla PV","","La pandemia de COVID-19 y la crisis climáticas: dos emergencias convergentes","Revista Catalana de Dret Ambiental","Revista Catalana de Dret Ambiental","2020","11","1","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","revistes.urv.cat","","","","","2020-06-30","2020-11-18","","2014-038X","2014-038X","https://revistes.urv.cat/index.php/rcda/article/view/2807;http://dx.doi.org/10.17345/rcda2807;https://revistes.urv.cat/index.php/rcda/article/download/2807/2871","10.17345/rcda2807","","","","","La aparición y rápida expansión del coronavirus COVID-19 a finales de 2019 y principios de 2020 ha acaparado toda la atención mundial y no es para menos. El mundo entero está poniendo todos sus esfuerzos para detener la expansión de la epidemia, convertida en pandemia, así como para enfrentar sus dramáticas consecuencias. En medio de esta crisis sanitaria que detuvo la economía y la sociedad, el abordaje de la todavía más crítica y profunda crisis climática parece haber quedado también en pausa. Pero la actual emergencia sanitaria de COVID-19 y las respuestas que se adopten para enfrentar sus consecuencias no pueden desvincularse de la emergencia climática, ni pueden ser una excusa para retardar la urgente y necesaria acción climática. Por el contrario, los escenarios pospandemia deben convertirse en una oportunidad para reforzar la acción climática internacional como una c onditio sine qua non no solo para reducir el riesgo de futuras pandemias, sino para evitar un calentamiento global que resulte catastrófico para las personas y el planeta.","COVID-19;Pandemia;Crisis Climática;Acción Climática;Acuerdo de París; COVID-19; Climate Crisis; Climate Action; Paris Agrement","","","","es","","","","","","","",""
"Journal Article","Pascoe J,Stripling M","","Category: Uncategorized","kiej.georgetown.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/category/uncategorized/","","","","","","Skip to content …","","","","","","","","","","","","",""
"Journal Article","Baldwin AN,Brantuo NA,Pichardo JP","","Black Feminisms and Pedagogical Space-Making: Public Knowledge and Praxis in the Contemporary Moment","Handbook of Social Justice","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Springer","","","","","2020","","","","","https://link.springer.com/content/pdf/10.1007/978-3-030-29553-0_111-1.pdf;https://www.academia.edu/download/64769352/Baldwin_Brantuo_and_Pichardo_Black_Feminisms_and_Pedagogical_Space.pdf","","","","","","Page 1. Black Feminisms and Pedagogical Space- Making Public Knowledge and Praxis in the Contemporary Moment Andrea N. Baldwin, Nana Afua Brantuo, and Jazmin P. Pichardo Contents Introduction …","","Requested 11/18","","","","","","","","","","",""
"Journal Article","Zhang L,Mcleod ST,Vargas R,Liu X,Young DK,Dobbs TE","","Subgroup comparison of COVID-19 case and mortality with associated factors in Mississippi: findings from analysis of the first four months of public data","J. Biomed. Res.","Journal of biomedical research","2020","","","1-12","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","jbr-pub.org.cn","","","","","2020-09-18","","","1674-8301","","http://dx.doi.org/10.7555/JBR.34.20200135;https://www.ncbi.nlm.nih.gov/pubmed/33100275;https://doi.org/10.7555/JBR.34.20200135;http://jbr-pub.org.cn/article/doi/10.7555/JBR.34.20200135","10.7555/JBR.34.20200135","33100275","","","","We compared subgroup differences in COVID-19 case and mortality and investigated factors associated with case and mortality rate (MR) measured at the county level in Mississippi. Findings were based on data published by the Mississippi State Department of Health between March 11 and July 16, 2020. The COVID-19 case rate and case fatality rate (CFR) differed by gender and race, while MR only differed by race. Residents aged 80 years or older and those who live in a non-metro area had a higher case rate, CFR, and MR. After controlling for selected factors, researchers found that the percent of residents who are obese, low income, or with certain chronic conditions were associated with the county COVID-19 case rate, CFR, and/or MR, though some were negatively related. The findings may help the state to identify counties with higher COVID-19 case rate, CFR, and MR based on county demographics and the degree of its chronic conditions.","COVID-19; case fatality rate; case rate; mortality rate","","","Office of Health Data and Research, Mississippi State Department of Health, Jackson, MS 39215, USA. School of Social Work, College of Education and Human Sciences, The University of Southern Mississippi, Hattiesburg, MS 39406, USA. Office of Health Data, Operations, and Research, Mississippi State Department of Health, Jackson, MS 39215, USA. Office of State Health Officer, Mississippi State Department of Health, Jackson, MS 39215, USA.","en","Research Article","","","","","","",""
"Ph.D. Thesis","Woyczynski L","","Quantifying the Distribution of Racial Inequalities in COVID-19 Mortality in the United States","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","search.proquest.com","","","","","2020","","","","","http://search.proquest.com/openview/2ec4f70dfa0cfce96abc7a3752b84efd/1?pq-origsite=gscholar&cbl=18750&diss=y&casa_token=uLSJV6wSJVQAAAAA:_QUFZWCANDxsdDxbSRUqQ5TnWUS3ddxFbigmR1tv-CaNapwJY0LImaDMSNG6b2kvMOscqoHf0w","","","","","","… The Atlantic, in collaboration with Boston University Center for Antiracist Research, published the COVID Racial Data Tracker on April 15th, spurred by a series of essays written by Ibram X. Kendi on the urgent need for collection and reporting of demographic data to fully …","","","","","","Ph.D. Thesis","","","","","","University of Washington",""
"Preprint Manuscript","Ristovska L","","Racial Disparities in COVID-19 Cases and Deaths: Theories and Evidence","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-08-05","2020-11-18","","","","https://papers.ssrn.com/abstract=3668051;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3668051;http://dx.doi.org/10.2139/ssrn.3668051;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3668051","10.2139/ssrn.3668051","","","","","Non-white individuals are 2.3 times more likely to get infected with COVID-19 and 1.42 times more likely to die of COVID-19 than white individuals. Using data on COVID-19 cases by race for 974 counties and county-level demographic, socio-economic, and health characteristics, I find that counties with larger differences in average household size and per-capita income between white and non-white individuals are associated with larger racial disparities in COVID-19 case rates, particularly for Black and Hispanic individuals. The magnitudes of these relationships imply that equalizing household size and per-capita income between Black and white individuals would be associated with reductions in the racial disparity in COVID-19 case rates of 21.1% and 22.7% for Black, 37.2% and 35% for Hispanic, and 18.3% and 5.8% for American Indian/Alaska Native and Native Hawaiian/Pacific Islander individuals. Analysis of overall county-level and ZIP code-level cases and deaths further supports these findings and highlights the role of public transportation use in explaining racial disparities.","COVID-19, race","","","","","","","","","","","","Available at SSRN 3668051"
"Journal Article","Williams JC,Anderson N,Holloway T,Samford 3rd E,Eugene J,Isom J","","Reopening the United States: Black and Hispanic Workers Are Essential and Expendable Again","Am. J. Public Health","American journal of public health","2020","110","10","1506-1508","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","ajph.aphapublications.org","","","","","2020-10","","","0090-0036","1541-0048","http://dx.doi.org/10.2105/AJPH.2020.305879;https://www.ncbi.nlm.nih.gov/pubmed/32903081;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483098;https://www.ajph.org/doi/10.2105/AJPH.2020.305879?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2020.305879;https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.2020.305879","10.2105/AJPH.2020.305879","32903081","","","PMC7483098","… References. 1. COVID Tracking Project. The COVID racial data tracker . 2020. Available at: https://covidtracking.com/race. Accessed July 25, 2020. Google Scholar. 2. McKinsey & Company. COVID-19: investing in Black lives and neighborhoods. 2020 …","","","","J. Corey Williams is with the Division of Child and Adolescent Psychiatry, Georgetown University Medical Center, Washington, DC. Nientara Anderson and Terrell Holloway are with the Department of Psychiatry, Yale-New Haven Hospital, New Haven, CT. Ezelle Samford III is with the Program on Race, Science, and Society, Center for Africana Studies, University of Pennsylvania, Philadelphia. Jeffrey Eugene is with the Children's Hospital of Philadelphia, Craig-Dalsimer Division of Adolescent Medicine, Philadelphia, PA. Jessica Isom is with the Department of Psychiatry, Codman Square Health Center, Boston Medical Center, Boston, MA.","en","Research Article","","","","","","",""
"Journal Article","Chikere CA,Aranmolate R,Chikere OE","","The impact of COVID-19 in the state of Mississippi","Pearl River","","","596","","41","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","researchgate.net","","","","","","","","","","https://www.researchgate.net/profile/Chidinma_Chikere/publication/344476621_The_impact_of_COVID-19_in_the_state_of_Mississippi/links/5f7b1fe7458515b7cf67b080/The-impact-of-COVID-19-in-the-state-of-Mississippi.pdf","","","","","","… Similarly, data from COVID Racial Data Tracker revealed that death caused by COVID-19 is higher in black people (81 per 100,000) than in Hispanic or Latino (46 per 100,000), American Indian or Alaska Native (44 per 100,000), White people (32 per 100,000), and Native …","","","","","","","","","","","","",""
"Journal Article","Galik E,Stefanacci R","","Racial Disparities Exposed by COVID-19","Caring for the Ages","","2020","21","6","2","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Elsevier","","","","","2020","","","","","https://www.caringfortheages.com/article/S1526411420302912/abstract;https://www.caringfortheages.com/article/S1526-4114(20)30291-2/fulltext","","","","","","Caring for the Ages is the official newspaper of AMDA and provides long-term care professionals with timely and relevant news and commentary about clinical developments and about the impact of health care policy on long-term care medicine.","","","","","","","","","","","","",""
"Journal Article","Loker A","","COVID-19 and the US Lettuce Supply Chain: Implications for Farmworker Health and Safety and a Secure Supply","Journal of Agricultural & Food Industrial Organization","","2020","18","2","20200029","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","De Gruyter","Berlin, Boston","","","","2020","","","","","https://www.degruyter.com/view/journals/jafio/18/2/article-20200029.xml;http://dx.doi.org/10.1515/jafio-2020-0029;https://www.degruyter.com/view/journals/jafio/ahead-of-print/article-10.1515-jafio-2020-0029/article-10.1515-jafio-2020-0029.xml","10.1515/jafio-2020-0029","","","","","The COVID-19 pandemic has exposed multiple vulnerabilities in the U.S. lettuce value chain. Restaurants and other food service operations closed almost overnight, leaving farmers with millions of dollars of excess lettuce. Because of the rigid value chain, farmers were forced to decide whether to harvest their crops for donation, try to find new customers, or plow their crops under. Close working and living conditions increase farmworkers’ risk of contracting COVID-19. Though many operations have implemented safety measures to protect farmworkers from COVID-19 in the short-term, larger structural changes must be made to provide workers with fair wages, access to health insurance and paid time off, and affordable housing. This review outlines the current value chain of lettuce in the U.S. and the disruptions caused by COVID-19, analyzes the impacts on farmworker health and safety, and offers recommendations for a more resilient lettuce value chain.","","","","","","","","","","","","",""
"Journal Article","Craft JF,Travassos MA,Foppiano Palacios C,Openshaw JJ","","Inadequate Minority Representation within SARS-CoV-2 Vaccine Trials","Am. J. Trop. Med. Hyg.","The American journal of tropical medicine and hygiene","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","ASTMH","","","","","2020-11-11","","","0002-9637","1476-1645","http://dx.doi.org/10.4269/ajtmh.20-1294;https://www.ncbi.nlm.nih.gov/pubmed/33200726;http://www.ajtmh.org/content/journals/10.4269/ajtmh.20-1294?crawler=true&mimetype=application/pdf;https://www.ajtmh.org/content/journals/10.4269/ajtmh.20-1294;https://www.ajtmh.org/content/journals/10.4269/ajtmh.20-1294?crawler=true&mimeType=application%2Fpdf","10.4269/ajtmh.20-1294","33200726","","","","Minority communities have borne the brunt of COVID-19 disease in the United States. Nonwhites have contracted most of the SARS-CoV-2 infections; COVID-19 mortality rates for Black Americans are more than twice those for whites. Given this, studying the most effective ways to prevent and treat SARS-CoV-2 in these populations should be a research priority, particularly with respect to vaccine trials. Federal guidelines from the National Institutes of Health and Food and Drug Administration emphasize the need for inclusion of minority groups in these trials, but none of the publicly available SARS-CoV-2 vaccine trial protocols require representative sampling of minorities. This piece emphasizes the importance of adequate inclusion of minority communities in SARS-CoV-2 vaccine trials, and the implications of this inclusion for SARS-CoV-2 vaccine distribution.","","","","Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland. Department of Internal Medicine, Infectious Diseases Section, Yale School of Medicine, New Haven, Connecticut. Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, California.","en","Research Article","","","","","","",""
"Journal Article","Wieland ML,Doubeni CA,Sia IG","","Community Engagement With Vulnerable Populations","Mayo Clin. Proc.","Mayo Clinic proceedings. Mayo Clinic","2020","95","9S","S60-S62","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Elsevier","","","","","2020-09","","","0025-6196","1942-5546","http://dx.doi.org/10.1016/j.mayocp.2020.05.041;https://www.ncbi.nlm.nih.gov/pubmed/32807521;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306723;https://linkinghub.elsevier.com/retrieve/pii/S0025-6196(20)30644-3;https://www.sciencedirect.com/science/article/pii/S0025619620306443/pdf?md5=f2999a9c9d8de09c822a8f1cf6c0294c&pid=1-s2.0-S0025619620306443-main.pdf;https://www.mayoclinicproceedings.org/article/S0025-6196(20)30644-3/fulltext","10.1016/j.mayocp.2020.05.041","32807521","","","PMC7306723","… Acknowledgment: Editing, proofreading, and reference verification were provided by Scientific Publications, Mayo Clinic. References: 1. The COVID Tracking Project. The COVID racial data tracker . 2020; Available from: https://covidtracking.com/race. Accessed May 22, 2020 …","","","","Center for Health Equity and Community Engagement Research, Mayo Clinic, Rochester, MN; Division of Community Internal Medicine, Mayo Clinic, Rochester, MN. Electronic address: wieland.mark@mayo.edu. Center for Health Equity and Community Engagement Research, Mayo Clinic, Rochester, MN; Department of Family Medicine, Mayo Clinic, Rochester, MN. Center for Health Equity and Community Engagement Research, Mayo Clinic, Rochester, MN; Division of Infectious Diseases, Mayo Clinic, Rochester, MN.","en","Research Article","","","","","","",""
"Journal Article","Escobar G,Taheri S","","Incarceration Weakens a Community’s Immune System: Mass Incarceration and COVID-19 Cases in Milwaukee","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","measuresforjustice.org","","","","","2020","","","","","https://measuresforjustice.org/about/docs/Incarceration_Weakens_Community_Immune_System_Preliminary_Results.pdf","","","","","","Page 1. Incarceration Weakens a Community's Immune System: Mass Incarceration and COVID-19 Cases in Milwaukee Preliminary Results Gipsy Escobar, PhD Sema Taheri, MA June 2, 2020 This study was conducted with …","","","","","","","","","","","","",""
"Journal Article","Greenfield J,Sears M,Nagrani R,Mazzaferro G,Widyastuti A,Austin CC","","the RDA-COVID19-WG.(2020). Common Data Models and Full Spectrum Epidemiology: Epi-STACK architecture for COVID-19 epidemiology datasets","Data Sharing in Epidemiology","","","","","65","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","rd-alliance.org","","","","","","","","","","https://www.rd-alliance.org/system/files/RDA-COVID19-Epidemiology%20SUPPORTING%20OUTPUT%20%28v0.053%202020-06-28%29.pdf#page=66","","","","","","Page 66. 65 ANNEX 7–Common Data Models and Full Spectrum Epidemiology: Epi-STACK architecture for COVID-19 epidemiology datasets Jay Greenfield1, Meg Sears2, Rajini Nagrani3, Gary Mazzaferro4, Anna Widyastuti5 …","","","","","","","","","","","","",""
"Journal Article","Garzón-Galvis C,Richardson MJ,Solomon GM","","Tracking Environmental and Health Disparities to Strengthen Resilience Before the Next Crisis","Environ. Justice","Environmental justice ","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Mary Ann Liebert, Inc., publishers","","","","","2020-09-09","","","1939-4071","","https://doi.org/10.1089/env.2020.0025;http://dx.doi.org/10.1089/env.2020.0025;https://www.liebertpub.com/doi/abs/10.1089/env.2020.0025?casa_token=mzDdQUAEkasAAAAA:9Gtu8S-EA7jheaV01QKXkFTqS_RBDLWQTN_oMcmA3643PWrxtD8Bp8JCiKWNJlSUMlEActK38PiACQ;https://www.liebertpub.com/doi/pdfplus/10.1089/env.2020.0025?casa_token=OC-OH6uf9IoAAAAA:ZlAVUBAkLAc9yaPs32iT8qRDbsjYkwdPGWlNoo1oo3BSOQ12ddJtanuxh2Vuw7wW_s1xl0go5KnWkg","10.1089/env.2020.0025","","","","","The COVID-19 pandemic has underscored how underlying disparities in environmental and health conditions exacerbate vulnerability during public health emergencies in low-income and communities of color. Neglected epidemics?high rates of pollution, chronic disease, and racial and socioeconomic health disparities?have continued amid persistent systemic racism and declining investment in public health. Recognized too late due to shortcomings in public health data tracking, COVID-19 has surged through vulnerable communities. Improved public health tracking is critical for informing the country's recovery from COVID-19, and it can be leveraged to measure and reduce health disparities and strengthen community resilience to respond more effectively to the next public health crisis. We emphasize how public health tracking agencies can engage communities in data collection and reporting; we also discuss the complementary role that communities can take to mobilize data to change policies and institutions, strengthening resilience through increased information and capacity driven by community priorities. Success requires the continuous collection of timely data at a community scale, and public health agencies partnering with communities to use the information in decision making and evaluation to ensure progress over time. We highlight community-engaged data collection and reporting?community air monitoring in Imperial County, CA?as an example of working with communities to improve public health data collection and reporting, increase community dialogue and engagement in governmental decision making, and inform public health tracking to reduce health disparities and strengthen community resilience.","","","","","","","","","","","","",""
"Journal Article","Batko S,DuBois N,Narayanan A,MacDonald G,Williams A,Greene S,Cunningham M","","Where to Prioritize Emergency Rental Assistance to Keep Renters in Their Homes","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","urban.org","","","","","2020","","","","","https://www.urban.org/sites/default/files/2020/08/24/where_to_prioritize_emergency_rental_assistance_to_keep_renters_in_their_homes_technical_appendix.pdf","","","","","","… homelessness- this-year/. 4 COVID Tracking Project and Boston University Center for Antiracist Research, “The COVID Racial Data Tracker ,” Atlantic, accessed July 24, 2020, https://covidtracking.com/race. References Capps, Randy …","","","","","","","","","","","","",""
"Journal Article","Conley D,Burroughs B","","Pained publics","Communication and the Public","Communication and the Public","2020","5","1-2","3-6","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","SAGE Publications","","","","","2020-03-01","","","2057-0473","","https://doi.org/10.1177/2057047320951894;http://dx.doi.org/10.1177/2057047320951894;https://journals.sagepub.com/doi/abs/10.1177/2057047320951894?casa_token=LpbEJHbAptoAAAAA:jTIdxhI79ygC8UgK66ksvXKnUkA_BO6rCodT011oGah5GEnTWz2wR2K5n6iG0PUOdjHbOpMUiAC1;https://journals.sagepub.com/doi/full/10.1177/2057047320951894?casa_token=Z2Rzjz6zgZcAAAAA:Eb6Bm4zx762pFf-MOkZxVjY58Om5DWGXKlA_CfiNGEj3U8uXMuARmPGJ_miqti3aK_i4woNmEMg3","10.1177/2057047320951894","","","","","In her contribution to the Quarterly Journal of Speech?s centennial issue, ?Pathologia,? Jenny Rice suggests, ?pathology does not only or always reveal something broken. Rather, the experience of pathology also reminds us that rhetoric?s sensorium is working?really working? (p. 35). Yes, and in a time of pandemic turbulence, we are reminded that the sensorium of civic life works in ways that shape, even threaten, our collective modes of engagement and relationality. Rice offers ?the wound? as a response to pathological publicness, noting, ?I propose that we begin to theorize the wound itself as the beginning of dialogue. Only the wound can stand as pathology?s counterpart? (p. 40). Wounds focalize and materialize the pathogenic, opening up possibilities for redress while also remediating their own contaminants. Accordingly, our special issue aims to grapple with the ways contemporary publicness affects, and is affected by, civic wounds: how they are discursively produced, and productively discursive. What emergent forms of expression or composition do wounds make possible or foreclose? And, how might critical communication scholarship ad/dress the pathogenic constitution of civic wounds? Each of the essays in ?Ad/Dressing Civic Wounds? thus situates particular ways in which wounds are ?really working? to produce the conditions that open or foreclose possibilities in the never-finished work of finding shared grounds of togetherness we might call civic life.","","","","","","","","","","","","",""
"Journal Article","Wu KH,Hornsby WE,Klunder B,Krause A,Driscoll A,Kulka J,Bickett-Hickok R,Fellows A,Graham S,Kaleba EO,Others","","Exposure and risk factors for COVID-19 and the impact of staying home on Michigan residents","medRxiv","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.25.20181800v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/31/2020.08.25.20181800.full.pdf","","","","","","Page 1. 1 TITLE PAGE Title: Exposure and risk factors for COVID-19 and the impact of staying home on Michigan residents AUTHORS: Kuan-Han H. Wu, MS 1 *, Whitney E. Hornsby, PhD 2 *, Bethany Klunder, BS 2 , Amelia Krause …","","","","","","","","","","","","",""
"Journal Article","Batko S,Gerken M,Williams A,Greene S","","Testing the Emergency Rental Assistance Priority Index","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","urban.org","","","","","2020","","","","","https://www.urban.org/sites/default/files/publication/102968/testing-the-emergency-rental-assistance-priority-index.pdf","","","","","","… in their homes. Notes 1 COVID Tracking Project and Boston University Center for Antiracist Research, “The COVID Racial Data Tracker ,” Atlantic, accessed July 24, 2020, https://covidtracking.com/race. 2 Steven Brown, “The …","","","","","","","","","","","","",""
"Report","Pathak PA,Schmidt H,Solomon A,Song E,Sönmez T,Utku Ünver M","","Do Black and Indigenous Communities Receive their Fair Share of Vaccines Under the 2018 CDC Guidelines?","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","working paper","National Bureau of Economic Research","w27817","nber.org","","","","","2020-09-14","2020-11-18","","","","https://www.nber.org/papers/w27817;http://dx.doi.org/10.3386/w27817;https://www.nber.org/system/files/working_papers/w27817/w27817.pdf","10.3386/w27817","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Preprint Manuscript","Savage A","","COVID-1619: A Brief History of Racism","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-08-10","2020-11-18","","","","https://papers.ssrn.com/abstract=3671093;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3671093;http://dx.doi.org/10.2139/ssrn.3671093;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3671093","10.2139/ssrn.3671093","","","","","Racism is the use of Black people to achieve the goals of white people without regard to the personhood, humanity, and agency of Blacks. This essay explores this definition of racism by tracing the influence of the twin institutions of law and religion in creating and maintaining the slave system in early colonial America. The essay then demonstrates the pernicious and persistent nature of racism by mapping this definition onto the current COVID-19 pandemic and its disproportionate impact on Black Americans.","race, racism, legal history, law and religion, COVID-19","","","","","","","","","","","","Available at SSRN 3671093"
"Journal Article","Pascoe J,Stripling M","","Tag: COVID-19","kiej.georgetown.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/tag/covid-19/","","","","","","Skip to content …","","","","","","","","","","","","",""
"Journal Article","Tracking Project C","","The COVID racial data tracker","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Journal Article","Krieger N","","ENOUGH: COVID-19, Structural Racism, Police Brutality, Plutocracy, Climate Change","portside.org","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://portside.org/node/23779/printable/print","","","","","","COVID-19 starkly reveals how structural injustice cuts short the lives of people subjected to systemic racism and economic deprivation. 2–4 It is not, however, the only crisis at hand.Since the May 25, 2020, murder of George Floyd, a 46-year-old African American man …","","","","","","","","","","","","",""
"Journal Article","Chen JT,Waterman PD,Krieger N,Krieger N","","COVID-19 and the unequal surge in mortality rates in Massachusetts, by","Population ","Population","2020","25014","B25014_013E","B25014_001E","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","cdn1.sph.harvard.edu","","","","","2020","","","0032-4663","","https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1266/2020/05/20_jtc_pdw_nk_COVID19_MA-excess-mortality_text_tables_figures_final_0509_with-cover-1.pdf","","","","","","… time of COVID-19. Interdisciplinary Association of Population Health Scientists, https://iaphs.org/racism-in-the-time-of-covid-19/ ; accessed May 9, 2020. 6. The COVID Racial Data Tracker . Available at: https://covidtracking.com/race ; accessed May 9, 2020 …","","","","","","","","","","","","",""
"Preprint Manuscript","Sandoval CJ,Cain PA,Diamond SF,Hammond A,Love JC,Smith S,Nabipour S","","Legal Education in the Era of COVID-19: Putting Health, Safety and Equity First","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-07-24","2020-11-18","","","","https://papers.ssrn.com/abstract=3660221;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3660221;http://dx.doi.org/10.2139/ssrn.3660221;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3660221","10.2139/ssrn.3660221","","","","","The COVID-19 pandemic has transformed the traditional academic model of gathering people into physical classes into a high-risk activity. Legal education is a Critical Infrastructure sector that supports democratic access to the legal system and trains students to become ethical members of the legal profession and society. Debates about whether legal education should be delivered in person, online, or through a hybrid model highlight the safety culture gap in American legal education. This Article proposes an ethical framework that values safety, recognizes the inherent worth and dignity of every human being, and centers diversity and inclusion as the foundation for effective educational dialogue, to recommend online legal education during the COVID-19 pandemic.This Article’s interdisciplinary team analyzes scientific studies on COVID-19 available to date, the virus’s mutation which promotes infection, and the limits of mitigation measures in indoor classrooms where people gather for more than an hour at a time to discuss educational material and develop legal skills. It examines the disproportionate effects of COVID-19 on African-Americans, Native Americans, Latinx Americans, older Americans, and those with certain underlying health conditions that would foreseeably lead members of those groups to participate in class online. Those participating in person in a hybrid educational model are likely to be younger and less diverse. The hybrid classroom model cleaves students and faculty by race, ethnicity, tribe, age, and health, undermining commitments to diversity and inclusion that support educational dialogue and first amendment values. In person classes may drive viral mutation and endanger health and safety as people under 45, the largest age cohort for American law students, lead the surge in COVID-19 infection. The Internet’s development creates the opportunity to deliver effective, synchronous, inclusive, ethical legal education. This Article concludes that legal education should be conducted online during the COVID-19 pandemic.","COVID-19, legal education, equity, health, safety, safety culture, ethics, diversity, diversity and inclusion, educational dialogue, first, amendment, online education, pandemic, pandemic response, educational models, science-based analysis, ethical decision-making, public health, education","","","","","","","","","","","","Safety and Equity"
"Journal Article","Robertson L,O’Toole J,Evans N","","COVID-19 Pandemic: A World in Turmoil","atrainceu.com","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://www.atrainceu.com/sites/default/files/COVID-19%20Update%20for%20Print%20and%20Go.pdf","","","","","","Page 1. COVID-19 Pandemic: A World in Turmoil Authors: Lauren Robertson, BA, MPT; JoAnn O'Toole, RN, BSN; Nancy Evans, BS Contact hours: 10 Price: $29 Source: National Institutes of Health Course Summary All of us …","","","","","","","","","","","","",""
"Preprint Manuscript","Hernandez Lopez E","","Trade War, PPE, and Race","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-07-10","2020-11-18","","","","https://papers.ssrn.com/abstract=3647947;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3647947;http://dx.doi.org/10.2139/ssrn.3647947;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3647947;https://www.academia.edu/download/64252903/Hernandez-8-23.pdf","10.2139/ssrn.3647947","","","","","Tariffs on Personal Protective Equipment (PPE), such as face masks and gloves, weaken the American response to COVID. The United States has exacerbated PPE shortages with Section 301 tariffs on these goods, part of a trade war with China. This has a disparate impact felt by minority communities because of a series of health inequity harms. COVID’s racial disparity appears in virus exposure, virus susceptibility, and COVID treatments. This essay makes legal, policy, and race-and-health arguments. Congress has delegated to the U.S. Trade Representative expansive authority to increase tariffs. This has made PPE supplies casualties of the trade war. In political terms, the Trump administration has prioritized increasing tariffs over public health readiness. Regarding race, PPE shortages exemplify the socio-economic effects of trade policies and add to COVID’s racial disparities.","personal protective equipment, trade, Section 301, trade war, tariffs, racial disparity, COVID, COVID-19, health inequity, coronavirus","","","","","","","","","","","","PPE, and Race (July 10, 2020)"
"Journal Article","Stokes AC,Lundberg DJ,Hempstead K,Elo IT,Preston SH","","Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","Preprint","","","ncbi.nlm.nih.gov","","","","","2020-09-02","","","","","http://dx.doi.org/10.1101/2020.08.31.20184036;https://www.ncbi.nlm.nih.gov/pubmed/32908999;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480051;https://doi.org/10.1101/2020.08.31.20184036;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480051.1/","10.1101/2020.08.31.20184036","32908999","","","PMC7480051","Covid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. In this study, we take advantage of spatial variation in Covid-19 mortality across US counties to construct an Ordinary Least Squares regression model estimating its relationship with all-cause mortality. We then examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by demographic, structural, and policy characteristics. We estimate that 20.4% [95% CI (13.7%, 27.2%)] of excess deaths between February 1 and August 26, 2020 were ascribed to causes of death other than Covid-19 itself. Excess deaths not assigned to Covid-19 were even higher than predicted by our model in counties with high income inequality, low median income, low homeownership, and high percentages of Black residents, showing a pattern related to socioeconomic disadvantage and structural racism. Our work suggests that inequities in excess deaths attributable to Covid-19 may be even greater than revealed by data reporting deaths assigned to Covid-19 alone.","","","","","en","Research Article","","","","","","",""
"Journal Article","Brief IT","","COVID-19 PREVALENCE AND MORTALITY RATES IN PRIMARY CARE HEALTH PROFESSIONAL SHORTAGE AREAS","impaqint.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://impaqint.com/sites/default/files/issue-briefs/COVID19-Prevalence-and-Mortality-Rates-Issue-Brief.pdf","","","","","","… county's total population, and multiplying by 100,000. We also obtained state-level data on the number of tests conducted as of August 27, 2020 from the COVID Tracking Project .20 The identification of pcHPSA counties was …","","","","","","","","","","","","",""
"Journal Article","Kochanczyk M,Lipniacki T","","Evaluation of national responses to COVID-19 pandemic based on Pareto optimality","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.27.20141747v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/12/01/2020.06.27.20141747.full.pdf","","","","","","Page 1. Pareto‐based evaluation of national responses to COVID‐19 pandemic shows that saving lives and protecting economy are non‐trade‐off objectives Marek Kochańczyk & Tomasz Lipniacki Department of Biosystems and Soft Matter …","","","","","","","","","","","","",""
"Journal Article","Wilson DJ","","Weather, social distancing, and the spread of COVID-19","","","2020","","","","COVID Tracking Project","","","","frbsf.org","","","","","2020","","","","","https://www.frbsf.org/economic-research/publications/working-papers/2020/23/;https://www.frbsf.org/economic-research/files/wp2020-23.pdf","","","","","","… the paper are nearly identical using the New York Times data. Data on daily testing, which are only available at the state level, come from The COVID Tracking Project and were downloaded from tracktherecovery.org. The time …","","","","","","","","","","","","",""
"Journal Article","Seda CH,Network MC","","Unique Vulnerabilities and Unprecedented Partnerships to Address Them","migrantclinician.org","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://www.migrantclinician.org/files/Streamline%20Fall%202020.pdf","","","","","","2 MCN Streamline be three times more likely to contract COVID-19 than non-agricultural workers. 4 While data specifically for agricultural workers aren't comprehensive, the majority of agricultural workers in the United States are Latinx; indeed, Latinx communities have had higher rates of COVID-19 infection. 5-8 With fewer resources and community connections, agricultural workers struggle to access health care and/or isolate if they do fall ill. Agricultural workers without authorization to live and work in the US are ineligible for federal wage relief …","","","","","","","","","","","","",""
"Journal Article","Gallarde-Kim S,Smith C,Roy S,Taylor PM,et al.","","Health Care Needs, Access to Care, and Experiences of Racism for Black Children and Youth with Special Health Care Needs and Their Families","ohsu.edu","","","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","","","","","","https://www.ohsu.edu/sites/default/files/2020-10/OCCYSHN%202020%20NA%20Ch.3.pdf","","","","","","Page 1. Oregon's Children with Special Health Care Needs Five Year Needs Assessment Findings – September 30, 2020 Oregon Center for Children and Youth with Special Health Needs www.occyshn.org occyshn@ohsu.edu …","","","","","","","","","","","","",""
"Journal Article","Petteway RJ","","LATENT//Missing: On Missing Values, Narrative Power, and Data Politics in Discourse of COVID-19","Health Educ. Behav.","Health education & behavior: the official publication of the Society for Public Health Education","2020","47","5","671-676","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","journals.sagepub.com","","","","","2020-10","","","1090-1981","1552-6127","http://dx.doi.org/10.1177/1090198120950194;https://www.ncbi.nlm.nih.gov/pubmed/32806932;https://journals.sagepub.com/doi/10.1177/1090198120950194?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://journals.sagepub.com/doi/abs/10.1177/1090198120950194?casa_token=naRX19H7rKEAAAAA:UTR-0RsfpE1gEDiRyfTTsq4kRTSh0o3dY-NMIG53u8J9HA3dkTB3XP327DP2gWsvZeQkSxXe5anR;https://journals.sagepub.com/doi/full/10.1177/1090198120950194?casa_token=B2Me_Rlz7xkAAAAA:1I_DiNprc4jbsiJizuOluMTpfG6AkkT7tPMLCEPugoWeONHNQ0gdGoD0Nhqg8Lbgl1-hi8qaGeZY","10.1177/1090198120950194","32806932","","","","April is National Minority Health Month in the United States. The first week of April is National Public Health Week. This year, both occasions passed as the COVID-19 pandemic unfolded and, in the process, rendered remarkably clear the magnitude of the United States' collective shortcomings in advancing population health equity-particularly as related to dominant narratives of health and data politics. Drawing from critical theory, I use essay to contextualize present COVID-19 discourse and poetry to situate this discourse within a broader historical arc of the United States' racist, classist, and homophobic proclivities in times of public health crises. I use the combination of essay/poem as creative praxis to analyze and reflect on our present moment in relation to public health pasts and to raise questions about public health research, education, and data futures-offering a critical commentary on the intersections of infectious diseases, structural inequality (e.g., racism), data politics, and public health violence.","COVID-19; data politics; narrative power; poetry; public health violence; structural racism","","","Portland State University, Portland, OR, USA.","en","Research Article","","","","","","",""
"Journal Article","Page KR,Flores-Miller A","","Lessons We’ve Learned—Covid-19 and the Undocumented Latinx Community","N. Engl. J. Med.","The New England journal of medicine","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","Mass Medical Soc","","","","","2020","","","0028-4793","","https://www.nejm.org/doi/full/10.1056/NEJMp2024897","","","","","","Lessons We've Learned In Baltimore, Covid-19 spread rapidly in the immigrant Latinx community. Transmission was fueled by poverty and economic necessity, and precarious housing arrangements and fea...","","","","","","","","","","","","",""
"Journal Article","Bowe E,Simmons E,Mattern S","","Learning from lines: Critical COVID data visualizations and the quarantine quotidian","Big Data & Society","Big Data & Society","2020","7","2","2053951720939236","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","SAGE Publications Ltd","","","","","2020-07-01","","","2053-9517","","https://doi.org/10.1177/2053951720939236;http://dx.doi.org/10.1177/2053951720939236;https://journals.sagepub.com/doi/abs/10.1177/2053951720939236;https://journals.sagepub.com/doi/full/10.1177/2053951720939236","10.1177/2053951720939236","","","","","In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled?and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic?s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations?many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care.","","","","","","","","","","","","",""
"Journal Article","Wissel BD,Van Camp PJ,Kouril M,Weis C,Glauser TA,White PS,Kohane IS,Dexheimer JW","","An interactive online dashboard for tracking covid-19 in us counties, cities, and states in real time","J. Am. Med. Inform. Assoc.","Journal of the American Medical Informatics Association: JAMIA","2020","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2020","","","1067-5027","","https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocaa071/5825284;https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocaa071/33128979/ocaa071.pdf?casa_token=ZrQvMIuojoAAAAAA:bzQJ4PGLqRlqyLsdoBirtG6jriPImZqHovckQM06JH1nndOmdoUjNb-9QvmNbBu7OMNFKHppp_n_","","","","","","… March 23rd, county level.[6] COVID Tracking Project Data. The COVID Tracking Project is a grassroots effort incubated by The Atlantic that tracks COVID- 19 testing in US states.[7] This group releases daily updates for the number of positive tests …","","","","","","","","","","","","",""
"Journal Article","Murray CJL,Alamro NMS,Hwang H,Lee U","","Digital public health and COVID-19","Lancet Public Health","The Lancet. Public health","2020","5","9","e469-e470","COVID Tracking Project","","","","thelancet.com","","","","","2020-09","","","2468-2667","","http://dx.doi.org/10.1016/S2468-2667(20)30187-0;https://www.ncbi.nlm.nih.gov/pubmed/32791051;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417165;https://linkinghub.elsevier.com/retrieve/pii/S2468-2667(20)30187-0;https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30187-0/fulltext?_hsmi=88974744&_hsenc=p2ANqtz-9XtlU3pAWn2JxYYHU-4V22VSWbgP36lM2TLFYRuoIgiSYs7_NF8VrlassgCXEvqlK27dAOxYL2r1z5zutIEpIind4lQg","10.1016/S2468-2667(20)30187-0","32791051","","","PMC7417165","… In the USA, for example, Black, Hispanic or Latino, and Native Americans are at much greater risk of COVID-19. 2 The COVID Tracking Project Racial data dashboard. https … 2. The COVID Tracking Project . Racial data dashboard. https …","","","","Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. Electronic address: cjlm@u.washington.edu. College of Medicine, King Saud University, Riyadh, Saudi Arabia. Seoul National University Bundang Hospital, Seoul, South Korea. Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.","en","Research Article","","","","","","",""
"Journal Article","Smith JP","","Comparison of COVID-19 case and death counts in the United States reported by four online trackers: January 22-May 31, 2020","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.20.20135764v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/25/2020.06.20.20135764.full.pdf","","","","","","… is to describe a project focused on comparing the numbers of COVID-19 cases and deaths in the United States reported by four different online trackers, namely, those maintained by USAFacts, the New York Times, Johns Hopkins University, and the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Weinberger DM,Cohen T,Crawford FW,Mostashari F,Olson D,Pitzer VE,Reich NG,Russi M,Simonsen L,Watkins A,Viboud C","","Estimating the early death toll of COVID-19 in the United States","bioRxiv","bioRxiv : the preprint server for biology","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-04-18","","","","","http://dx.doi.org/10.1101/2020.04.15.20066431;https://www.ncbi.nlm.nih.gov/pubmed/32511293;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217085;https://doi.org/10.1101/2020.04.15.20066431;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217085/","10.1101/2020.04.15.20066431","32511293","","","PMC7217085","Background: Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza (P&I) or all causes, can provide a more complete and consistent picture of the burden caused by COVID-19. Methods: We evaluated increases in the occurrence of deaths due to P&I above a seasonal baseline (adjusted for influenza activity) or due to any cause across the United States in February and March 2020. These estimates are compared with reported deaths due to COVID-19 and with testing data. Results: There were notable increases in the rate of death due to P&I in February and March 2020. In a number of states, these deaths pre-dated increases in COVID-19 testing rates and were not counted in official records as related to COVID-19. There was substantial variability between states in the discrepancy between reported rates of death due to COVID-19 and the estimated burden of excess deaths due to P&I. The increase in all-cause deaths in New York and New Jersey is 1.5-3 times higher than the official tally of COVID-19 confirmed deaths or the estimated excess death due to P&I. Conclusions: Excess P&I deaths provide a conservative estimate of COVID-19 burden and indicate that COVID-19-related deaths are missed in locations with inadequate testing or intense pandemic activity.","","","","Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT. Department of Biostatistics and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT; Yale Departments of Ecology and Evolutionary Biology, Statistics & Data Science, Yale School of Management. Aledade, Inc. Department of Health and Mental Hygiene, New York City, NY. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA. Department of Science and Environment, Roskilde University, Denmark. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD.","en","Research Article","","","","","","",""
"Journal Article","Souch JM,Cossman JS","","A Commentary on Rural-Urban Disparities in COVID-19 Testing Rates per 100,000 and Risk Factors","J. Rural Health","The Journal of rural health: official journal of the American Rural Health Association and the National Rural Health Care Association","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","0890-765X","","https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jrh.12450?casa_token=_qP51hbDYgcAAAAA:Qt3idEQ47CUx9gSuS5LH8mMXPuGK9Z0gzUAaEVdU24xg_Wrg7FRlGE2asLAcnqKrUl_Lw7jw1kUAb_0","","","","","","… Testing count and result data have been compiled and aggregated at the state level by the COVID Tracking Project daily.11 Rate of testing per 1,000,000 population and percent of … Accessed March 28, 2020. 11. The COVID Tracking Project . https://covidtracking.com …","","","","","","","","","","","","",""
"Journal Article","Dubov A,Shoptawb S","","The Value and Ethics of Using Technology to Contain the COVID-19 Epidemic","Am. J. Bioeth.","The American journal of bioethics: AJOB","2020","20","7","W7-W11","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-07-02","","","1526-5161","","https://doi.org/10.1080/15265161.2020.1764136;http://dx.doi.org/10.1080/15265161.2020.1764136;https://www.tandfonline.com/doi/full/10.1080/15265161.2020.1764136","10.1080/15265161.2020.1764136","","","","","… According to the COVID Tracking Project , the number of tests performed in the US has plateaued at about 130,000 to 160,000 a day (The COVID Tracking Project 2020) … arXiv 2003: 11511. [Google Scholar]; The COVID tracking project . 2020. Covidtracking.com …","","","","","","","","","","","","",""
"Preprint Manuscript","Kaashoek J,Santillana M","","COVID-19 Positive Cases, Evidence on the Time Evolution of the Epidemic or An Indicator of Local Testing Capabilities? A Case Study in the United States","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-10","2020-12-08","","","","https://papers.ssrn.com/abstract=3574849;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3574849;http://dx.doi.org/10.2139/ssrn.3574849;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3574849","10.2139/ssrn.3574849","","","","","The novel SARS-CoV-2 coronavirus, first identified in Wuhan (Hubei), China, in December 2019, has spread to more than 180 countries and caused over 1,700,000 cases of COVID-19 worldwide to date. In an effort to limit human-to-human contact and slow the transmission of COVID-19, the disease caused by this novel coronavirus, the United States have implemented a collection of shelter-in-place public health interventions. To monitor if these interventions are working and to determine when people may go back to (perhaps a new) business as usual requires reliable monitoring systems that provide an accurate real-time picture of the trajectory of the epidemic outbreak. Here, we present evidence that our current healthcare-based monitoring systems, aimed at detecting the new daily number of COVID-19-positive individuals across the US, may be better at tracking the local testing (detection) capabilities than at monitoring the time evolution of the outbreak. This suggests that other data sources are necessary to inform (real-time) critical decisions about when to stop (and perhaps when to restart) shelter-in-place mitigation strategies.","COVID-19, Disease surveillance, COVID-19 testing","","","","","","","","","","","","A Case Study in the United States (April"
"Journal Article","Chastain DB,Osae SP,Henao-Martínez AF,Franco-Paredes C,Chastain JS,Young HN","","Racial Disproportionality in Covid Clinical Trials","N. Engl. J. Med.","The New England journal of medicine","2020","383","9","e59","COVID Tracking Project","","","","Mass Medical Soc","","","","","2020-08-27","","","0028-4793","1533-4406","http://dx.doi.org/10.1056/NEJMp2021971;https://www.ncbi.nlm.nih.gov/pubmed/32780573;https://www.nejm.org/doi/10.1056/NEJMp2021971?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.nejm.org/doi/full/10.1056/NEJMp2021971","10.1056/NEJMp2021971","32780573","","","","Racial Disproportionality in Covid Clinical Trials To provide the necessary data for generalizing efficacy and safety outcomes across racial groups, Covid-19 clinical trials must prioritize inclusi...","","","","From the University of Georgia College of Pharmacy (D.B.C., S.P.O.) and Phoebe Putney Memorial Hospital (J.S.C.), Albany, and the University of Georgia College of Pharmacy, Athens (H.N.Y.); the Division of Infectious Diseases, University of Colorado, Anschutz Medical Campus, Aurora (A.F.H.-M., C.F.-P.); and the Hospital Infantil de México, Federico Gómez, México City (C.F.-P.).","en","Research Article","","","","","","",""
"Journal Article","Yang MJ,Seegert N,Gaulin M,Looney A,Orleans B,Pavia A,Stratford K,Samore M,Alder S","","What is the Active Prevalence of COVID-19?","Available at SSRN 3734463","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3734463;https://mjyang.com/wp-content/uploads/2020/09/COVID_Alpha-09162020.pdf","","","","","","Page 1. What is the Active Prevalence of COVID-19? Mu-Jeung Yanga, Nathan Seegerta, Maclean Gaulinb, Adam Looneya, Brian Orleansc, Andrew T. Paviad, Kristina Stratforde, Matthew Samoree, Steven Alderf aDepartment …","","","","","","","","","","","","",""
"Journal Article","Weinberger DM,Chen J,Cohen T,Crawford FW,Mostashari F,Olson D,Pitzer VE,Reich NG,Russi M,Simonsen L,Watkins A,Viboud C","","Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020","JAMA Intern. Med.","JAMA internal medicine","2020","180","10","1336-1344","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-10-01","","","2168-6106","2168-6114","http://dx.doi.org/10.1001/jamainternmed.2020.3391;https://www.ncbi.nlm.nih.gov/pubmed/32609310;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330834;https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/10.1001/jamainternmed.2020.3391;https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2767980;https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2767980","10.1001/jamainternmed.2020.3391","32609310","","","PMC7330834","Importance: Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19. Objective: To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020. Design, Setting, and Population: This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020. Main Outcomes and Measures: Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data. Results: There were approximately 781 000 total deaths in the United States from March 1 to May 30, 2020, representing 122 300 (95% prediction interval, 116 800-127 000) more deaths than would typically be expected at that time of year. There were 95 235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28% higher than the official tally of COVID-19-reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths. Conclusions and Relevance: Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states.","","","","Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland. Department of Biostatistics and the Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut. Departments of Ecology and Evolutionary Biology, Statistics and Data Science, Yale School of Management, New Haven, Connecticut. Aledade Inc, Bethesda, Maryland. Department of Health and Mental Hygiene, New York, New York. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst. Department of Science and Environment, Roskilde University, Fredeiksberg, Denmark.","en","Research Article","","","","","","",""
"Journal Article","Project CT","","US Historical Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Journal Article","Padalabalanarayanan S,Hanumanthu VS,Sen B","","Association of State Stay-at-Home Orders and State-Level African American Population With COVID-19 Case Rates","JAMA Netw Open","JAMA network open","2020","3","10","e2026010","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-10-01","","","2574-3805","","http://dx.doi.org/10.1001/jamanetworkopen.2020.26010;https://www.ncbi.nlm.nih.gov/pubmed/33095253;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584926;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.26010;https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2772155;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2772155","10.1001/jamanetworkopen.2020.26010","33095253","","","PMC7584926","Importance: To cope with the continuing coronavirus disease 2019 (COVID-19) pandemic, state and local officials need information on the effectiveness of policies aimed at curbing disease spread, as well as state-specific characteristics, like the racial mix, associated with increased risks related to the disease. Objective: To investigate whether state-imposed stay-at-home orders (SAHOs) and the proportion of African American population in a state were associated with the state-level COVID-19 cases. Design, Setting, and Participants: This cross-sectional study used daily, state-level data on COVID-19 cases, tests, and fatalities from the COVID Tracking Project. Data from March 1 to May 4, 2020, for all states (except Washington state) as well as the District of Columbia were used. Exposures: The key exposure variables were state-level SAHO (1 if in place, 0 otherwise), and proportion of state population who are African American. Main Outcomes and Measures: The primary outcome was daily cumulative COVID-19 case rates. A secondary outcome was subsequent COVID-19 fatality rates, derived using mean cumulative fatality rates 21 to 28 days after each date. Multivariate regression models were estimated. Results: The final sample included 3023 pooled state- and day-level observations. The mean (SD) cumulative positive case rate was 103.186 (200.067) cases per 100 000 state population, the mean (SD) cumulative test rate was 744.23 (894.944) tests per 100 000 state population, and the mean (SD) subsequent cumulative fatality rate was 12.923 (21.737) deaths per 100 000 state population. There was a negative association of SAHOs with cumulative case rates (β = -1.166; 95% CI, -1.484 to -0.847; P < .001) and subsequent fatality rates (β = -0.204; 95% CI, -0.294 to -0.113; P < .001). Estimation analyses indicated that expected cumulative case rates would have been more than 200% higher and fatality rates approximately 22% higher if there were no SAHOs, as compared with SAHOs fully in place. A higher proportion of African American population was associated with higher case rates (β = 0.045; 95% CI, 0.014 to 0.077; P = .001) and fatality rates (β = 0.068; 95% CI, 0.044 to 0.091; P < .001). Conclusions and Relevance: In this cross-sectional study, SAHOs were associated with reductions in COVID-19 case rates. These findings could help inform policy makers to address the continued COVID-19 pandemic in the US. The proportion of African American population was positively associated with COVID-19 case rates, and this state-level finding adds to evidence from existing ecological studies using county-level data on racial disparities in COVID-19 infection rates and underlines the urgency of better understanding and addressing these disparities.","","","","Department of Health Services Administration, University of Alabama at Birmingham, Birmingham. University of Alabama at Birmingham School of Health Professions, Birmingham. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham. Department of Health Care Organization and Policy, University of Alabama at Birmingham, Birmingham.","en","Research Article","","","","","","",""
"Journal Article","Zhang Y,Keegan LT,Yuqing Q,Samore MH","","The real time effective reproductive number for COVID-19 in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.08.20095703v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/13/2020.05.08.20095703.full.pdf","","","","","","… Using a collated time series of daily state-wise positive case counts from the COVID Tracking Project 2 , we estimate Rt to provide a baseline estimate of the impact of all combined non-pharmaceutical interventions (NPIs) at the state level and for the entire country …","","","","","","","","","","","","",""
"Journal Article","Allen WE,Altae-Tran H,Briggs J,Jin X,McGee G,Shi A,Raghavan R,Kamariza M,Nova N,Pereta A,Danford C,Kamel A,Gothe P,Milam E,Aurambault J,Primke T,Li W,Inkenbrandt J,Huynh T,Chen E,Lee C,Croatto M,Bentley H,Lu W,Murray R,Travassos M,Coull BA,Openshaw J,Greene CS,Shalem O,King G,Probasco R,Cheng DR,Silbermann B,Zhang F,Lin X","","Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing","Nat Hum Behav","Nature human behaviour","2020","4","9","972-982","COVID Tracking Project","","","","nature.com","","","","","2020-09","","","2397-3374","","http://dx.doi.org/10.1038/s41562-020-00944-2;https://www.ncbi.nlm.nih.gov/pubmed/32848231;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501153;https://doi.org/10.1038/s41562-020-00944-2;https://www.nature.com/articles/s41562-020-00944-2","10.1038/s41562-020-00944-2","32848231","","","PMC7501153","Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.","","","","The How We Feel Project, San Leandro, CA, USA. weallen@fas.harvard.edu. Society of Fellows, Harvard University, Cambridge, MA, USA. weallen@fas.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. weallen@fas.harvard.edu. The How We Feel Project, San Leandro, CA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. Schmidt Science Fellows, Oxford, UK. Society of Fellows, Harvard University, Cambridge, MA, USA. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Health Sciences and Technology Program, Massachusetts Institute of Technology and Harvard Medical School, Cambridge, MA, USA. Department of Biology, Stanford University, Stanford, CA, USA. Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA. Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. Albert J. Weatherhead III University Professor, Institute for Quantitative Social Sciences, Harvard University, Cambridge, MA, USA. The How We Feel Project, San Leandro, CA, USA. zhang_f@mit.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. zhang_f@mit.edu. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu. Howard Hughes Medical Institute, Chevy Chase, MD, USA. zhang_f@mit.edu. The How We Feel Project, San Leandro, CA, USA. xlin@hsph.harvard.edu. Broad Institute of MIT and Harvard, Cambridge, MA, USA. xlin@hsph.harvard.edu. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. xlin@hsph.harvard.edu. Department of Statistics, Harvard University, Cambridge, MA, USA. xlin@hsph.harvard.edu.","en","Research Article","","","","","","",""
"Journal Article","VoPham T,Weaver MD,Hart JE,Ton M,White E,Newcomb PA","","Effect of social distancing on COVID-19 incidence and mortality in the US","MedRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.10.20127589v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/12/2020.06.10.20127589.full.pdf","","","","","","… 173 Additional Contributions: 174 We thank Unacast, Johns Hopkins University, US Census Bureau, Robert Wood Johnson Foundation, 175 University of Wisconsin Population Health Institute, and The COVID Tracking Project for providing 176 publicly available data. 177 178 …","","","","","","","","","","","","",""
"Journal Article","Lyu W,Wehby GL","","Shelter-in-place orders reduced COVID-19 Mortality and reduced the rate of growth in hospitalizations: study examine effects of shelter-in-places orders on daily …","Health Aff.","Health affairs","2020","","","","COVID Tracking Project","","","","healthaffairs.org","","","","","2020","","","0092-8577","","https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2020.00719?casa_token=bXvDd40iOh0AAAAA:ec_UFK_uPZmAHTNvY0eGvNACCvQlJHp2Z2Mw1QczlR1UiP89jmuL-4gQUKrEgv2dc2l6-tkMxKk;https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2020.00719?casa_token=Ta_lF8VT8LMAAAAA:wN-KMU91p9wT6MKAIGY7baB0uzLoXcnMP120jS2jOSxtv7Lg5SNcOkmGrHtabBGjw4BpbFHVOGU","","","","","","… date. 21. Data on state-level COVID-19 hospitalizations come from The COVID Tracking Project , which collects COVID-19 related data from state public authorities. 22 Not all states systematically report data on hospitalizations …","","","","","","","","","","","","",""
"Journal Article","Harris JE","","Correction to: Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults","Rev. Econ. Househ.","Review of economics of the household","2020","","","1","COVID Tracking Project","","","","Springer","","","","","2020-09-09","","","1569-5239","","http://dx.doi.org/10.1007/s11150-020-09507-w;https://www.ncbi.nlm.nih.gov/pubmed/32922243;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480208;https://link.springer.com/article/10.1007/s11150-020-09496-w","10.1007/s11150-020-09507-w","32922243","","","PMC7480208","[This corrects the article DOI: 10.1007/s11150-020-09496-w.].","","","","Massachusetts Institute of Technology, Cambridge, MA 02139 USA.","en","Research Article","","","","","","",""
"News","Jaffe S","","Regulators split on antimalarials for COVID-19","Lancet","The Lancet","2020","395","10231","1179","COVID Tracking Project","","","","thelancet.com","","","","","2020-04-11","","","0140-6736","1474-547X","http://dx.doi.org/10.1016/S0140-6736(20)30817-5;https://www.ncbi.nlm.nih.gov/pubmed/32278373;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146712;https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(20)30817-5;https://www.thelancet.com/journals/lancet/article/PIIS0140-67362030817-5/fulltext?fbclid=IwAR07uIxUiRHEt3Nx4bjJJT5fNfNVkQtuEItJWQzhN7CtQ-2ssOw2iOQsGtE","10.1016/S0140-6736(20)30817-5","32278373","","","PMC7146712","… and ten deaths. Almost a month later, there are more than 363 000 cases and 10 847 people have died as of April 7, according to state and federal government data compiled by the COVID Tracking Project . Elmhurst, with 545 …","","","","","en","News","","","","","","",""
"Journal Article","Yam KC,Jackson JC,Barnes CM,Lau J,Qin X,Lee HY","","The rise of COVID-19 cases is associated with support for world leaders","Proc. Natl. Acad. Sci. U. S. A.","Proceedings of the National Academy of Sciences of the United States of America","2020","117","41","25429-25433","COVID Tracking Project","","","","National Acad Sciences","","","","","2020-10-13","","","0027-8424","1091-6490","http://dx.doi.org/10.1073/pnas.2009252117;https://www.ncbi.nlm.nih.gov/pubmed/32973100;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568236;http://www.pnas.org/cgi/pmidlookup?view=long&pmid=32973100;https://www.pnas.org/content/117/41/25429.short?casa_token=VgSgyQO2pBAAAAAA:L6JgHYJMuXH_JY9zUxMUlci4QVw7rE446mgJKzF6AUA-6MOjTBzlwaUm5EQpbDcFb9uBH7iMbJDcaw;https://www.pnas.org/content/pnas/117/41/25429.full.pdf?casa_token=TY5ahZWXzBoAAAAA:oBvlGmSKLqhujtvSFY05uYUkzAYvPPF0fc1nWnfnLVGrfn4Bm4s4tAV7Utho3Z8nNH4nxkAtYR3SYA","10.1073/pnas.2009252117","32973100","","","PMC7568236","COVID-19 has emerged as one of the deadliest and most disruptive events in recent human history. Drawing from political science and psychological theories, we examine the effects of daily confirmed cases in a country on citizens' support for the political leader through the first 120 d of 2020. Using three unique datasets which comprise daily approval ratings of head of government (n = 1,411,200) across 11 world leaders (Australia, Brazil, Canada, France, Germany, Hong Kong, India, Japan, Mexico, the United Kingdom, and the United States) and weekly approval ratings of governors across the 50 states in the United States (n = 912,048), we find a strong and significant positive association between new daily confirmed and total confirmed COVID-19 cases in the country and support for the heads of government. These analyses show that political leaders received a boost in approval in the early months of the COVID-19 pandemic. Moreover, these findings suggest that the previously documented \"rally 'round the flag\" effect applies beyond just intergroup conflict.","COVID-19; leader support; political support","","","Department of Management and Organisation, National University of Singapore, 119077 Singapore; bizykc@nus.edu.sg qinxin@mail.sysu.edu.cn. Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599. Department of Management and Organization, University of Washington, Seattle, WA 98195. Department of Human Resource Management, Temple University, Philadelphia, PA 19122. Department of Business Administration, Sun Yat-sen University, 510275 Guangzhou, China; bizykc@nus.edu.sg qinxin@mail.sysu.edu.cn. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.","en","Research Article","","","","","","",""
"Journal Article","Ala'raj M,Majdalawieh M,Nizamuddin N","","Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections","Infectious Disease Modelling","","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020-12-03","","","2468-0427","","http://www.sciencedirect.com/science/article/pii/S2468042720301032;http://dx.doi.org/10.1016/j.idm.2020.11.007;https://www.sciencedirect.com/science/article/pii/S2468042720301032","10.1016/j.idm.2020.11.007","","","","","The outbreak of novel coronavirus (COVID-19) attracted worldwide attention. It has posed a significant challenge for the global economies, especially the healthcare sector. Even with a robust healthcare system, countries were not prepared for the ramifications of COVID-19. Several statistical, dynamic, and mathematical models of the COVID-19 outbreak including the SEIR model have been developed to analyze the infection its transmission dynamics. The objective of this research is to use public data to study the properties associated with the COVID-19 pandemic to develop a dynamic hybrid model based on SEIRD and ascertainment rate with automatically selected parameters. The proposed model consists of two parts: the modified SEIRD dynamic model and ARIMA models. We fit SEIRD model parameters against historical values of infected, recovered and deceased population divided by ascertainment rate, which, in its turn, is also a parameter of the model. Residuals of the first model for infected, recovered, and deceased populations are then corrected using ARIMA models. The model can analyze the input data in real-time and provide long- and short-term forecasts with confidence intervals. The model was tested and validated on the USA COVID statistics dataset from the COVID Tracking Project. For validation, we use unseen recent statistical data. We use five common measures to estimate model prediction ability: MAE, MSE, MLSE, Normalized MAE, and Normalized MSE. We proved a great model ability to make accurate predictions of infected, recovered, and deceased patients. The output of the model can be used by the government, private sectors, and policymakers to reduce health and economic risks significantly improved consumer credit scoring.","COVID 19; Coronavirus; SEIRD model; ARIMA; Hybrid model","","","","","","","","","","","",""
"Journal Article","Mollalo A,Vahedi B,Rivera KM","","GIS-based spatial modeling of COVID-19 incidence rate in the continental United States","Sci. Total Environ.","The Science of the total environment","2020","728","","138884","COVID Tracking Project","","","","Elsevier","","","","","2020-08-01","","","0048-9697","1879-1026","http://dx.doi.org/10.1016/j.scitotenv.2020.138884;https://www.ncbi.nlm.nih.gov/pubmed/32335404;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175907;https://linkinghub.elsevier.com/retrieve/pii/S0048-9697(20)32401-3;https://www.sciencedirect.com/science/article/pii/S0048969720324013;https://www.researchgate.net/profile/Ismail_Mondal/post/What_GIS_tools_can_be_used_to_analyze_the_global_transmission_and_prediction_of_a_new_coronavirus_pandemic_COVID-193/attachment/5eac033dc005cf0001877dfd/AS%3A886339973169154%401588331325483/download/1-s2-0-s0048969720324013-main.pdf","10.1016/j.scitotenv.2020.138884","32335404","","","PMC7175907","During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.","COVID-19; GIS; Multiscale GWR; Spatial non-stationarity","","","Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA. Electronic address: amollalo@bw.edu. Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA, USA. Electronic address: behzad@ucsb.edu. Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA. Electronic address: krivera19@bw.edu.","en","Research Article","","","","","","",""
"Journal Article","Kullar R,Marcelin JR,Swartz TH,Piggott DA,Macias Gil R,Mathew TA,Tan T","","Racial Disparity of Coronavirus Disease 2019 in African American Communities","J. Infect. Dis.","The Journal of infectious diseases","2020","222","6","890-893","COVID Tracking Project","","","","academic.oup.com","","","","","2020-08-17","","","0022-1899","1537-6613","http://dx.doi.org/10.1093/infdis/jiaa372;https://www.ncbi.nlm.nih.gov/pubmed/32599614;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337812;https://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiaa372;https://academic.oup.com/jid/article-abstract/222/6/890/5864892;https://academic.oup.com/jid/article/222/6/890/5864892?casa_token=7v-z4p4A44MAAAAA:g76dvFUVOl5xyjmyeTgcSEI3lbF1BFvPoP2lgV0Gpnjpc9br8lD-z8sAbPo3k6a0PEB1sli5T4I7","10.1093/infdis/jiaa372","32599614","","","PMC7337812","The coronavirus disease 2019 (COVID-19) pandemic has unveiled unsettling disparities in the outcome of the disease among African Americans. These disparities are not new but are rooted in structural inequities that must be addressed to adequately care for communities of color. We describe the historical context of these structural inequities, their impact on the progression of COVID-19 in the African American (black) community, and suggest a multifaceted approach to addressing these healthcare disparities. (Of note, terminology from survey data cited for this article varied from blacks, African Americans, or both; for consistency, we use African Americans throughout.).","African Americans; COVID-19; SARS-CoV2; coronavirus; racial disparity","","","Expert Stewardship, Newport Beach, California, USA. Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, Nebraska, USA. Icahn School of Medicine at Mount Sinai, Department of Medicine, Division of Infectious Diseases, New York, New York, USA. Johns Hopkins University School of Medicine, Department of Medicine, Division of Infectious Diseases, Baltimore, Maryland, USA. Alpert Medical School of Brown University, Division of Infectious Diseases, Providence, Rhode Island, USA. Division of Infectious Diseases and International Medicine, Beaumont Hospital, Royal Oak, Michigan, USA. Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.","en","Research Article","","","","","","",""
"Journal Article","Weiner Z,Wong G,Elbanna A,Tkachenko A,Maslov S,Goldenfeld N","","Projections and early-warning signals of a second wave of the COVID-19 epidemic in Illinois","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.06.20147868v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/07/2020.07.06.20147868.full.pdf","","","","","","… Figure 2 shows COVID-19 hospitalizations (purple) and the daily case positivity rate (green) in Texas between June 1 and June 28 as reported by the COVID Tracking Project [5]. Over the period from June 15 through June 28 hospitalizations in Texas have increased with an ex …","","","","","","","","","","","","",""
"Journal Article","Kettl DF","","States Divided: The Implications of American Federalism for Covid‐19","Public Adm. Rev.","Public administration review","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","0033-3352","","https://onlinelibrary.wiley.com/doi/abs/10.1111/puar.13243?casa_token=GBdwYS4UggIAAAAA:iFoSDFY3JADlRpNrZH2tppPv6K7Ls3D02oCZwl08rYUp_CAHK76fD2atjI_2mK3dRCmqhpNyoAFGe-U;https://onlinelibrary.wiley.com/doi/pdf/10.1111/puar.13243?casa_token=wFKmUpwtVjgAAAAA:SV4wuuN0DjOWZ5lqENNETjubdCBwkBGTi85rD8bsaZ4EGNA02AVjAJ_q6pCPW0_2roA9K_pMI1VvDgk","","","","","","… A collaborative of media organizations created their own COVID Tracking Project (2020). Many of the data flowed from health care providers to county health departments, from … All rights reserved. Accepted Article Page 23. Covid Tracking Project . 2020. https://covidtracking.com …","","","","","","","","","","","","",""
"Journal Article","Schneider EC","","Failing the Test—The Tragic Data Gap Undermining the US Pandemic Response","N. Engl. J. Med.","The New England journal of medicine","2020","","","","COVID Tracking Project","","","","Mass Medical Soc","","","","","2020","","","0028-4793","","https://www.nejm.org/doi/full/10.1056/NEJMp2014836","","","","","","Failing the Test Tragically, the United States, unable to match other countries' pandemic response, has tallied the most Covid-19 cases and deaths in the world. Why has the US response been so in...","","","","","","","","","","","","",""
"Journal Article","Xu J,Hussain S,Lu G,Zheng K,Wei S,et al.","","Associations of stay-at-home order and face-masking recommendation with trends in daily new cases and deaths of laboratory-confirmed COVID-19 in the …","and hypothesis in …","","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020","","","","","https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7361445/;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7361445","","","","","pmc7361445","… Explor Res Hypothesis Med. 2020;5(2):1–6. doi: 10.14218/ERHM.2020.00023. [PMC free article] [PubMed] [CrossRef] [Google Scholar]. 2. COVID-Tracking. The COVID Tracking Project . Available from: https://covidtracking.com/about-data. Accessed July 3, 2020. 3. CDC …","","","","","","","","","","","","",""
"Journal Article","Data US","","The COVID Tracking Project","Link: https://bit. ly/3gb9pQf","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Miscellaneous","Meyer R,Kissane E,Madrigal A","","The COVID Tracking Project","","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Journal Article","Selden TM,Berdahl TA","","COVID-19 And Racial/Ethnic Disparities In Health Risk, Employment, And Household Composition: Study examines potential explanations for racial-ethnic disparities in COVID-19 hospitalizations and mortality","Health Aff.","Health affairs","2020","39","9","1624-1632","COVID Tracking Project","","","","healthaffairs.org","","","","","2020","","","0092-8577","","https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2020.00897?casa_token=i_Xhd7bjMj8AAAAA:8GCBInVuKLbdfDjm5RQ6kI9ZShOwvVudIGv6J6jeBwbE6DnbpMdsRoWvTbpHqgWz70ShmjhEtwQ;https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2020.00897?casa_token=Jqw-B-Y6U5oAAAAA:wKfLqezNRgZzj02wTBu9va-GlnqxOjRKGCZkkWvU-yu1pa8xcCW-ZfWx23o6odrq65T6iRfqtyU","","","","","","… Available from: https://www.npr.org/sections/health-shots/2020/05/30/865413079/what-do- coronavirus-racial-disparities-look-like-state-by-state Google Scholar; 2 Racial data dashboard. The Covid Tracking Project [serial on the Internet]. 2020 Jun 12 [cited 2020 Jul 15 ] …","","","","","","","","","","","","",""
"Journal Article","Piller C","","Federal hospital data system falters at tracking pandemic","Science","Science","2020","370","6521","1148-1149","COVID Tracking Project","","","","science.sciencemag.org","","","","","2020-12-04","","","0036-8075","1095-9203","http://dx.doi.org/10.1126/science.370.6521.1148;https://www.ncbi.nlm.nih.gov/pubmed/33273081;http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=33273081;https://science.sciencemag.org/content/370/6521/1148.summary?casa_token=0QccLSeclwgAAAAA:2Nx_jRc781YMSow8jl-bceB1V351SK8ih3aZRBJV202EllIlkYgC72RznZxCXTYYa9MjNCBoHYasoA;https://science.sciencemag.org/content/sci/370/6521/1148.full.pdf?casa_token=sAnrFcOaCMEAAAAA:TdT36T2tcM3JrzxzhcGaytsdwekJRqVaCiQEfw8Ev80MmVKTi-VfZmyzbwzEoY_fJjwa-vq1dvxJ4g","10.1126/science.370.6521.1148","33273081","","","","… part, why most media organizations—as well as President-elect Joe Biden's transition team—instead have relied on state or county websites that vary widely in completeness and quality, or on aggregations such as The Atlantic magazine's COVID Tracking Project , which collects …","","","","","en","Research Article","","","","","","",""
"Preprint Manuscript","Yang T,Shen K,He S,Li E,Sun P,Chen P,Zuo L,Hu J,Mo Y,Zhang W,Zhang H,Chen J,Guo Y","","CovidNet: To Bring Data Transparency in the Era of COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-22","","","","","http://arxiv.org/abs/2005.10948","","","2005.10948","","","Timely, creditable, and fine-granular case information is vital for local communities and individual citizens to make rational and data-driven responses to the COVID-19 pandemic. This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset, which was initiated by 1Point3Acres. To the best of our knowledge, the project is the only platform providing real-time global case information of more than 4,124 sub-divisions from over 27 countries worldwide with multi-language supports. The platform also offers interactive visualization tools to analyze the full historical case curves in each region. Initially launched as a voluntary project to bridge the data transparency gap in North America in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms. The accuracy and freshness of the dataset is a result of the painstaking efforts from our voluntary teamwork, crowd-sourcing channels, and automated data pipelines. As of May 18, 2020, the project website has been visited more than 200 million times and the CovidNet dataset has empowered over 522 institutions and organizations worldwide in policy-making and academic researches. All datasets are openly accessible for non-commercial purposes at https://coronavirus.1point3acres.com via a formal request through our APIs.","","","","","","","","arXiv","2005.10948","cs.CY","","","arXiv [cs.CY]"
"Journal Article","Newport KB,Malhotra S,Widera E","","Prognostication and Proactive Planning in COVID-19","J. Pain Symptom Manage.","Journal of pain and symptom management","2020","60","2","e52-e55","COVID Tracking Project","","","","Elsevier","","","","","2020-08","","","0885-3924","1873-6513","http://dx.doi.org/10.1016/j.jpainsymman.2020.04.152;https://www.ncbi.nlm.nih.gov/pubmed/32389604;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204675;https://linkinghub.elsevier.com/retrieve/pii/S0885-3924(20)30374-2;https://www.sciencedirect.com/science/article/pii/S0885392420303742;https://www.jpsmjournal.com/article/S0885-3924(20)30374-2/fulltext","10.1016/j.jpainsymman.2020.04.152","32389604","","","PMC7204675","Accurate prognostication is challenging in the setting of SARS-CoV-2, the virus responsible for COVID-19, due to rapidly changing data, studies that are not generalizable, and lack of morbidity and functional outcomes in survivors. To provide meaningful guidance to patients, existing mortality data must be considered and appropriately applied. Although most people infected with SARS-CoV-2 will recover, mortality increases with age and comorbidity in those who develop severe illness.","COVID-19; Prognosis; SARS-CoV-2; palliative care; proactive planning; prognostication","","","Section of Palliative Care, Penn State Health Department of Medicine, Hershey, Pennsylvania, USA. Electronic address: knewport@pennstatehealth.psu.edu. Section of General Internal Medicine & Geriatrics, Deming Department of Medicine, Tulane School of Medicine/University Medical Center New Orleans, New Orleans, Louisiana, USA. Division of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, California, USA.","en","Research Article","","","","","","",""
"Journal Article","Tromberg BJ,Schwetz TA,Pérez-Stable EJ,Hodes RJ,Woychik RP,Bright RA,Fleurence RL,Collins FS","","Rapid scaling up of Covid-19 diagnostic testing in the United States—the NIH RADx initiative","N. Engl. J. Med.","The New England journal of medicine","2020","383","11","1071-1077","COVID Tracking Project","","","","Mass Medical Soc","","","","","2020","","","0028-4793","","https://www.nejm.org/doi/full/10.1056/NEJMsr2022263","","","","","","RADx — Rapid Scaling Up of Covid-19 Testing In April 2020, Congress appropriated $1.5 billion to the NIH to increase national testing capacity for SARS-CoV-2. The NIH established the Rapid Accelera...","","","","","","","","","","","","",""
"Preprint Manuscript","Yang T,Shen K,He S,Li E,Sun P,Chen P,Zuo L,Hu J,Mo Y,Zhang W,Zhang H,Chen J,Guo Y","","CovidNet: To Bring Data Transparency in the Era of COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-22","","","","","http://arxiv.org/abs/2005.10948","","","2005.10948","","","Timely, creditable, and fine-granular case information is vital for local communities and individual citizens to make rational and data-driven responses to the COVID-19 pandemic. This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset, which was initiated by 1Point3Acres. To the best of our knowledge, the project is the only platform providing real-time global case information of more than 4,124 sub-divisions from over 27 countries worldwide with multi-language supports. The platform also offers interactive visualization tools to analyze the full historical case curves in each region. Initially launched as a voluntary project to bridge the data transparency gap in North America in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms. The accuracy and freshness of the dataset is a result of the painstaking efforts from our voluntary teamwork, crowd-sourcing channels, and automated data pipelines. As of May 18, 2020, the project website has been visited more than 200 million times and the CovidNet dataset has empowered over 522 institutions and organizations worldwide in policy-making and academic researches. All datasets are openly accessible for non-commercial purposes at https://coronavirus.1point3acres.com via a formal request through our APIs.","","","","","","","","arXiv","2005.10948","cs.CY","","","arXiv [cs.CY]"
"Journal Article","Piller C","","Federal hospital data system falters at tracking pandemic","Science","Science","2020","370","6521","1148-1149","COVID Tracking Project","","","","science.sciencemag.org","","","","","2020-12-04","","","0036-8075","1095-9203","http://dx.doi.org/10.1126/science.370.6521.1148;https://www.ncbi.nlm.nih.gov/pubmed/33273081;http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=33273081;https://science.sciencemag.org/content/370/6521/1148.summary?casa_token=0WKEiba_ZjwAAAAA:dvFvE9Opig70M0cpFbimVvEK3zoVD6HXmdw-qHYWTYjO9YCrZhoHmQojyan7adag3ObnHzDevOeATg;https://science.sciencemag.org/content/sci/370/6521/1148.full.pdf?casa_token=1T9jv1VX7-cAAAAA:32VRxKOMLXjdnsc-Iyu6If9jJRiAMlBShlCjGlWccbFpOM1I4Oj8kzj-DOJDUWzIwnmzCKcKDHJo1g","10.1126/science.370.6521.1148","33273081","","","","… part, why most media organizations—as well as President-elect Joe Biden's transition team—instead have relied on state or county websites that vary widely in completeness and quality, or on aggregations such as The Atlantic magazine's COVID Tracking Project , which collects …","","","","","en","Research Article","","","","","","",""
"Preprint Manuscript","Killeen BD,Wu JY,Shah K,Zapaishchykova A,Nikutta P,Tamhane A,Chakraborty S,Wei J,Gao T,Thies M,Unberath M","","A County-level Dataset for Informing the United States' Response to COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-01","","","","","http://arxiv.org/abs/2004.00756","","","2004.00756","","","As the coronavirus disease 2019 (COVID-19) continues to be a global pandemic, policy makers have enacted and reversed non-pharmaceutical interventions with various levels of restrictions to limit its spread. Data driven approaches that analyze temporal characteristics of the pandemic and its dependence on regional conditions might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction on the example of the United States, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph and Google mobility reports. We compile information from IHME, state and county-level government, and newspapers for dates of the enactment and reversal of non-pharmaceutical interventions. By collecting these data, as well as providing tools to read them, we hope to accelerate research that investigates how the disease spreads and why spread may be different across regions. Our dataset and associated code are available at github.com/JieYingWu/COVID-19_US_County-level_Summaries.","","","","","","","","arXiv","2004.00756","cs.CY","","","arXiv [cs.CY]"
"Journal Article","LeMasters K,McCauley E,Nowotny K,Brinkley-Rubinstein L","","COVID-19 Cases and Testing in 53 Prison Systems","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.25.20181842v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/31/2020.08.25.20181842.full.pdf","","","","","","… For general population COVID-19 counts, we used data for positive cases and fatalities from the Johns Hopkins Coronavirus Resource Center and data on testing numbers from the COVID Tracking Project .1,2 For the general population count, we used data from The US …","","","","","","","","","","","","",""
"Journal Article","Selden TM,Berdahl TA","","COVID-19 And Racial/Ethnic Disparities In Health Risk, Employment, And Household Composition: Study examines potential explanations for racial-ethnic disparities …","Health Aff.","Health affairs","2020","","","","COVID Tracking Project","","","","healthaffairs.org","","","","","2020","","","0092-8577","","https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2020.00897?casa_token=pSRfGfOxpigAAAAA:4IpBF98JMzqaN3KD2TH1x2pOR5K5HaMwl6QiqfKgYnBY66yyltsyxB2unjbJ4cUwWIIPVw-saow;https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2020.00897?casa_token=zZivfkEM1dAAAAAA:vkPi3_v8uk98miqzfXxcMaTZLjamKGKQdlvr7d1f32fUMCSxco5FeBSjis2YuTiWb-pwN_SEjOg","","","","","","… Available from: https://www.npr.org/sections/health-shots/2020/05/30/865413079/what-do- coronavirus-racial-disparities-look-like-state-by-state Google Scholar; 2 Racial data dashboard. The Covid Tracking Project [serial on the Internet]. 2020 Jun 12 [cited 2020 Jul 15 ] …","","","","","","","","","","","","",""
"Journal Article","Newport KB,Malhotra S,Widera E","","Prognostication and proactive planning in COVID-19","J. Pain Symptom Manage.","Journal of pain and symptom management","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020","","","0885-3924","","https://www.sciencedirect.com/science/article/pii/S0885392420303742;https://www.jpsmjournal.com/article/S0885-3924(20)30374-2/fulltext","","","","","","… Individual states, however, report similar hospitalization rates via The COVID Tracking Project , revealing rates ranging from 8-24% with higher hospitalization frequency correlating directly with age(7). Of those who were sick enough to require hospitalization in one hospital in …","","","","","","","","","","","","",""
"Journal Article","Data USH","","The COVID Tracking Project","Link: https://bit. ly/3gb9pQf","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"News","Williams LB","","COVID-19 disparity among Black Americans: A call to action for nurse scientists","Res. Nurs. Health","Research in nursing & health","2020","43","5","440-441","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-09","","","0160-6891","1098-240X","http://dx.doi.org/10.1002/nur.22056;https://www.ncbi.nlm.nih.gov/pubmed/32681736;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7404749;https://doi.org/10.1002/nur.22056;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7404749/","10.1002/nur.22056","32681736","","","PMC7404749","… International Journal of Maternal and Child Health and AIDS, 6(2), 139–164. 10.21106/ijma.236 [PMC free article] [PubMed] [CrossRef] [Google Scholar]; The COVID Tracking Project at the Atlantic (nd) . Retrieved from https://covidtracking.com/data …","","","","College of Nursing, University of Kentucky, Lexington, Kentucky.","en","News","","","","","","",""
"Journal Article","Bergquist S,Otten T,Sarich N","","COVID-19 pandemic in the United States","Health Policy Technol","Health policy and technology","2020","9","4","623-638","COVID Tracking Project","","","","Elsevier","","","","","2020-12","","","2211-8837","","http://dx.doi.org/10.1016/j.hlpt.2020.08.007;https://www.ncbi.nlm.nih.gov/pubmed/32874854;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451131;https://linkinghub.elsevier.com/retrieve/pii/S2211-8837(20)30079-4;https://www.sciencedirect.com/science/article/pii/S2211883720300794;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451131/","10.1016/j.hlpt.2020.08.007","32874854","","","PMC7451131","Objectives: The paper highlights US health policy and technology responses to the COVID-19 pandemic from January 1, 2020 - August 9, 2020. Methods: A review of primary data sources in the US was conducted. The data were summarized to describe national and state-level trends in the spread of COVID-19 and in policy and technology solutions. Results: COVID-19 cases and deaths initially peaked in late March and April, but after a brief reduction in June cases and deaths began rising again during July and continued to climb into early August. The US policy response is best characterized by its federalist, decentralized nature. The national government has led in terms of economic and fiscal response, increasing funding for scientific research into testing, treatment, and vaccines, and in creating more favorable regulations for the use of telemedicine. State governments have been responsible for many of the containment, testing, and treatment responses, often with little federal government support. Policies that favor economic re-opening are often followed by increases in state-level case numbers, which are then followed by stricter containment measures, such as mask wearing or pausing re-opening plans. Conclusions: While all US states have begun to \"re-open\" economic activities, this trend appears to be largely driven by social tensions and economic motivations rather than an ability to effectively test and surveil populations.","Coronavirus; Federalist policy; Pandemic; United States","","","Haas School of Business, UC Berkeley, United States. Erasmus School of Health Policy and Management, EUR, Netherlands. Management Center Innsbruck, MCI, Austria.","en","Research Article","","","","","","",""
"Journal Article","Hendryx M,Luo J","","COVID-19 prevalence and fatality rates in association with air pollution emission concentrations and emission sources","Environ. Pollut.","Environmental pollution ","2020","265","Pt A","115126","COVID Tracking Project","","","","Elsevier","","","","","2020-10","","","0269-7491","1873-6424","http://dx.doi.org/10.1016/j.envpol.2020.115126;https://www.ncbi.nlm.nih.gov/pubmed/32806422;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320861;https://linkinghub.elsevier.com/retrieve/pii/S0269-7491(20)33141-9;https://www.sciencedirect.com/science/article/pii/S0269749120331419?casa_token=iVCO1SVYFEIAAAAA:t7OwxxSLjJyQOSqoFT0dYczQ3RYi1nFuDXh4OaKlMDLRz1LPzsR9ncYlptcNS-aYt5JrNXMBBA","10.1016/j.envpol.2020.115126","32806422","","","PMC7320861","The novel coronavirus disease (COVID-19) is primarily respiratory in nature, and as such, there is interest in examining whether air pollution might contribute to disease susceptibility or outcome. We merged data on COVID-19 cumulative prevalence and fatality rates as of May 31, 2020 with 2014-2019 pollution data from the US Environmental Protection Agency Environmental Justice Screen (EJSCREEN), with control for state testing rates, population density, and population covariate data from the County Health Rankings. Pollution data included three types of air emission concentrations (particulate matter<2.5 μm (PM2.5), ozone and diesel particulate matter (DPM)), and four pollution source variables (proximity to traffic, National Priority List sites, Risk Management Plan (RMP) sites, and hazardous waste treatment, storage and disposal facilities (TSDFs)). Results of mixed model linear multiple regression analyses indicated that, controlling for covariates, COVID-19 prevalence and fatality rates were significantly associated with greater DPM. Proximity to TSDFs was associated to greater fatality rates, and proximity to RMPs was associated with greater prevalence rates. Results are consistent with previous research indicating that air pollution increases susceptibility to respiratory viral pathogens. Results should be interpreted cautiously given the ecological design, the time lag between exposure and outcome, and the uncertainties in measuring COVID-19 prevalence. Areas with worse prior air quality, especially higher concentrations of diesel exhaust, may be at greater COVID-19 risk, although further studies are needed to confirm these relationships.","Air pollution; COVID-19; Diesel particulate matter; Hazardous waste sites","","","Department of Environmental and Occupational Health, School of Public Health, Indiana University, 1025, E. 7th St., Bloomington, USA. Electronic address: hendryx@indiana.edu. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA.","en","Research Article","","","","","","",""
"Journal Article","Chen JT,Krieger N","","Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses","J. Public Health Manag. Pract.","Journal of public health management and practice: JPHMP","2021","27 Suppl 1, COVID-19 and Public Health: Looking Back, Moing Forward","","S43-S56","COVID Tracking Project","","","","ingentaconnect.com","","","","","2021","","","1078-4659","1550-5022","http://dx.doi.org/10.1097/PHH.0000000000001263;https://www.ncbi.nlm.nih.gov/pubmed/32956299;https://doi.org/10.1097/PHH.0000000000001263;https://www.ingentaconnect.com/content/wk/phh/2021/00000027/a00100s1/art00008","10.1097/PHH.0000000000001263","32956299","","","","OBJECTIVE: To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States. DESIGN: We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes. SETTING: US counties and zip codes in Illinois and New York City, as of May 5, 2020. MAIN OUTCOME MEASURES: Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures. RESULTS: As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (≥20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed. CONCLUSIONS: Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation.","","","","Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.","en","Research Article","","","","","","",""
"Journal Article","Chen X,Hazra DK","","Understanding the Bias between the Number of Confirmed Cases and Actual Number of Infections in the COVID-19 Pandemic","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.22.20137208v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/23/2020.06.22.20137208.full.pdf","","","","","","… We use the data of the following states from the database of the Covid Tracking Project [2] (see also [3]), because these states provide all necessary information to extract these four quantities and at the same time have large values of daily new hospitalizations (H ≫ 10) so that …","","","","","","","","","","","","",""
"Journal Article","Hamidi S,Ewing R,Sabouri S","","Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States","Health Place","Health & place","2020","64","","102378","COVID Tracking Project","","","","Elsevier","","","","","2020","","","1353-8292","","https://www.sciencedirect.com/science/article/pii/S1353829220305244?casa_token=Fx8E0V8bx74AAAAA:X0eKa1vqP9pgumTADJ9Jdsx4c-royxbVAROHfJ7cEEfrtRuWS8ZBrxh7xZX9Fx2i43_Abz-Dsg","","","","","","… ln of activity density (population + employment per square mile), ACS, 2017; LEHD 2017 (US Census Bureau, 2020b), 5.48 (1.47). state-wide number of COVID-19 testing per 10,000 population, The COVID Tracking Project d, 432.48 (162.55) … d The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Venzin M","","COVID‐19 Dashboard Helps Associations Think Proactively","The Membership Management Report","The Membership Management Report","2020","16","6","3","COVID Tracking Project","","","","Wiley-Blackwell","","","","","2020-06","2020-12-08","","","","https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267256/;http://dx.doi.org/10.1002/mmr.31480;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267256","10.1002/mmr.31480","","","","PMC7267256","… Gravitate Solutions, an analytics company dedicated to the member industry, has developed the COVID‐19 Analytics Dashboard for Associations using datasets provided by the COVID Tracking Project , The New York Times and other global COVID 19 data sources …","","","","","en","","","","","","","",""
"Journal Article","IHME COVID-19 Forecasting Team","","Modeling COVID-19 scenarios for the United States","Nat. Med.","Nature medicine","2020","","","","COVID Tracking Project","","","","nature.com","","","","","2020-10-23","","","1078-8956","1546-170X","http://dx.doi.org/10.1038/s41591-020-1132-9;https://www.ncbi.nlm.nih.gov/pubmed/33097835;https://doi.org/10.1038/s41591-020-1132-9;https://www.nature.com/articles/s41591-020-1132-9","10.1038/s41591-020-1132-9","33097835","","","","We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state-with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)-suggest that, cumulatively, 511,373 (469,578-578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284-170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731-133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario.","","","","","en","Research Article","","","","","","",""
"Journal Article","Yap FF,Yong M","","Implementation of An Online COVID-19 Epidemic Calculator for Tracking the Spread of the Coronavirus in Singapore and Other Countries","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.02.20120188v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/04/2020.06.02.20120188.full.pdf","","","","","","Page 1. 1 Implementation of An Online COVID-19 Epidemic Calculator for Tracking the Spread of the Coronavirus in Singapore and Other Countries Fook Fah YAP1*, Minglee YONG2 1 School of Mechanical and Aerospace …","","","","","","","","","","","","",""
"Journal Article","Zhang W,Oltean A,Nichols S,Odeh F,Zhong F","","Epidemiology of Reopening in the COVID-19 Pandemic in the United States, Europe and Asia","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.05.20168757v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/06/2020.08.05.20168757.full.pdf","","","","","","Page 1. Page 1 of 16 Epidemiology of Reopening in the COVID-19 Pandemic in the United States, Europe and Asia Authors: Weiqi Zhang 1, Alina Oltean 2, Scott Nichols 2, Fuad Odeh 2, Fei Zhong 2 Affiliations: 1 Merck Holding …","","","","","","","","","","","","",""
"Journal Article","Miller IF,Becker AD,Grenfell BT,Metcalf CJE","","Disease and healthcare burden of COVID-19 in the United States","Nat. Med.","Nature medicine","2020","26","8","1212-1217","COVID Tracking Project","","","","nature.com","","","","","2020-08","","","1078-8956","1546-170X","http://dx.doi.org/10.1038/s41591-020-0952-y;https://www.ncbi.nlm.nih.gov/pubmed/32546823;https://doi.org/10.1038/s41591-020-0952-y;https://www.nature.com/articles/s41591-020-0952-y","10.1038/s41591-020-0952-y","32546823","","","","As of 24 April 2020, the SARS-CoV-2 epidemic has resulted in over 830,000 confirmed infections in the United States1. The incidence of COVID-19, the disease associated with this new coronavirus, continues to rise. The epidemic threatens to overwhelm healthcare systems, and identifying those regions where the disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency medical care and public health resources. Globally, the risk of severe outcomes associated with COVID-19 has consistently been observed to increase with age2,3. We used age-specific mortality patterns in tandem with demographic data to map projections of the cumulative case burden of COVID-19 and the subsequent burden on healthcare resources. The analysis was performed at the county level across the United States, assuming a scenario in which 20% of the population of each county acquires infection. We identified counties that will probably be consistently, heavily affected relative to the rest of the country across a range of assumptions about transmission patterns, such as the basic reproductive rate, contact patterns and the efficacy of quarantine. We observed a general pattern that per capita disease burden and relative healthcare system demand may be highest away from major population centers. These findings highlight the importance of ensuring equitable and adequate allocation of medical care and public health resources to communities outside of major urban areas.","","","","Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. ifmiller@princeton.edu. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. Woodrow Wilson School of Public Affairs, Princeton University, Princeton, NJ, USA.","en","Research Article","","","","","","",""
"Journal Article","Husain I,Briggs B,Lefebvre C,Cline DM,Stopyra JP,O'Brien MC,Vaithi R,Gilmore S,Countryman C","","Fluctuation of Public Interest in COVID-19 in the United States: Retrospective Analysis of Google Trends Search Data","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","3","e19969","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-07-17","","","2369-2960","","http://dx.doi.org/10.2196/19969;https://www.ncbi.nlm.nih.gov/pubmed/32501806;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371405;https://publichealth.jmir.org/2020/3/e19969/","10.2196/19969","32501806","","","PMC7371405","BACKGROUND: In the absence of vaccines and established treatments, nonpharmaceutical interventions (NPIs) are fundamental tools to control coronavirus disease (COVID-19) transmission. NPIs require public interest to be successful. In the United States, there is a lack of published research on the factors that influence public interest in COVID-19. Using Google Trends, we examined the US level of public interest in COVID-19 and how it correlated to testing and with other countries. OBJECTIVE: The aim of this study was to determine how public interest in COVID-19 in the United States changed over time and the key factors that drove this change, such as testing. US public interest in COVID-19 was compared to that in countries that have been more successful in their containment and mitigation strategies. METHODS: In this retrospective study, Google Trends was used to analyze the volume of internet searches within the United States relating to COVID-19, focusing on dates between December 31, 2019, and March 24, 2020. The volume of internet searches related to COVID-19 was compared to that in other countries. RESULTS: Throughout January and February 2020, there was limited search interest in COVID-19 within the United States. Interest declined for the first 21 days of February. A similar decline was seen in geographical regions that were later found to be experiencing undetected community transmission in February. Between March 9 and March 12, 2020, there was a rapid rise in search interest. This rise in search interest was positively correlated with the rise of positive tests for SARS-CoV-2 (6.3, 95% CI -2.9 to 9.7; P<.001). Within the United States, it took 52 days for search interest to rise substantially after the first positive case; in countries with more successful outbreak control, search interest rose in less than 15 days. CONCLUSIONS: Containment and mitigation strategies require public interest to be successful. The initial level of COVID-19 public interest in the United States was limited and even decreased during a time when containment and mitigation strategies were being established. A lack of public interest in COVID-19 existed in the United States when containment and mitigation policies were in place. Based on our analysis, it is clear that US policy makers need to develop novel methods of communicating COVID-19 public health initiatives.","COVID-19; Google Trends; Infodemiology; SARS-CoV-2; digital health; internet; public health; trend","","","School of Medicine, Wake Forest University, Winston-Salem, NC, United States. Tuba City Regional Healthcare, Tuba City, AZ, United States.","en","Research Article","","","","","","",""
"Review","Allam M,Cai S,Ganesh S,Venkatesan M,Doodhwala S,Song Z,Hu T,Kumar A,Heit J,Study Group C,Coskun AF","","COVID-19 Diagnostics, Tools, and Prevention","Diagnostics (Basel)","Diagnostics (Basel, Switzerland)","2020","10","6","","COVID Tracking Project","","","","mdpi.com","","","","","2020-06-16","","","2075-4418","","http://dx.doi.org/10.3390/diagnostics10060409;https://www.ncbi.nlm.nih.gov/pubmed/32560091;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344926;https://www.mdpi.com/resolver?pii=diagnostics10060409;https://www.mdpi.com/2075-4418/10/6/409;https://www.mdpi.com/2075-4418/10/6/409/pdf","10.3390/diagnostics10060409","32560091","","","PMC7344926","The Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), outbreak from Wuhan City, Hubei province, China in 2019 has become an ongoing global health emergency. The emerging virus, SARS-CoV-2, causes coughing, fever, muscle ache, and shortness of breath or dyspnea in symptomatic patients. The pathogenic particles that are generated by coughing and sneezing remain suspended in the air or attach to a surface to facilitate transmission in an aerosol form. This review focuses on the recent trends in pandemic biology, diagnostics methods, prevention tools, and policies for COVID-19 management. To meet the growing demand for medical supplies during the COVID-19 era, a variety of personal protective equipment (PPE) and ventilators have been developed using do-it-yourself (DIY) manufacturing. COVID-19 diagnosis and the prediction of virus transmission are analyzed by machine learning algorithms, simulations, and digital monitoring. Until the discovery of a clinically approved vaccine for COVID-19, pandemics remain a public concern. Therefore, technological developments, biomedical research, and policy development are needed to decipher the coronavirus mechanism and epidemiological characteristics, prevent transmission, and develop therapeutic drugs.","3D printing; COVID-19; SARS-CoV-2; digital tracking; do-it-yourself; immunity; machine learning; pandemic policy; rapid testing; vaccines","","","Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.","en","Review","","","","","","",""
"Journal Article","Sen-Crowe B,McKenney M,Elkbuli A","","Consistency and reliability of COVID-19 projection models as a means to save lives","Am. J. Emerg. Med.","The American journal of emergency medicine","2020","","","","COVID Tracking Project","","","","ajemjournal.com","","","","","2020-07-12","","","0735-6757","1532-8171","http://dx.doi.org/10.1016/j.ajem.2020.07.020;https://www.ncbi.nlm.nih.gov/pubmed/32713604;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354274;https://linkinghub.elsevier.com/retrieve/pii/S0735-6757(20)30611-2;https://www.ajemjournal.com/article/S0735-6757(20)30611-2/abstract;https://www.ajemjournal.com/article/S0735-6757(20)30611-2/fulltext","10.1016/j.ajem.2020.07.020","32713604","","","PMC7354274","… conclusions. Texas has been reporting increasing cases since mid-June [ 6 Total by state. The COVID tracking project . https://covidtracking.com/data Date accessed: July 4, 2020. Google Scholar. ] … state. The COVID tracking project …","","","","Division of Trauma and Acute Care Surgery, Department of Surgery, Kendall Regional Medical Center, Miami, FL, USA. Division of Trauma and Acute Care Surgery, Department of Surgery, Kendall Regional Medical Center, Miami, FL, USA; University of South Florida, Tampa, FL, USA. Division of Trauma and Acute Care Surgery, Department of Surgery, Kendall Regional Medical Center, Miami, FL, USA. Electronic address: Adel.Elkbuli@hcahealthcare.com.","en","Research Article","","","","","","",""
"Book Chapter","Qazi S,Ahmad S,Raza K","Raza K","Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks","","","2021","","","47-59","COVID Tracking Project","","","","Springer Singapore","Singapore","Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis","","","2021","","9789811585340","","","https://doi.org/10.1007/978-981-15-8534-0_3;http://dx.doi.org/10.1007/978-981-15-8534-0_3;https://link.springer.com/chapter/10.1007/978-981-15-8534-0_3","10.1007/978-981-15-8534-0_3","","","","","The novel coronavirus disease (COVID-19) causes serious respiratory tract infections in humans, and worse leads to mortality in old-aged people or individuals with co-morbidities. Websites and online social platforms generate a gargantuan amount of data in myriad aspects namely—technology, global news, human healthcare, medicine, socio-political domain, etc., aiding to decipher significant knowledge using web mining. Since the outbreak, people from different geographical locations used hashtags about novel coronavirus. The FAMEC model, the Honghou Hybrid System (HHS), the COVID Tracking Project of Twitter are a few examples of computational intelligent online social trackers that have been devised to track the COVID-19 pandemic. Researchers have identified the significance of tweets to be consistent with the CDC and the WHO reports and discerned that mining of such personal tweets was effective to track, manage, and predict the mortality and morbidity rates, identify the geographic location of patients infected which would, in turn, lead to rapid treatment assessment, employment of telemedicine and sanitization of such regions. This chapter presents how computational intelligence along with online social networks can be used for tracking COVID-19 patients.","","","","","","","","","","","","",""
"Journal Article","Mavragani A,Gkillas K","","COVID-19 predictability in the United States using Google Trends time series","Sci. Rep.","Scientific reports","2020","10","1","20693","COVID Tracking Project","","","","nature.com","","","","","2020-11-26","","","2045-2322","","http://dx.doi.org/10.1038/s41598-020-77275-9;https://www.ncbi.nlm.nih.gov/pubmed/33244028;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692493;https://doi.org/10.1038/s41598-020-77275-9;https://www.nature.com/articles/s41598-020-77275-9","10.1038/s41598-020-77275-9","33244028","","","PMC7692493","During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.","","","","Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, Scotland, UK. amaryllis.mavragani1@stir.ac.uk. Department of Management Science and Technology, University of Patras, Patras, Greece.","en","Research Article","","","","","","",""
"Journal Article","Wang G,Gu Z,Li X,Yu S,Kim M,Wang Y,Gao L,Wang L","","Comparing and Integrating US COVID-19 Data from Multiple Sources with Anomaly Detection and Repairing","arXiv preprint arXiv:2006. 01333","","2020","","","","COVID Tracking Project","","","","128.84.4.34","","","","","2020","","","","","http://128.84.4.34/abs/2006.01333","","","","","","… We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences …","","","","","","","","","","","","",""
"Preprint Manuscript","Amla K,Amla T","","The Impact of Public Safety Measures on the Spread of COVID-19 in the United States Assessed By Causal Model-Based Projections of the Pandemic","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-07","","","","","http://arxiv.org/abs/2004.03200","","","2004.03200","","","The novel coronavirus, SARS-CoV-2, and the disease it causes, COVID-19 was declared a pandemic on March 11, 2020 by the World Health Organization. Since then, the disease has spread all over the world, with the United States becoming the country with the highest number of cases. Governments around the world have undertaken varying degrees of public safety measures, including recommendations and ad campaigns for improved hygiene practices, enacting social distancing requirements and limiting large public gatherings, and stay-at-home orders and lockdowns. In the United States, while the response has varied greatly from state to state, we clearly see that the effect of these public safety measures is significant and, if these measures continue to remain in effect, or are expanded to a nationwide lockdown, the pandemic can be controlled and the disease likely overcome with mitigated consequences. In this paper, we model the spread of the novel coronavirus using a causal model. We find that, with continued lockdown measures, the United States can, according to this model, limit the total number of infections to ~1.35 million and the total number of deaths to ~72 thousand. A 60 day lockdown can save countless lives.","","","","","","","","arXiv","2004.03200","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Karaye IM,Horney JA","","The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships","Am. J. Prev. Med.","American journal of preventive medicine","2020","59","3","317-325","COVID Tracking Project","","","","Elsevier","","","","","2020","","","0749-3797","","https://www.sciencedirect.com/science/article/pii/S0749379720302592;https://www.ajpmonline.org/article/S0749-3797(20)30259-2/fulltext","","","","","","… Research. 36 Data on the number of people tested for COVID-19 were derived from the COVID Tracking Project . 37 Finally, regional boundary data on states and counties were downloaded from data.gov. 38. Measures. In the …","","","","","","","","","","","","",""
"Report","Chetty R,Friedman JN,Hendren N,Stepner M,The Opportunity Insights Team","","The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27431","nber.org","","","","","2020-06-29","2020-12-08","","","","https://www.nber.org/papers/w27431;http://dx.doi.org/10.3386/w27431;https://www.nber.org/system/files/working_papers/w27431/w27431.pdf","10.3386/w27431","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Chitwood MH,Russi M,Gunasekera K,Havumaki J,Pitzer VE,Warren JL,Weinberger D,Cohen T,Menzies NA","","Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","Cold Spring Harbor Laboratory Press","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.17.20133983v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/20/2020.06.17.20133983.full.pdf","","","","","","… We used publicly available data from the COVID Tracking Project (7) to produce the estimates reported here. Because states report cases and deaths in different ways, there are a number of potential inconsistencies in these data … 35 7. The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Byrnes YM,Civantos AM,Go BC,McWilliams TL,Rajasekaran K","","Effect of the COVID-19 pandemic on medical student career perceptions: a national survey study","Med. Educ. Online","Medical education online","2020","25","1","1798088","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-12","","","1087-2981","","http://dx.doi.org/10.1080/10872981.2020.1798088;https://www.ncbi.nlm.nih.gov/pubmed/32706306;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482653;http://www.tandfonline.com/doi/full/10.1080/10872981.2020.1798088;https://www.tandfonline.com/doi/abs/10.1080/10872981.2020.1798088;https://www.tandfonline.com/doi/pdf/10.1080/10872981.2020.1798088","10.1080/10872981.2020.1798088","32706306","","","PMC7482653","BACKGROUND & OBJECTIVE: The COVID-19 pandemic and resulting cancellation of medical student clinical rotations pose unique challenges to students' educations, the impact of which has not yet been explored. DESIGN: This cross-sectional survey study collected responses from 13 April 2020 until 30 April 2020. Students at US allopathic medical schools completed the survey online. RESULTS: 1,668 responses were analyzed. A total of 337 (20.2%) respondents thought the pandemic would affect their choice of specialty, with differences across class years: 15.2% (53) of first-years (MS1s), 26.4% (92) of second-years (MS2s), 23.7% (162) of third-years (MS3s), and 9.7% (22) of fourth-years (MS4s) (p < 0.0001). Among all classes, the most common reason chosen was inability to explore specialties of interest (244, 72.4%), and the second was inability to bolster their residency application (162, 48.1%). Out of the MS3s who chose the latter, the majority were concerned about recommendation letters (68, 81.0%) and away rotations (62, 73.8%). As high as 17.4% (119) of MS3s said they were more likely to take an extra year during medical school as a result of the pandemic. Region of the US, number of local COVID cases, and number of local COVID deaths had no effect on whether respondents thought the pandemic would affect their specialty choice. CONCLUSIONS: Our study found that about one-fifth of surveyed medical students currently believe that the COVID-19 pandemic will affect their choice of specialty, with many of these citing concerns that they cannot explore specialties or obtain recommendation letters. With prolonged suspension of clinical rotations, targeted efforts by medical schools to address these concerns through enhanced virtual curriculum development and advising strategies will become increasingly important. Further study is needed to explore whether these cross-sectional student perspectives will manifest as changes in upcoming National Residency Matching Program data.","‘COVID-19’; ‘Medical student’; ‘career development’; ‘specialty choice’; ‘virtual education’","","","Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, USA. Biostatistics Analysis Center, University of Pennsylvania , Philadelphia, PA, USA. Department of Otorhinolaryngology - Head & Neck Surgery, University of Pennsylvania , Philadelphia, PA, USA.","en","Research Article","","","","","","",""
"Journal Article","Roser M,Ritchie H,Ortiz-Ospina E,Hasell J","","Coronavirus disease (COVID-19)--Statistics and research","Our World in data","","2020","","","","COVID Tracking Project","","","","sipotra.it","","","","","2020","","","","","https://www.sipotra.it/wp-content/uploads/2020/03/Coronavirus-Disease-COVID-19-%E2%80%93-Statistics-and-Research.pdf","","","","","","Page 1. Coronavirus Disease (COVID-19) – Statistics and Research …","","","","","","","","","","","","",""
"Journal Article","Gregory N","","Health-system pharmacists discuss racial disparities in COVID-19 pandemic","Am. J. Health. Syst. Pharm.","American journal of health-system pharmacy: AJHP: official journal of the American Society of Health-System Pharmacists","2020","77","23","1932-1933","COVID Tracking Project","","","","academic.oup.com","","","","","2020-11-16","","","1079-2082","1535-2900","http://dx.doi.org/10.1093/ajhp/zxaa360;https://www.ncbi.nlm.nih.gov/pubmed/33196817;https://academic.oup.com/ajhp/article-lookup/doi/10.1093/ajhp/zxaa360;https://academic.oup.com/ajhp/article-abstract/77/23/1932/5983366;https://academic.oup.com/ajhp/article-pdf/77/23/1932/34322416/zxaa360.pdf?casa_token=192VxldY6zwAAAAA:usj6jCgn_NG7C_VOehw3s10xO879O8g4--KuXMrYtYweZ9Dguh69g2pKkou-YxdS5_7DjN3Bj5D-","10.1093/ajhp/zxaa360","33196817","","","","… ethnicity. • The COVID Racial Data Tracker, a collaboration between the COVID Tracking Project and the Boston University Center for Antiracist Research, reports that black Americans have died at 2.5 times the rate of Whites …","","","","","en","Research Article","","","","","","",""
"Journal Article","Mattos D","","Community Development in the Time of COVID-19","","","2020","","","","COVID Tracking Project","","","","digitalcommons.unl.edu","","","","","2020","2020-12-08","","","","https://digitalcommons.unl.edu/agecon_cornhusker/1073/;https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=2072&context=agecon_cornhusker","","","","","","The global pandemic has driven the whole country into an unprecedented crisis. As the months passed and the death toll climbed, the pandemic did some-thing else: it unveiled deep inequities within the country. Those getting sick and dying were disproportionately low-income racial and ethnographic minorities, most of them essential workers. According to the lat-est data from the Center for Disease Control and Prevention, the virus has overly affected Black people and Latinos (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html). Communities of color are also over represented among essential workers who are generally unable to work from home and more likely to come into contact with the virus stretching the racial wealth gap in the United States and making the richest wealthier while leaving many of the poorest without jobs (https://www.epi.org/blog/black-and-hispanic-workers-are-much-less-likely-to-be-able-to-work-from-home/).","","","","","","","Cornhusker Economics","","","","","",""
"Miscellaneous","Miller K,Curry K","","The COVID tracking project","","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Journal Article","Liang Z,Pang M,Yang X,Li J,Wang Y,Li Z,Zhang Y,et al.","","Weekly Assessment of the COVID-19 Pandemic and Risk of Importation—China, April 8, 2020","CCDCW","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Xiao_Ping_Dong/publication/341693715_Notes_from_the_Field_Weekly_Assessment_of_the_COVID-19_Pandemic_and_Risk_of_Importation_-China_April_8_2020/links/5ecf1d7e92851c9c5e62e8dc/Notes-from-the-Field-Weekly-Assessment-of-the-COVID-19-Pandemic-and-Risk-of-Importation-China-April-8-2020.pdf","","","","","","… From the COVID Tracking Project website (5), we have also collected data on the numbers of the hospitalized COVID-19 cases on April 8 in the 6 most affected states … worldometers.info/ coronavirus/country/us/. [2020-04-8]. 3. The COVID Tracking Project . US Historical Data …","","","","","","","","","","","","",""
"Miscellaneous","Lipton Z,Ellington J,Riley K","","The COVID tracking project","","","2020","","","","COVID Tracking Project","","","","Zenodo","","","","","2020","","","","","","","","","","","","","","","","","","","","","","","",""
"Conference Paper","Wolfe G,Elnashar A,Schreiber W,Alsmadi I","","COVID-19 Candidate Treatments, a Data Analytics Approach","","","2020","","","139-146","COVID Tracking Project","","","","ieeexplore.ieee.org","","","2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA)","","2020-10","","","","","http://dx.doi.org/10.1109/MCNA50957.2020.9264290;https://ieeexplore.ieee.org/abstract/document/9264290/?casa_token=6AC6kSgupvcAAAAA:mo3cm-kQoexqDc-wTWly4XHWHUV9GLeuMlbxPbY0rqbFPr6HN2DHt4IkSOY0FQ3UNIHn4iUWRg;https://ieeexplore.ieee.org/iel7/9264268/9264272/09264290.pdf?casa_token=dmzbrCxqWe8AAAAA:DlcImgG31Klc-LfnlXRiGUZbdHKDkIIJlI9l02bBmc8tfyRJXYbov5Z3sWYSSCcgoTyReEZvkA","10.1109/MCNA50957.2020.9264290","","","","","COVID-19, short for “coronavirus disease 2019” has majorly affected millions of people worldwide. In the U.S. alone as of the end of this week (June 1, 2020), there have been 1,790,191 total cases, with 104,383 deaths. There have been 6,166,978 cases in the entire world, with 372,037 deaths, these are just the reported cases. Our focus in this research is in evaluating a repository of research papers to extract knowledge related to COVID-19 and possible treatments. Driven by the COVID-19 Open Research Dataset Challenge from Kaggle, we focused on a subset of that, COVID-19 Pulmonary Risks Literature Clustering. The second dataset we are using is from the Maryland Transportation Institute (MTI). The data is broken up into four categories: (1) Mobility and Social Distancing, (2) COVID and Health, (3) Economic Impact, and (4) Vulnerable Population. The data is extracted from NPR, ESRI, the COVID tracking project, CDC, and several other sources. MTI has been the source of several papers regarding mobility impact, social distancing, stay at-home orders, and non-pharmaceutical interventions.","COVID-19;Diseases;Libraries;Feature extraction;Data models;Multimedia computing;Dictionaries;COVID-19;Coronavirus;Risk Factors;Pulmonary Disease","","","","","","","","","","","",""
"Journal Article","Aliprantis D,Tauber K","","Measuring deaths from covid-19","Economic Commentary","","2020","","2020-17","","COVID Tracking Project","","","","Federal Reserve Bank of Cleveland","","","","","2020","","","","","https://www.clevelandfed.org/ec202018","","","","","","… with excess-mortality data. We concentrate on data aggregated from state health departments by the COVID Tracking Project . The COVID Tracking Project provides daily updated state-level and national data. It also provides …","","","","","","","","","","","","",""
"Journal Article","Long C,Fu XM,Fu ZF","","Global analysis of daily new COVID-19 cases reveals many static-phase countries including the United States potentially with unstoppable epidemic","World J Clin Cases","World journal of clinical cases","2020","8","19","4431-4442","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-10-06","","","2307-8960","","http://dx.doi.org/10.12998/wjcc.v8.i19.4431;https://www.ncbi.nlm.nih.gov/pubmed/33083402;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559691;https://www.wjgnet.com/2307-8960/full/v8/i19/4431.htm;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559691/","10.12998/wjcc.v8.i19.4431","33083402","","","PMC7559691","BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is hitting many countries. It is hypothesized the epidemic is differentially progressing in different countries. AIM: To investigate how the COVID-19 epidemic is going on in different countries by analyzing representative countries. METHODS: The status of COVID-19 epidemic in over 60 most affected countries was characterized. The data of daily new cases of each country were collected from Worldometer. The data of daily tests for the United States, Italy, and South Korea were collected from the Website of One World Data. Levels of daily positive COVID-19 tests in the two most affected states of the United States (New York and New Jersey) were collected from the website of the COVID Tracking Project. Statistics were analyzed using Microcal Origin software with ANOVA algorithm, and significance level was set at a P value of 0.05. RESULTS: The COVID-19 epidemic was differentially progressing in different countries. Comparative analyses of daily new cases as of April 19, 2020 revealed that 61 most affected countries can be classified into four types: Downward (22), upward (20), static-phase (12), and uncertain ones (7). In particular, the 12 static-phase countries including the United States were characterized by largely constant numbers of daily new cases in the past over 14 d. Furthermore, these static-phase countries were overall significantly lower in testing density (P = 0.016) but higher in the level of positive COVID-19 tests than downward countries (P = 0.028). These findings suggested that the testing capacity in static-phase countries was lagging behind the spread of the outbreak, i.e., daily new cases (confirmed) were likely less than daily new infections and the remaining undocumented infections were thus still expanding, resulting in unstoppable epidemic. CONCLUSION: Increasing the testing capacity and/or reducing the COVID-19 transmission are urgently needed to stop the potentially unstoppable, severing crisis in static-phase countries.","COVID-19; Coronavirus; Pandemic; SARS-CoV-2; Testing density","","","Department of Orthopaedics, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China. College of Life Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China. Anxi AIER Eye Hospital (AIER EYE Hospital Group), Anxi 362400, Fujian Province, China. fu_zhifu@163.com.","en","Research Article","","","","","","",""
"Journal Article","Gapen M,Millar J,Blerina U,Sriram P","","Assessing the effectiveness of alternative measures to slow the spread of COVID-19 in the United States","Covid Economics","","2020","40","","46-75","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Ioannis_Laliotis/publication/343307261_CovidEconomics40/links/5f22b3e1a6fdcccc43995642/CovidEconomics40.pdf#page=51","","","","","","… Source: FRB Dallas, Barclays Research Source: Census Bureau, The COVID Tracking Project , Barclays Research 9 Additional information on the Dallas Fed Mobility and Engagement Index can be found at https://www. dallasfed. org/research/mei …","","","","","","","","","","","","",""
"Journal Article","Delen D,Eryarsoy E,Davazdahemami B","","No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","2","e19862","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-05-28","","","2369-2960","","http://dx.doi.org/10.2196/19862;https://www.ncbi.nlm.nih.gov/pubmed/32434145;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7257477;https://publichealth.jmir.org/2020/2/e19862/","10.2196/19862","32434145","","","PMC7257477","BACKGROUND: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values. RESULTS: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.","COVID-19; machine learning; pandemic; public health; social distancing","","","Center for Health Systems Innovation, Department of Management Science and Information Systems, Oklahoma State University, Tulsa, OK, United States. School of Management, Sabanci University, Istanbul, Turkey. Department of IT and Supply Chain Management, University of Wisconsin-Whitewater, Whitewater, WI, United States.","en","Research Article","","","","","","",""
"Journal Article","Oh J,Lee JK,Schwarz D,Ratcliffe HL,Markuns JF,Hirschhorn LR","","National Response to COVID-19 in the Republic of Korea and Lessons Learned for Other Countries","Health Syst Reform","Health systems and reform","2020","6","1","e1753464","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-01-01","","","2328-8620","","http://dx.doi.org/10.1080/23288604.2020.1753464;https://www.ncbi.nlm.nih.gov/pubmed/32347772;https://www.tandfonline.com/doi/abs/10.1080/23288604.2020.1753464;https://www.tandfonline.com/doi/pdf/10.1080/23288604.2020.1753464","10.1080/23288604.2020.1753464","32347772","","","","In the first two months of the COVID-19 pandemic, the Republic of Korea (South Korea) had the second highest number of cases globally yet was able to dramatically lower the incidence of new cases and sustain a low mortality rate, making it a promising example of strong national response. We describe the main strategies undertaken and selected facilitators and challenges in order to identify transferable lessons for other countries working to control the spread and impact of COVID-19. Identified strategies included early recognition of the threat and rapid activation of national response protocols led by national leadership; rapid establishment of diagnostic capacity; scale-up of measures for preventing community transmission; and redesigning the triage and treatment systems, mobilizing the necessary resources for clinical care. Facilitators included existing hospital capacity, the epidemiology of the COVID-19 outbreak, and strong national leadership despite political changes and population sensitization due to the 2015 Middle East respiratory syndrome-related coronavirus (MERS-CoV) epidemic. Challenges included sustaining adequate human resources and supplies in high-caseload areas. Key recommendations include (1) recognize the problem, (2) establish diagnostic capacity, (3) implement aggressive measures to prevent community transmission, (4) redesign and reallocate clinical resources for the new environment, and (5) work to limit economic impact through and while prioritizing controlling the spread and impact of COVID-19. South Korea's strategies to prevent, detect, and respond to the pandemic represent applicable knowledge that can be adopted by other countries and the global community facing the enormous COVID-19 challenges ahead.","COVID-19 pandemic; South Korea; health system reform; national response; triage and quarantine","","","Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea. Department of Social and Behavioral Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Department of Family Medicine, Seoul National University College of Medicine, Seoul, South Korea. Ariadne Labs, Brigham & Women's Hospital and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA. Division of Global Health Equity, Brigham & Women's Hospital, Boston, MA, USA. Global Health Collaborative, Department of Family Medicine, Boston University, Boston, MA, USA. Northwestern University Feinberg School of Medicine, Chicago, IL, USA.","en","Research Article","","","","","","",""
"Journal Article","Hu M,Roberts JD,Azevedo GP,Milner D","","The role of built and social environmental factors in Covid-19 transmission: A look at America’s capital city","Sustainable Cities and Society","","2020","","","102580","COVID Tracking Project","","","","Elsevier","","","","","2020","","","","","https://www.sciencedirect.com/science/article/pii/S2210670720307988","","","","","","JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …","","","","","","","","","","","","",""
"Journal Article","Le NK,Le AV,Parikh J,Brooks JP,Gardellini T,et al.","","Ecological and health infrastructure factors affecting the transmission and mortality of COVID-19","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-19504/latest.pdf","","","","","","… b48e9ecf6. Page 9. 9 12. The COVID Tracking Project 2020 [Available from: https://covidtracking. com/data/. 13. Central Intelligence Agency. The World Factbook [Available from: https://www.cia.gov/library/publications/the-world-factbook/fields/335rank.html. 14 …","","","","","","","","","","","","",""
"Journal Article","Wang G,Gu Z,Li X,Yu S,Kim M,Wang Y,et al.","","Comparing and Integrating US COVID-19 Daily Data from Multiple Sources: A County-Level Dataset with Local Characteristics","arXiv preprint arXiv","","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020","","","","","https://arxiv.org/abs/2006.01333;https://arxiv.org/pdf/2006.01333","","","","","","… We collect the COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, and compare the similarities and differences among them …","","","","","","","","","","","","",""
"Journal Article","Chetty R,Friedman JN,Hendren N,et al.","","Real-time economics: A new platform to track the impacts of COVID-19 on people, businesses, and communities using private sector data","NBER Working","","2020","","","","COVID Tracking Project","","","","opportunityinsights.org","","","","","2020","","","","","https://opportunityinsights.org/wp-content/uploads/2020/06/Short_Covid_Paper.pdf","","","","","","… national level.9 We also report daily state-level data on the number of tests performed per day per 100,000 people from the COVID Tracking Project .10 For each measure - cases, deaths, and tests – we report two daily series per …","","","","","","","","","","","","",""
"Journal Article","Manski CF,Molinari F","","Estimating the COVID-19 infection rate: Anatomy of an inference problem","J. Econom.","Journal of econometrics","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020-05-06","","","0304-4076","","http://dx.doi.org/10.1016/j.jeconom.2020.04.041;https://www.ncbi.nlm.nih.gov/pubmed/32377030;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200382;https://linkinghub.elsevier.com/retrieve/pii/S0304-4076(20)30167-6;https://www.sciencedirect.com/science/article/pii/S0304407620301676?casa_token=-ty5E6boQlIAAAAA:VUNOy9IZmoDaSRAckmNC9PJ8bPN_JzJMpGwmYG5PGcPOkrbas9-xMF7FETVg5Re0PMrqUddcPQ","10.1016/j.jeconom.2020.04.041","32377030","","","PMC7200382","As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Illinois, New York, and Italy is substantially lower than reported.","Epidemiology; Missing data; Novel coronavirus; Partial identification","","","Department of Economics and Institute for Policy Research, Northwestern University 2211 Campus Drive, Evanston, IL 60208-2600, USA. Department of Economics, Cornell University Uris Hall, Ithaca, NY 14853, USA.","en","Research Article","","","","","","",""
"Review","Chen CF,Zarazua de Rubens G,Xu X,Li J","","Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19","Energy Res Soc Sci","Energy research & social science","2020","68","","101688","COVID Tracking Project","","","","Elsevier","","","","","2020-10","","","2214-6296","","http://dx.doi.org/10.1016/j.erss.2020.101688;https://www.ncbi.nlm.nih.gov/pubmed/32839705;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341977;https://linkinghub.elsevier.com/retrieve/pii/S2214-6296(20)30263-2;https://www.sciencedirect.com/science/article/pii/S2214629620302632;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341977/","10.1016/j.erss.2020.101688","32839705","","","PMC7341977","This study explores the dynamics of energy use patterns, climate change issues and the relationship between social-psychological factors, with residents' acceptance of and willingness to pay (WTP) for home energy management systems (HEMS) during the COVID-19 pandemic in New York. The results of our survey suggest that there were no longer morning or evening usage peaks on weekdays, and a significant portion of respondents are experiencing higher or much higher electricity use than average. Most residents' perception of climate change issues during COVID-19 remained unchanged. Attitude, perceived behavioral control, and social norms are overall the strongest predictors of adoption intention and WTP for HEMS. Regarding WTP for specific well-being features, attitude was the strongest positive predictor of telemedical and home security features, and social norms are the strongest positive predictor of elderly assistance and job search. Technology anxiety, surprisingly, positively influences WTP for the well-being features. Trust in utilities is not related to adoption intention, but is positively associated with WTP for the well-being features. Although cybersecurity concerns are positively associated with HEMS adoption intention for energy and well-being features, this relationship is not significant in WTP. Residents who had moderate perceived risk of getting COVID-19 are willing to pay more than the high- and low-risk groups. This paper addresses the interactions among technology attributes, and users' social-psychological and demographics factors. Additionally, this study provides insights for further research in examining technology adoption and energy dynamics during times of crises, such as the COVID-19.","COVID-19; Climate change; Energy demand; Home Energy Management System (HEMS); Social norms; Willingness to pay","","","Center for Ultra-wide-area Resilient Electrical Energy Transmission Networks (CURENT), Department of Electrical Engineering and Computer Science, University of Tennessee, USA. Center for Energy Technologies, Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, DK-7400 Herning, Denmark. Department of Sociology, University of Tennessee, USA.","en","Review","","","","","","",""
"Journal Article","Lee M","","We Must Act Now: Building Trust and Increasing Minority Participation in COVID-19 Clinical Trials","djph.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://djph.org/wp-content/uploads/2020/11/Lee_COVID-19-Racism-and-Trials.pdf","","","","","","… African Americans account for 13% of the US population, yet they account for approximately 22% of the coronavirus deaths for which racial or ethnic information was available as of August 12, 2020, according to The COVID Tracking Project .1 Research supports the fact that …","","","","","","","","","","","","",""
"Journal Article","Auger KA,Shah SS,Richardson T,Hartley D,Hall M,Warniment A,Timmons K,Bosse D,Ferris SA,Brady PW,Schondelmeyer AC,Thomson JE","","Association Between Statewide School Closure and COVID-19 Incidence and Mortality in the US","JAMA","JAMA: the journal of the American Medical Association","2020","324","9","859-870","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-09-01","","","0098-7484","1538-3598","http://dx.doi.org/10.1001/jama.2020.14348;https://www.ncbi.nlm.nih.gov/pubmed/32745200;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391181;https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2020.14348;https://jamanetwork.com/journals/jama/article-abstract/2769034;https://jamanetwork.com/journals/jama/articlepdf/2769034/jama_auger_2020_oi_200088_1598047378.85855.pdf","10.1001/jama.2020.14348","32745200","","","PMC7391181","Importance: In the US, states enacted nonpharmaceutical interventions, including school closure, to reduce the spread of coronavirus disease 2019 (COVID-19). All 50 states closed schools in March 2020 despite uncertainty if school closure would be effective. Objective: To determine if school closure and its timing were associated with decreased COVID-19 incidence and mortality. Design, Setting, and Participants: US population-based observational study conducted between March 9, 2020, and May 7, 2020, using interrupted time series analyses incorporating a lag period to allow for potential policy-associated changes to occur. To isolate the association of school closure with outcomes, state-level nonpharmaceutical interventions and attributes were included in negative binomial regression models. States were examined in quartiles based on state-level COVID-19 cumulative incidence per 100 000 residents at the time of school closure. Models were used to derive the estimated absolute differences between schools that closed and schools that remained open as well as the number of cases and deaths if states had closed schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile. Exposures: Closure of primary and secondary schools. Main Outcomes and Measures: COVID-19 daily incidence and mortality per 100 000 residents. Results: COVID-19 cumulative incidence in states at the time of school closure ranged from 0 to 14.75 cases per 100 000 population. School closure was associated with a significant decline in the incidence of COVID-19 (adjusted relative change per week, -62% [95% CI, -71% to -49%]) and mortality (adjusted relative change per week, -58% [95% CI, -68% to -46%]). Both of these associations were largest in states with low cumulative incidence of COVID-19 at the time of school closure. For example, states with the lowest incidence of COVID-19 had a -72% (95% CI, -79% to -62%) relative change in incidence compared with -49% (95% CI, -62% to -33%) for those states with the highest cumulative incidence. In a model derived from this analysis, it was estimated that closing schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile was associated with 128.7 fewer cases per 100 000 population over 26 days and with 1.5 fewer deaths per 100 000 population over 16 days. Conclusions and Relevance: Between March 9, 2020, and May 7, 2020, school closure in the US was temporally associated with decreased COVID-19 incidence and mortality; states that closed schools earlier, when cumulative incidence of COVID-19 was low, had the largest relative reduction in incidence and mortality. However, it remains possible that some of the reduction may have been related to other concurrent nonpharmaceutical interventions.","","","","Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio. Pediatric Research in Inpatient Settings Network, Cincinnati, Ohio. Division of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.","en","Research Article","","","","","","",""
"Journal Article","Rǎdulescu A","","Course of the first month of the COVID 19 outbreak in the New York State counties","PLoS One","PloS one","2020","15","9","e0238560","COVID Tracking Project","","","","journals.plos.org","","","","","2020-09-02","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0238560;https://www.ncbi.nlm.nih.gov/pubmed/32877453;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467259;https://dx.plos.org/10.1371/journal.pone.0238560;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238560","10.1371/journal.pone.0238560","32877453","","","PMC7467259","We illustrate and study the evolution of reported infections over the month of March in New York State as a whole, as well as in each individual county in the state. We identify piecewise exponential trends, and search for correlations between the timing and dynamics of these trends and statewide mandated measures on testing and social distancing. We conclude that the reports on April 1 may be dramatically under-representing the actual number of statewide infections, an idea which is supported by more recent retroactive estimates based on serological studies. A follow-up study is underway, reassessing data until June 1, using additional measures for validation and monitoring for effects of the PAUSE directive, and of the reopening timeline.","","","","Mathematics, SUNY New Paltz, New Paltz, NY, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Wu C,Wilkes R,Fairbrother M,Giordano G","","Social Capital, Trust, and State Coronavirus Testing","Contexts ","Contexts ","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","1536-5042","","https://www.researchgate.net/profile/Cary_Wu2/publication/340284242_Social_Capital_Trust_and_State_Coronavirus_Testing/links/5e822837299bf1a91b8cf7ff/Social-Capital-Trust-and-State-Coronavirus-Testing.pdf","","","","","","… stopped publishing testing data. Some of the most comprehensive data are, however, available from The COVID Tracking Project . This project provides state and district … cases (positive) March 20, 2020. Note: Testing data from The COVID Tracking Project . Page 3 …","","","","","","","","","","","","",""
"Journal Article","Comba JL","","Data Visualization for the Understanding of COVID-19","Computing in Science Engineering","","2020","22","6","81-86","COVID Tracking Project","","","","ieeexplore.ieee.org","","","","","2020-11","","","1558-366X","","http://dx.doi.org/10.1109/MCSE.2020.3019834;https://ieeexplore.ieee.org/abstract/document/9222822/?casa_token=8-pGSZge4dwAAAAA:tZ4zIeMrJgDb1qtn6rXG_XMb2AQ3MDJFFusqBA-3XqPfn-5LqI-OTfNEZ9Up8Uw_mrqhBpXCFA;https://ieeexplore.ieee.org/iel7/5992/9222575/09222822.pdf?casa_token=RyNInJdzzJ0AAAAA:zyeVmpP3vWt0S0T5rvUVxOfWQQHJvE5sJRPEI5WOH38SXjc4P6ga3DVvNmkA28cdPvczKHvfdA","10.1109/MCSE.2020.3019834","","","","","Visualization techniques have been front-and-center in the efforts to communicate the science around COVID-19 to the very broad audience of policy makers, scientists, healthcare providers, and the general public. In this article, I summarize and illustrate with examples how visualization can help understand different aspects of the pandemic.","data visualisation;diseases;epidemics;health care;medical computing;data visualization;COVID-19;policy makers;healthcare providers;pandemic;COVID-19;Data visualization;Pandemics;Time series analysis","","","","","","","","","","","",""
"Journal Article","Baker SR,Bloom N,Davis SJ,Kost K,Sammon M,Viratyosin T","","The Unprecedented Stock Market Reaction to COVID-19","Rev Asset Pric Stud","The Review of Asset Pricing Studies","2020","10","4","742-758","COVID Tracking Project","","","","Oxford Academic","","","","","2020-07-18","2020-12-08","","2045-9920","","https://academic.oup.com/raps/article-abstract/10/4/742/5873533;http://dx.doi.org/10.1093/rapstu/raaa008;https://academic.oup.com/raps/article/10/4/742/5873533?casa_token=_M-siDzLqgAAAAAA:NwHiG9dCJn6v5ltekz8pK6EgXE0DUWE_Z_T7ArOaIqaIRPnXQp_32VktbEQDg1e5Uzb_LvwetzJE","10.1093/rapstu/raaa008","","","","","Abstract. No previous infectious disease outbreak, including the Spanish Flu, has affected the stock market as forcefully as the COVID-19 pandemic. In fact, pre","","","","","en","","","","","","","",""
"Journal Article","Ravichandran K,Anbazhagan S,Agri H,et al.","","Global status of COVID-19 diagnosis: an overview","J Pure Appl","","2020","","","","COVID Tracking Project","","","","pdfs.semanticscholar.org","","","","","2020","","","","","https://pdfs.semanticscholar.org/c6d8/3b2f14fcce02d94d00c1a20ae220abbdbf18.pdf","","","","","","… testing at the right time55. The COVID Tracking Project , which is a volunteer organization launched by The Atlanfic, collects and issues the comprehensive testing data available for US states and territories56. The policy is to …","","","","","","","","","","","","",""
"Journal Article","Harris JE","","COVID-19 Case Mortality Rates Continue to Decline in Florida","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.03.20167338v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/04/2020.08.03.20167338.full.pdf","","","","","","… Study Group. 2020. \"Decreased in-hospital mortality in patients with COVID-19 pneumonia.\" Pathog Glob Health:1-2. doi: 10.1080/20477724.2020.1785782. COVID Tracking Project . 2020. Our Data: Florida. https://covidtracking.com/data/state/florida: August 3, 2020 …","","","","","","","","","","","","",""
"Journal Article","Pichler S,Wen K,Ziebarth NR","","COVID-19 Emergency Sick Leave Has Helped Flatten The Curve In The United States: Study examines the impact of emergency sick leave on the spread of COVID …","Health Aff.","Health affairs","2020","","","","COVID Tracking Project","","","","healthaffairs.org","","","","","2020","","","0092-8577","","https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2020.00863?casa_token=KcRzCmp9H98AAAAA:OnMc5MBV9m0QmP-prklJG7EiZm43y_4NhexlMjcxIhaqxNBXhmBI0ykVu1LbDf23Yn_UFyts1Kc;https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2020.00863?casa_token=x0NRow63uVsAAAAA:do-BRWXMakXDbwgsYuCJsmmOOaVjwNtCfkxy7h_seu4YKehXqDvUf56Rn0alaRE9IWdoCKig074","","","","","","… 19. Study Data And Methods. Data Sources. We compile our main dataset from various sources. First, we include the number of new reported COVID-19 cases for all US states from the COVID Tracking Project at the daily level …","","","","","","","","","","","","",""
"Preprint Manuscript","Béland LP,Brodeur A,Wright T","","The Short-Term Economic Consequences of Covid-19: Exposure to Disease, Remote Work and Government Response","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-27","2020-12-08","","","","https://papers.ssrn.com/abstract=3584922;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3584922;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3584922;https://www.econstor.eu/bitstream/10419/216471/1/dp13159.pdf","","","","","","In this ongoing project, we examine the short-term consequences of COVID-19 on employment and wages in the United States. Guided by a pre-analysis plan, we document the impact of COVID-19 at the national-level using a simple difference and test whether states with relatively more confirmed cases/deaths were more affected. Our findings suggest that COVID-19 increased the unemployment rate, decreased hours of work and labor force participation and had no significant impacts on wages. The negative impacts on labor market outcomes are larger for men, younger workers, Hispanics and less-educated workers. This suggest that COVID-19 increases labor market inequalities. We also investigate whether the economic consequences of this pandemic were larger for certain occupations. We built three indexes using ACS and O*NET data: workers relatively more exposed to disease, workers that work with proximity to coworkers and workers who can easily work remotely. Our estimates suggest that individuals in occupations working in proximity to others are more affected while occupations able to work remotely are less affected. We also find that occupations classifed as more exposed to disease are less affected, possibly due to the large number of essential workers in these occupations.","COVID-19, unemployment, wages, remote work, exposure to disease","","","","","","","","","","","",""
"Journal Article","Douglass RW,Scherer TL,Gartzke E","","The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends","Peace Economics, Peace Science and Public Policy","","2020","26","3","20200053","COVID Tracking Project","","","","De Gruyter","Berlin, Boston","","","","2020","","","","","https://www.degruyter.com/view/journals/peps/26/3/article-20200053.xml;http://dx.doi.org/10.1515/peps-2020-0053","10.1515/peps-2020-0053","","","","","One of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.","","","","","","","","","","","","",""
"Journal Article","Nowotny KM,Cloud D,Wurcel AG,et al.","","Disparities in COVID-19 Related Mortality in US Prisons and the General Population","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.17.20183392v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/18/2020.09.17.20183392.full.pdf","","","","","","… Additional national data were pooled from a variety of sources including COVID Tracking Project 5 , Centers for Disease Control and Prevention 6 , American … 2020: https://www.nytimes.com/ interactive/2020/us/coronavirus-us-cases.html#clusters. 5. Covid Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Ndeffo-Mbah ML","","Using test positivity and reported case rates to estimate state-level COVID-19 prevalence in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.07.20208504v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/12/2020.10.07.20208504.full.pdf","","","","","","… log / log , log 6 The positivity , and the testing rate are calculated from data obtained from the COVID Tracking Project 13, averaged of the past days … 13. The COVID Tracking Project | The COVID Tracking Project . https://covidtracking.com/. Accessed May 18, 2020. 14 …","","","","","","","","","","","","",""
"Journal Article","Fowler JH,Hill SJ,Levin R,Obradovich N","","The effect of stay-at-home orders on COVID-19 infections in the United States","arXiv preprint arXiv","","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020","","","","","https://arxiv.org/abs/2004.06098;https://arxiv.org/pdf/2004.06098","","","","","","… This information is not currently available for each county, but it is available for each state by date from the COVID Tracking Project ​19​ for about 80% of our observations. We merged this data with information about stay-at-home orders and confirmed cases …","","","","","","","","","","","","",""
"Journal Article","Hamidi S,Sabouri S,Ewing R","","Does density aggravate the COVID-19 pandemic? Early findings and lessons for planners","J. Am. Plann. Assoc.","Journal of the American Planning Association. American Planning Association","2020","","","","COVID Tracking Project","","","","Taylor & Francis","","","","","2020","","","0194-4363","","https://www.tandfonline.com/doi/abs/10.1080/01944363.2020.1777891;https://www.tandfonline.com/doi/pdf/10.1080/01944363.2020.1777891","","","","","","… We collected the total number of people tested for COVID-19 in each state from the COVID Tracking Project (CTP, 2020) website and computed the testing rate per 10,000 population. Note that these data are only available at the state level …","","","","","","","","","","","","",""
"Journal Article","Kolker E,et al.","","Monte Carlo Simulations to Democratize COVID-19 Policies: Predicting and monitoring COVID-19 fatalities at the country, province, state, county, and city levels to …","Genet. Eng. ","Genetic engineering","2020","","","","COVID Tracking Project","","","","liebertpub.com","","","","","2020","","","0196-3716","","https://www.liebertpub.com/doi/abs/10.1089/gen.40.08.15?casa_token=OMH2yIh3ZGwAAAAA:ReZ5VGDJuLIr5X30mmg90urrn3zrugtbNlL2OTDalE4s-wy_9YYMt4SwL0jie_2oMl7ZU_klPMw8DA;https://www.liebertpub.com/doi/pdfplus/10.1089/gen.40.08.15?casa_token=3dLahylugVYAAAAA:IdaB7zh4ktj5QstuRRmDP6j44a7GTT5AMuycyI7nWI1wMoJnl89a3A_DpTg98vlWs-Pge4OYNO6wWg","","","","","","… Range of fatality forecast from Monte Carlo simulations. Data derived from the COVID Tracking Project 3 were visualized with Tableau … Data derived from the COVID Tracking Project 3 were visualized with Tableau. Accuracy, limitations, and future developments …","","","","","","","","","","","","",""
"Journal Article","Zohner YEM,Morris JS","","COVID-Track: World and USA SARS-COV-2 Testing and COVID-19 Tracking","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-41444/latest.pdf","","","","","","… Different jurisdictions record and report data differently, and it is challenging to compile this complex data in a timely manner. Two sources have worked to provide accurate and reliable data: The COVID Tracking Project at … The COVID Tracking project has aggregated and …","","","","","","","","","","","","",""
"Report","Amuedo-Dorantes C,Kaushal N,Muchow AN","","Is the Cure Worse than the Disease? County-Level Evidence from the COVID-19 Pandemic in the United States","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27759","nber.org","","","","","2020-08-31","2020-12-08","","","","https://www.nber.org/papers/w27759;http://dx.doi.org/10.3386/w27759;https://www.nber.org/system/files/working_papers/w27759/w27759.pdf","10.3386/w27759","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Favero N","","Adjusting confirmed COVID-19 case counts for testing volume","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.26.20141135v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/28/2020.06.26.20141135.full.pdf","","","","","","… this analysis. The COVID Tracking Project maintains a publicly available dataset of daily state reports of COVID-19 test results.11 Weekly measures of the number of newly confirmed … available). The COVID Tracking Project primarily tracks the results of viral tests, although …","","","","","","","","","","","","",""
"Review","Xu D,Starr MR,Boucher N,Chiang A,Yonekawa Y,Klufas MA,Khan MA,Cohen MN,Mehta S,Kuriyan AE","","Real-world vitreoretinal practice patterns during the 2020 COVID-19 pandemic: a nationwide, aggregated health record analysis","Curr. Opin. Ophthalmol.","Current opinion in ophthalmology","2020","31","5","427-434","COVID Tracking Project","","","","journals.lww.com","","","","","2020-09","","","1040-8738","1531-7021","http://dx.doi.org/10.1097/ICU.0000000000000692;https://www.ncbi.nlm.nih.gov/pubmed/32740067;https://doi.org/10.1097/ICU.0000000000000692;https://journals.lww.com/co-ophthalmology/Fulltext/2020/09000/Real_world_vitreoretinal_practice_patterns_during.19.aspx?context=LatestArticles","10.1097/ICU.0000000000000692","32740067","","","","PURPOSE OF REVIEW: The COVID-19 pandemic has posed an unprecedented challenge to the healthcare community. To reduce disease transmission, national regulatory agencies temporarily recommended curtailment of all nonurgent office visits and elective surgeries in March 2020, including vitreoretinal outpatient care in the USA. The effect of these guidelines on utilization of vitreoretinal care has not been explored to date. RECENT FINDINGS: Retinal outpatient visits, new patient visits, intravitreal antivascular endothelial growth factor injections and in-office multimodal retinal imaging has seen a significant decline in utilization in the early phase of the pandemic. Intravitreal injections were performed at a comparatively higher rate than office visits. Utilization appeared to steadily increase in April 2020. Telemedicine visits, enabled by new national legislation for all areas of medicine, have been adopted to a modest degree by the retina community. SUMMARY: In-office retinal care declined in response to the COVID-19 pandemic and national regulatory guidelines limiting nonurgent care. These trends in practice patterns and care utilization may be of interest to vitreoretinal providers and all ophthalmologists at large.","","","","Retina Service, Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania. Vestrum Health LLC, Naperville, Illinois, USA.","en","Review","","","","","","",""
"Journal Article","Chiu WA,Fischer R,Ndeffo-Mbah ML","","State-level impact of social distancing and testing on COVID-19 in the United States","Res Sq","Research square","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-07-07","","","","","http://dx.doi.org/10.21203/rs.3.rs-40364/v1;https://www.ncbi.nlm.nih.gov/pubmed/32702727;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362894;https://doi.org/10.21203/rs.3.rs-40364/v1;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362894/","10.21203/rs.3.rs-40364/v1","32702727","","","PMC7362894","Social distancing measures have been implemented in the United States (US) since March 2020, to mitigate the spread of SARS-CoV-2, the causative agent of COVID-19. However, by mid-May most states began relaxing these measures to support the resumption of economic activity, even as disease incidence continued to increase in many states. To evaluate the impact of relaxing social distancing restrictions on COVID-19 dynamics and control in the US, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths from March to June 20th, 2020, using Bayesian methods. We used this model to evaluate the impact of reopening, social distancing, testing, contact tracing, and case isolation on the COVID-19 epidemic in each state. We found that using stay-at-home orders, most states were able to curtail their COVID-19 epidemic curve by reducing and achieving an effective reproductive number below 1. But by June 20th, 2020, only 19 states and the District of Columbia were on track to curtail their epidemic curve with a 75% confidence, at current levels of reopening. Of the remaining 31 states, 24 may have to double their current testing and/or contact tracing rate to curtail their epidemic curve, and seven need to further restrict social contact by 25% in addition to doubling their testing and contact tracing rates. When social distancing restrictions are being eased, greater state-level testing and contact tracing capacity remains paramount for mitigating the risk of large-scale increases in cases and deaths.","Bayesian analysis; COVID-19; contact tracing; mathematical modeling; social distancing; testing","","","Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77845. Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, TX 77845.","en","Research Article","","","","","","",""
"Journal Article","Post LA,Issa TZ,Boctor MJ,Moss CB,Murphy RL,Ison MG,Achenbach CJ,Resnick D,Singh LN,White J,Faber JMM,Culler K,Brandt CA,Oehmke JF","","Dynamic Public Health Surveillance to Track and Mitigate the US COVID-19 Epidemic: Longitudinal Trend Analysis Study","J. Med. Internet Res.","Journal of medical Internet research","2020","22","12","e24286","COVID Tracking Project","","","","jmir.org","","","","","2020-12-03","","","1439-4456","1438-8871","http://dx.doi.org/10.2196/24286;https://www.ncbi.nlm.nih.gov/pubmed/33216726;https://www.jmir.org/2020/12/e24286/","10.2196/24286","33216726","","","","BACKGROUND: The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths worldwide. Without a coordinated national public health plan informed by surveillance with actionable metrics, the United States has been ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance has incomplete ascertainment and is limited by the use of standard surveillance metrics. Although many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic. OBJECTIVE: The aim of this study is to develop dynamic metrics for public health surveillance that can inform worldwide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision making and to identify where and when corrective action is required to prevent outbreaks. METHODS: Using a longitudinal trend analysis study design, we extracted COVID-19 data from global public health registries. We used an empirical difference equation to measure daily case numbers for our use case in 50 US states and the District of Colombia as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Examination of the United States and state data demonstrated that most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk, and persistence. Larger US states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic, and other states have alarming rates of speed, acceleration, jerk, and 7-day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the United States enters its third wave of COVID-19, all 50 states and the District of Colombia have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota), and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) new infections per 100,000 people. CONCLUSIONS: Standard surveillance techniques such as daily and cumulative infections and deaths are helpful but only provide a static view of what has already occurred in the pandemic and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as signals when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allow us to zero in on where transmissions are increasing, meaning corrective action can be applied with precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary while preserving the economy in other US areas.","Arellano-Bond estimator; COVID-19; COVID-19 acceleration; COVID-19 jerk; COVID-19 persistence; COVID-19 speed; COVID-19 transmission acceleration; US COVID-19; US COVID-19 surveillance system; US COVID-19 transmission speed; US SARS-CoV-2; United States econometrics; United States public health surveillance; dynamic panel data; generalized method of the moments; global COVID-19 surveillance; surveillance metrics","","","Buehler Center for Health Policy & Economics and Departments of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States. Center for Global Communicable Diseases, Institute for Global Health, Northwestern University, Chicago, IL, United States. Divsion of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. International Food Policy Research Institute, Washington, DC, United States. Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, CT, United States.","en","Research Article","","","","","","",""
"Journal Article","Haratian A,Fazelinia H,Maleki Z,Wang H,Lewis M,et al.","","Dataset of COVID-19 outbreak and potential predictive features in the USA","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.16.20214098v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/20/2020.10.16.20214098.full.pdf","","","","","","Page 1. 1 Dataset of COVID-19 outbreak and potential predictive features in the USA Authors Arezoo Haratian1, Hadi Fazelinia1, Zeinab Maleki1, Pouria Ramazi2,3, Hao Wang2, Mark A. Lewis2,4, Russell Greiner3,5, David Wishart3,4 …","","","","","","","","","","","","",""
"Journal Article","Lyu W,Wehby GL","","Comparison of Estimated Rates of Coronavirus Disease 2019 (COVID-19) in Border Counties in Iowa Without a Stay-at-Home Order and Border Counties in Illinois With a Stay-at-Home Order","JAMA Netw Open","JAMA network open","2020","3","5","e2011102","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-05-01","","","2574-3805","","http://dx.doi.org/10.1001/jamanetworkopen.2020.11102;https://www.ncbi.nlm.nih.gov/pubmed/32413112;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229521;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.11102;https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2766229;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2766229","10.1001/jamanetworkopen.2020.11102","32413112","","","PMC7229521","Importance: Iowa is 1 of 5 states in the US that have not issued a stay-at-home order during the coronavirus disease 2019 (COVID-19) pandemic. There is no empirical evidence on whether issuing a stay-at-home order in Iowa could have been associated with a reduced rate of COVID-19 infections in the state. Objective: To compare COVID-19 cases in border counties in Iowa, which did not issue a stay-at-home order, with cases in border counties in Illinois, which did issue a stay-at-home order. Design, Setting, and Participants: This cross-sectional study with a difference-in-differences design compared daily changes in COVID-19 cases per 10 000 residents in 8 Iowa counties bordering Illinois with those in the 7 Illinois counties bordering Iowa before and after Illinois issued a stay-at-home order on March 21, 2020. Additional sensitivity analyses were conducted to account for differences in timing of closing schools and nonessential businesses between the 2 states and differential trends in COVID-19 cases by county population density and poverty rates. Exposures: Issuing a stay-at-home order. Main Outcomes and Measures: Comparison of cumulative cases of COVID-19 per 10 000 residents in border counties in Iowa and Illinois. Results: The total populations were 462 445 in the Iowa border counties and 272 385 in the Illinois border counties. Population density was higher in the Iowa counties (114.2 people per square mile) than in the Illinois counties (78.2 people per square mile). Trends of cumulative COVID-19 cases per 10 000 residents for the Iowa and Illinois border counties were comparable before the Illinois stay-at-home order, which went into effect at 5:00 pm on March 21 (March 15 to March 21: 0.024 per 10 000 residents vs 0.026 per 10 000 residents). After that, cases increased more quickly in Iowa and more slowly in Illinois. Within 10, 20, and 30 days after the enactment of the stay-at-home order in Illinois, the difference in cases was -0.51 per 10 000 residents (SE, 0.09; 95% CI, -0.69 to -0.32; P < .001), -1.15 per 10 000 residents (SE, 0.49; 95% CI, -2.12 to -0.18; P = .02), and -4.71 per 10 000 residents (SE, 1.99; 95% CI, -8.64 to -0.78; P = .02), respectively. The estimates indicate excess cases in the border Iowa counties by as many as 217 cases after 1 month without a stay-at-home order. This estimate of excess cases represents 30.4% of the 716 total cases in those Iowa counties by that date. Sensitivity analyses addressing differences in timing of closing schools and nonessential businesses and differences in county population density and poverty rates between the 2 states supported these findings. Conclusions and Relevance: This cross-sectional study with a difference-in-differences design found an increase in estimated rates of COVID-19 cases per 10 000 residents in the border counties in Iowa compared with the border counties in Illinois following a stay-at-home order that was implemented in Illinois but not in Iowa.","","","","Department of Health Management and Policy, The University of Iowa, Iowa City. Department of Economics, The University of Iowa, Iowa City. Department of Preventive and Community Dentistry, The University of Iowa, Iowa City. Public Policy Center, The University of Iowa, Iowa City. National Bureau of Economic Research, Cambridge, Massachusetts.","en","Research Article","","","","","","",""
"Journal Article","Rodriguez PF","","Predicting Whom to Test is More Important Than More Tests-Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.01.20050393v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/06/2020.04.01.20050393.full.pdf","","","","","","… The ratio of 15-to-1 ratio of tests-per-new-cases at the peak is much higher than in US states like New York and California currently, and these and others states are trying to rapidly increase these number ( COVID Tracking Project ) …","","","","","","","","","","","","",""
"Journal Article","Guest JL,Del Rio C,Sanchez T","","The Three Steps Needed to End the COVID-19 Pandemic: Bold Public Health Leadership, Rapid Innovations, and Courageous Political Will","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","2","e19043","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-04-06","","","2369-2960","","http://dx.doi.org/10.2196/19043;https://www.ncbi.nlm.nih.gov/pubmed/32240972;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171587;https://publichealth.jmir.org/2020/2/e19043/;https://publichealth.jmir.org/2020/2/e19043/?fbclid=IwAR2nhoadEgt9U8uozAxeltzSx6kWgb5NC-TuFpaQbIq25TjYekJod6odLSs&utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_1","10.2196/19043","32240972","","","PMC7171587","The world is experiencing the expansive spread of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) in a global pandemic that is placing strain on health care, economic, and social systems. Commitment to implementing proven public health strategies will require bold public health leadership and courageous acts by politicians. Developing new innovative communication, mitigation, and health care approaches, particularly in the era of social media, is also clearly warranted. We believe that the best public health evidence must inform activities in three priority areas to stop this pandemic: (1) coordinated and consistent stay-at-home orders across multiple jurisdictions, including potential nationwide mandates; (2) rapid scale-up of SARS-CoV-2 testing; and (3) improved health care capacity to respond. This editorial outlines those areas, the rationale behind them, and the call for innovation and engagement of bold public health leadership to empower courageous political action to reduce the number of deaths during this pandemic.","COVID-19; SARS-CoV-2; coronavirus","","","Rollins School of Public Health at Emory University, Atlanta, GA, United States. Emory University School of Medicine, Atlanta, GA, United States. JMIR Public Health and Surveillance, JMIR Publications, Atlanta, GA, United States.","en","Research Article","","","","","","",""
"Journal Article","Dave D,Friedson AI,Matsuzawa K,et al.","","When do shelter‐in‐place orders fight COVID‐19 best? Policy heterogeneity across states and adoption time","Econ. Inq.","Economic inquiry","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","0095-2583","","https://onlinelibrary.wiley.com/doi/abs/10.1111/ecin.12944?casa_token=rpUAYDV23R8AAAAA:H-N0973iWEQHc2wgAl0hWrv0AyQk9zJmMG35FsaqnvVBRhhHva07NfsOPGhjE2f7XY0TeIxJ-R4acAQ;https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecin.12944?casa_token=nCWPze3AfPEAAAAA:dM8Xp-vBEF0u7y0p27s31SyyQjGkLR-RC-0YyvijqOrLkj5fnKtlaMnK0gqFSmILOg6HV3rLaAiCzjo","","","","","","… 19 testing may conflate the effects of SIPOs on COVID-19 cases. To address this issue, we measure data on testing from the COVID Tracking Project , compiled by The Atlantic and Related Sciences from 24 Sun and Abraham …","","","","","","","","","","","","",""
"Journal Article","Rivera R,Rosenbaum JE,Quispe W","","Excess mortality in the United States during the first three months of the COVID-19 pandemic","Epidemiol. Infect.","Epidemiology and infection","2020","148","","e264","COVID Tracking Project","","","","cambridge.org","","","","","2020-10-29","","","0950-2688","1469-4409","http://dx.doi.org/10.1017/S0950268820002617;https://www.ncbi.nlm.nih.gov/pubmed/33115546;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7653492;https://www.cambridge.org/core/product/identifier/S0950268820002617/type/journal_article;https://www.cambridge.org/core/journals/epidemiology-and-infection/article/excess-mortality-in-the-united-states-during-the-first-three-months-of-the-covid19-pandemic/F893889230439143C3E1F40E3D70400A;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F893889230439143C3E1F40E3D70400A/S0950268820002617a.pdf/excess_mortality_in_the_united_states_during_the_first_three_months_of_the_covid19_pandemic.pdf","10.1017/S0950268820002617","33115546","","","PMC7653492","Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013-127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338-6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095-3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646-6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341-3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562-7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170-16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538-39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125-6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.","COVID-19; epidemiology; excess deaths; infectious disease; natural disasters","","","College of Business, University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico. Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.","en","Research Article","","","","","","",""
"Journal Article","Rodriguez A,Tabassum A,Cui J,Xie J,Ho J,et al.","","DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.28.20203109v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/29/2020.09.28.20203109.full.pdf","","","","","","… T2. Inci- dence daily hospitalizations: Reported new hospitalizations for US states and the US overall. CDC does not fix a gold standard for this but we found the data are provided by the COVID Tracking Project (cov 2020c) to be the closest. Problem formulation …","","","","","","","","","","","","",""
"Preprint Manuscript","Hu G,Geng J","","Heterogeneity Learning for SIRS model: an Application to the COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-16","","","","","http://arxiv.org/abs/2007.08047","","","2007.08047","","","We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest coronavirus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains both number of clusters and cluster configurations. Specifically, our key idea is to formulates the SIRS model into a hierarchical form and assign the Mixture of Finite mixtures priors for heterogeneity learning. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze the state level COVID-19 data in U.S.","","","","","","","","arXiv","2007.08047","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Shankar K,Jeng W,Thomer A,et al.","","Data curation as collective action during COVID‐19","Journal of the","","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","","","https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/asi.24406?casa_token=CM46FuuG5b0AAAAA:6guQl0YIXD7rAbpzUsKPai3aW8qnzJ18ubBu8mZHtKQFNMbNuuqBnZSRsnHyjdPvXIE0SC0OClYE3Gw;https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/asi.24406?casa_token=fP2HSssC2jgAAAAA:Sf8TFj_XlOyGPs7JtMqsJr2esRSfwszaPkv9JaBRtKi_t_VFe3NalCYW3brXfxOgt09yfreXwAE0L3c","","","","","","… county‐level reports. The COVID Tracking Project housed by Atlantic Magazine is aggregating state‐level data in the United States and creating visualizations that depict the racial inequality in infection rates. This data curation …","","","","","","","","","","","","",""
"Journal Article","Xian Z,Saxena A,Javed Z,Jordan JE,Alkarawi S,et al.","","Racial and Ethnic Disparities in COVID-19 Infection and Mortality in the United States: A state-wise update","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.12.03.20243360v1","","","","","","… Abstract. Objectives: To evaluate COVID-19 infection and mortality in ethnic and racial sub-groups across all states in the United States. Methods: Publicly available data from The COVID Tracking Project at The Atlantic was accessed between 09/09/2020 and 09/14/2020 …","","","","","","","","","","","","",""
"Journal Article","Kogan NE,Clemente L,Liautaud P,Kaashoek J,Link NB,Nguyen AT,Lu FS,Huybers P,Resch B,Havas C,Petutschnig A,Davis J,Chinazzi M,Mustafa B,Hanage WP,Vespignani A,Santillana M","","An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time","ArXiv","ArXiv","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020-07-01","","","2331-8422","","https://www.ncbi.nlm.nih.gov/pubmed/32676518;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341456;https://arxiv.org/abs/2007.00756;https://arxiv.org/pdf/2007.00756.pdf?referringSource=articleShare&fbclid=IwAR1ZKjtlawhWQo6yjRSSc5xK_cuMiGNiIz1Yz1Aa5u6HxUYXFt4tcZxOrRk","","32676518","","","PMC7341456","Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.","","","","","en","Research Article","","","","","","",""
"Journal Article","Gross CP,Essien UR,Pasha S,Gross JR,Wang SY,Nunez-Smith M","","Racial and Ethnic Disparities in Population-Level Covid-19 Mortality","J. Gen. Intern. Med.","Journal of general internal medicine","2020","35","10","3097-3099","COVID Tracking Project","","","","medrxiv.org","","","","","2020-10","","","0884-8734","1525-1497","http://dx.doi.org/10.1007/s11606-020-06081-w;https://www.ncbi.nlm.nih.gov/pubmed/32754782;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402388;https://dx.doi.org/10.1007/s11606-020-06081-w;https://www.medrxiv.org/content/10.1101/2020.05.07.20094250v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/11/2020.05.07.20094250.full.pdf","10.1007/s11606-020-06081-w","32754782","","","PMC7402388","… 6. The Foundation for AIDS Research. COVID-19 Racial Disparities in US Counties. 2020; https://ehe.amfar.org/disparities. Accessed May 7, 2020. 7. The COVID Tracking Project . Most recent data. 2020; https://covidtracking.com/data, 2020. 8. US Census Bureau PD …","","","","National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, USA. cary.gross@yale.edu. Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT, USA. cary.gross@yale.edu. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA. cary.gross@yale.edu. Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale Cancer Center, New Haven, CT, USA. School of Arts & Sciences, Tufts University, Medford, MA, USA. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA. National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, USA. Equity Research and Innovation Center, Yale University, New Haven, CT, USA.","en","Research Article","","","","","","",""
"Journal Article","Wu SL,Mertens AN,Crider YS,Nguyen A,Pokpongkiat NN,Djajadi S,Seth A,Hsiang MS,Colford Jr JM,Reingold A,Arnold BF,Hubbard A,Benjamin-Chung J","","Substantial underestimation of SARS-CoV-2 infection in the United States","Nat. Commun.","Nature communications","2020","11","1","4507","COVID Tracking Project","","","","nature.com","","","","","2020-09-09","","","2041-1723","","http://dx.doi.org/10.1038/s41467-020-18272-4;https://www.ncbi.nlm.nih.gov/pubmed/32908126;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481226;https://doi.org/10.1038/s41467-020-18272-4;https://www.nature.com/articles/s41467-020-18272-4","10.1038/s41467-020-18272-4","32908126","","","PMC7481226","Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.","","","","Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA. Energy and Resources Group, University of California, 310 Barrows Hall, Berkeley, CA, 94720-3050, USA. Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390-9003, USA. Pandemic Community Response and Resilience Initiative, Global Health Group, University of California, San Francisco, Mission Hall, Box 1224, 550 16th Street, Third Floor, San Francisco, CA, 94158, USA. Department of Pediatrics, University of California, San Francisco 550 16th Street, Box 0110, San Francisco, CA, 94143, USA. Francis I. Proctor Foundation, University of California, San Francisco 95 Kirkham Street, San Francisco, CA, 94143, USA. Department of Ophthalmology, University of California, San Francisco 10 Koret Way, San Francisco, CA, 94143-0730, USA. Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA. jadebc@berkeley.edu.","en","Research Article","","","","","","",""
"Journal Article","Loeffler-Wirth H,Schmidt M,Binder H","","Covid-19 Transmission Trajectories—Monitoring the Pandemic in the Worldwide Context","Viruses","Viruses","2020","","","","COVID Tracking Project","","","","mdpi.com","","","","","2020","","","","","https://www.mdpi.com/1999-4915/12/7/777;https://www.mdpi.com/1999-4915/12/7/777/htm","","","","","","The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication …","","","","","","","","","","","","",""
"Preprint Manuscript","Yang T,Sha L,Li J,Hong P","","A Deep Learning Approach for COVID-19 Trend Prediction","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-09","","","","","http://arxiv.org/abs/2008.05644","","","2008.05644","","","In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States. We implemented the designed model using the United States to confirm cases and state demographic data and achieved promising trend prediction results. The model incorporates demographic information and epidemic time-series data through a Gated Recurrent Unit structure. The identification of dominating demographic factors is delivered in the end.","","","","","","","","arXiv","2008.05644","cs.CY","","","arXiv [cs.CY]"
"Journal Article","Thomas MD,Michaels EK,Darling-Hammond S,Nguyen TT,Glymour MM,Vittinghoff E","","Whites' County-Level Racial Bias, COVID-19 Rates, and Racial Inequities in the United States","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2020","17","22","","COVID Tracking Project","","","","mdpi.com","","","","","2020-11-23","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph17228695;https://www.ncbi.nlm.nih.gov/pubmed/33238526;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7700363;https://www.mdpi.com/resolver?pii=ijerph17228695;https://www.mdpi.com/1660-4601/17/22/8695;https://www.mdpi.com/1660-4601/17/22/8695/pdf","10.3390/ijerph17228695","33238526","","","PMC7700363","Mounting evidence reveals considerable racial inequities in coronavirus disease 2019 (COVID-19) outcomes in the United States (US). Area-level racial bias has been associated with multiple adverse health outcomes, but its association with COVID-19 is yet unexplored. Combining county-level data from Project Implicit on implicit and explicit anti-Black bias among non-Hispanic Whites, Johns Hopkins Coronavirus Resource Center, and The New York Times, we used adjusted linear regressions to estimate overall COVID-19 incidence and mortality rates through 01 July 2020, Black and White incidence rates through 28 May 2020, and Black-White incidence rate gaps on average area-level implicit and explicit racial bias. Across 2994 counties, the average COVID-19 mortality rate (standard deviation) was 1.7/10,000 people (3.3) and average cumulative COVID-19 incidence rate was 52.1/10,000 (77.2). Higher racial bias was associated with higher overall mortality rates (per 1 standard deviation higher implicit bias b = 0.65/10,000 (95% confidence interval: 0.39, 0.91); explicit bias b = 0.49/10,000 (0.27, 0.70)) and higher overall incidence (implicit bias b = 8.42/10,000 (4.64, 12.20); explicit bias b = 8.83/10,000 (5.32, 12.35)). In 957 counties with race-specific data, higher racial bias predicted higher White and Black incidence rates, and larger Black-White incidence rate gaps. Anti-Black bias among Whites predicts worse COVID-19 outcomes and greater inequities. Area-level interventions may ameliorate health inequities.","COVID-19; health inequities; racism and discrimination; social determinants of health","","","Department of Epidemiology and Biostatistics, School of Medicine, University of California, 550 16th St 2nd floor, San Francisco, CA 94158, USA. Department of Psychiatry, School of Medicine, University of California, 1001 Potrero Ave, San Francisco, CA 94110, USA. Division of Epidemiology, School of Public Health, University of California, 2121 Berkeley Way, Room 5302, Berkeley, CA 94720, USA. Goldman School of Public Policy, University of California, 2607 Hearst Ave, Berkeley, CA 94720, USA. Department of Family and Community Medicine, School of Medicine, University of California, 995 Potrero Ave, San Francisco, CA 94110, USA.","en","Research Article","","","","","","",""
"Journal Article","Gandal K,Gandal N","","US COVID-19 Deaths: The Weekend-Effect “Mystery”","Available at SSRN","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3654952","","","","","","… the difficulties.9 According to that article, “data from the COVID Tracking Project have been used by Johns Hopkins University, governors and members of Congress, and the White House.” Hence, we employ data from the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Chetty R,Friedman J,Hendren N,et al.","","The economic impacts of COVID-19: Evidence from a new public database built from private sector data","Opportunity","","2020","","","","COVID Tracking Project","","","","fairmodel.econ.yale.edu","","","","","2020","","","","","https://fairmodel.econ.yale.edu/ec438/chetty1.pdf","","","","","","Page 1. The Economic Impacts of COVID-19: Evidence from a New Public Database Built from Private Sector Data ∗ Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, and the Opportunity Insights Team † September 2020 …","","","","","","","","","","","","",""
"Preprint Manuscript","Wang L,Wang G,Gao L,Li X,Yu S,Kim M,Wang Y,Gu Z","","Spatiotemporal Dynamics, Nowcasting and Forecasting of COVID-19 in the United States","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-29","","","","","http://arxiv.org/abs/2004.14103","","","2004.14103","","","In response to the ongoing public health emergency of COVID-19, we investigate the disease dynamics to understand the spread of COVID-19 in the United States. In particular, we focus on the spatiotemporal dynamics of the disease, accounting for the control measures, environmental effects, socioeconomic factors, health service resources, and demographic conditions that vary from different counties. In the modeling of an epidemic, mathematical models are useful, however, pure mathematical modeling is deterministic, and only demonstrates the average behavior of the epidemic; thus, it is difficult to quantify the uncertainty. Instead, statistical models provide varieties of characterization of different types of errors. In this paper, we investigate the disease dynamics by working at the interface of theoretical models and empirical data by combining the advantages of mathematical and statistical models. We develop a novel nonparametric space-time disease transmission model for the epidemic data, and to study the spatial-temporal pattern in the spread of COVID-19 at the county level. The proposed methodology can be used to dissect the spatial structure and dynamics of spread, as well as to forecast how this outbreak may unfold through time and space in the future. To assess the uncertainty, projection bands are constructed from forecast paths obtained in bootstrap replications. A dashboard is established with multiple R shiny apps embedded to provide a 7-day forecast of the COVID-19 infection count and death count up to the county level, as well as a long-term projection of the next four months. The proposed method provides remarkably accurate short-term prediction results.","","","","","","","","arXiv","2004.14103","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Barhak J","","The Reference Model: An Initial Use Case for COVID-19","Cureus","Cureus","2020","12","7","e9455","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-07-29","","","2168-8184","","http://dx.doi.org/10.7759/cureus.9455;https://www.ncbi.nlm.nih.gov/pubmed/32760637;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392354;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392354/","10.7759/cureus.9455","32760637","","","PMC7392354","The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors.","disease modeling; estimation; high performance computing; machine learning; monte-carlo; optimization; population modeling","","","Software Developer and Computational Disease Modeler, Jacob Barhak - Sole Proprietor, Austin, USA.","en","Research Article","","","","","","",""
"Journal Article","Chin V,Samia NI,Marchant R,Rosen O,Ioannidis JPA,Tanner MA,Cripps S","","A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state","Eur. J. Epidemiol.","European journal of epidemiology","2020","35","8","733-742","COVID Tracking Project","","","","Springer","","","","","2020-08","","","0393-2990","1573-7284","http://dx.doi.org/10.1007/s10654-020-00669-6;https://www.ncbi.nlm.nih.gov/pubmed/32780189;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417851;https://doi.org/10.1007/s10654-020-00669-6;https://link.springer.com/article/10.1007/s10654-020-00669-6","10.1007/s10654-020-00669-6","32780189","","","PMC7417851","Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the \"ground truth\" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.","COVID-19; Hospital resource utilisation; Model evaluation; Uncertainty quantification","","","ARC Centre for Data Analytics for Resources and Environments, Sydney, Australia. School of Mathematics and Statistics, The University of Sydney, Sydney, Australia. Department of Statistics, Northwestern University, Chicago, USA. Department of Mathematical Sciences, University of Texas at El Paso, El Paso, USA. Stanford Prevention Research Center, Stanford, USA. Department of Medicine, Stanford University, Stanford, USA. Department of Epidemiology and Population Health, Stanford University, Stanford, USA. Department of Biomedical Data Sciences, Stanford University, Stanford, USA. Department of Statistics, Stanford University, Stanford, USA. Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, USA. ARC Centre for Data Analytics for Resources and Environments, Sydney, Australia. sally.cripps@sydney.edu.au. School of Mathematics and Statistics, The University of Sydney, Sydney, Australia. sally.cripps@sydney.edu.au.","en","Research Article","","","","","","",""
"Journal Article","Miron O,Yu KH,Wilf-Miron R,Davidovitch N","","Association of Mass Gatherings and COVID-19 Hospitalization","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.27.20220707v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/03/2020.10.27.20220707.full.pdf","","","","","","… who did not have a mass gathering rally since April 2020 and had daily hospitalizations counts in COVID - tracking project . These states were Alabama, Arkansas, Colorado, Connecticut, Georgia, Hawaii … 5. Data Download. In: The COVID Tracking Project [Internet]. [cited 12 Oct …","","","","","","","","","","","","",""
"Journal Article","White ER,Hébert-Dufresne L","","State-level variation of initial COVID-19 dynamics in the United States","PLoS One","PloS one","2020","15","10","e0240648","COVID Tracking Project","","","","journals.plos.org","","","","","2020-10-13","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0240648;https://www.ncbi.nlm.nih.gov/pubmed/33048967;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553297;https://dx.plos.org/10.1371/journal.pone.0240648;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240648","10.1371/journal.pone.0240648","33048967","","","PMC7553297","During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.","","","","Department of Biology, University of Vermont, Burlington, VT, United States of America. Gund Institute for Environment, University of Vermont, Burlington, VT, United States of America. Department of Computer Science, University of Vermont, Burlington, VT, United States of America. Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Chow CC,Chang JC,Gerkin RC,Vattikuti S","","Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-05-05","","","","","http://dx.doi.org/10.1101/2020.04.29.20083485;https://www.ncbi.nlm.nih.gov/pubmed/32510525;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239078;https://doi.org/10.1101/2020.04.29.20083485;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239078/","10.1101/2020.04.29.20083485","32510525","","","PMC7239078","Estimation of infectiousness and fatality of the SARS-CoV-2 virus in the COVID-19 global pandemic is complicated by ascertainment bias resulting from incomplete and non-representative samples of infected individuals. We developed a strategy for overcoming this bias to obtain more plausible estimates of the true values of key epidemiological variables. We fit mechanistic Bayesian latent-variable SIR models to confirmed COVID-19 cases, deaths, and recoveries, for all regions (countries and US states) independently. Bayesian averaging over models, we find that the raw infection incidence rate underestimates the true rate by a factor, the case ascertainment ratio CARt that depends upon region and time. At the regional onset of COVID-19, the predicted global median was 13 infections unreported for each case confirmed (CARt = 0.07 C.I. (0.02, 0.4)). As the infection spread, the median CARt rose to 9 unreported cases for every one diagnosed as of April 15, 2020 (CARt = 0.1 C.I. (0.02, 0.5)). We also estimate that the median global initial reproduction number R0 is 3.3 (C.I (1.5, 8.3)) and the total infection fatality rate near the onset is 0.17% (C.I. (0.05%, 0.9%)). However the time-dependent reproduction number Rt and infection fatality rate as of April 15 were 1.2 (C.I. (0.6, 2.5)) and 0.8% (C.I. (0.2%,4%)), respectively. We find that there is great variability between country- and state-level values. Our estimates are consistent with recent serological estimates of cumulative infections for the state of New York, but inconsistent with claims that very large fractions of the population have already been infected in most other regions. For most regions, our estimates imply a great deal of uncertainty about the current state and trajectory of the epidemic.","","","","Mathematical Biology Section, LBM, NIDDK, National Institutes of Health. Epidemiology and Biostatistics Section, Rehabilitation Medicine, Clinical Center, National Institutes of Health. mederrata. School of Life Sciences, Arizona State University.","en","Research Article","","","","","","",""
"Journal Article","Rajan K,Dhana K,Barnes LL,Aggarwal NT,Evans L,et al.","","Strong Effects of Population Density and Social Characteristics on Distribution of COVID-19 Infections in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.08.20073239v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/19/2020.05.08.20073239.full.pdf","","","","","","… Testing Statistics: The source for the total number of tests for COVID-19 came from the COVID tracking project [8] and the US Centers for Disease Control and Prevention (CDC) [9]. The COVID tracking project aggregates testing data by individual states and reports the number …","","","","","","","","","","","","",""
"Journal Article","Alexander D,Karger E,McFarland A","","Measuring the Relationship between Business Reopenings, Covid-19, and Consumer Behavior","Chicago Fed Letter","","2020","","","","COVID Tracking Project","","","","chicagofed.org","","","","","2020","","","","","https://www.chicagofed.org/~/media/publications/chicago-fed-letter/2020/cfl445-pdf.pdf","","","","","","… We begin by looking at patterns of health outcomes and mobility. We use data from the COVID Tracking Project describing new coronavirus cases and deaths. The COVID Tracking Project is a data collection effort conducted by the Atlantic Monthly Group …","","","","","","","","","","","","",""
"Journal Article","Noll NB,Aksamentov I,Druelle V,Badenhorst A,et al.","","COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.05.20091363v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/12/2020.05.05.20091363.full.pdf","","","","","","… These resources include the daily updated case counts by ECDC (European Centre for Disease Control, 2020), the US COVID tracking project (The COVID tracking project , 2020), other official governmental agen- cies from around the world, and data aggregated by vol- unteers …","","","","","","","","","","","","",""
"Journal Article","White J,Badger D","","In Order to Defeat COVID-19, the Federal Government Must Modernize Its Public Health Data","heritage.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.heritage.org/sites/default/files/2020-09/BG3527_0.pdf","","","","","","… Similar data are provided by the COVID Tracking Project , a data dashboard set up a few months ago by The Atlantic magazine to inform the public about relevant corona- virus developments. 2 Universities and other private entities have established COVID-19 tracking systems …","","","","","","","","","","","","",""
"Preprint Manuscript","Baccini L,Brodeur A","","Explaining Governors' Response to the Covid-19 Pandemic in the United States","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-20","2020-12-08","","","","https://papers.ssrn.com/abstract=3579229;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3579229;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3579229;https://www.econstor.eu/bitstream/10419/216449/1/dp13137.pdf","","","","","","What is the response of US governors to the COVID-19 pandemic? In this research note, we explore the determinants of implementing stay-at-home orders, focusing on governors' characteristics. In our most conservative estimate, being a Democratic governor increases the probability of implementing a stay-at-home order by more than 50 percent. Moreover, we find that the probability of implementing a statewide stay-at-home order is about 40 percent more likely for governors without a term limit than governors with a term limit. We also find that Democratic governors and governors without a term limit are significantly faster to adopt statewide orders than Republican governors and governors with a term limit. There is evidence of politics as usual in these unusual times.","COVID-19, pandemic, ideology, governors, United States","","","","","","","","","","","",""
"Journal Article","Sehra ST,George M,Wiebe DJ,Fundin S,et al.","","Cell Phone Activity in Categories of Places and Associations With Growth in Cases of COVID-19 in the US","JAMA Intern. Med.","JAMA internal medicine","2020","","","","COVID Tracking Project","","","","jamanetwork.com","","","","","2020","","","2168-6106","","https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2769771;https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/2769771/jamainternal_sehra_2020_oi_200068_1597765069.12142.pdf","","","","","","This cohort study examines the association between cell phone location and the rate of change in new COVID-19 cases by county across the US.","","","","","","","","","","","","",""
"Preprint Manuscript","She Z,Wang Z,Ayer T,Toumi A,Chhatwal J","","Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-31","","","","","http://arxiv.org/abs/2011.01219","","","2011.01219","","","Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accuracy vs. speed. In particular, while estimation accuracy improves with longer fitting windows, speed degrades. This paper presents a machine learning framework to balance this tradeoff using generalized random forests (GRF), and applies it to detect county level COVID-19 outbreaks. This algorithm chooses an adaptive fitting window size for each county based on relevant features affecting the disease spread, such as changes in social distancing policies. Experiment results show that our method outperforms any non-adaptive window size choices in 7-day ahead COVID-19 outbreak case number predictions.","","","","","","","","arXiv","2011.01219","cs.LG","","","arXiv [cs.LG]"
"Journal Article","Sananès N,Lodi M,Koch A,Lecointre L,Sananès A,Lefebvre N,Debry C","","3D-printed simulator for nasopharyngeal swab collection for COVID-19","Eur. Arch. Otorhinolaryngol.","European archives of oto-rhino-laryngology: official journal of the European Federation of Oto-Rhino-Laryngological Societies : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery","2020","","","","COVID Tracking Project","","","","Springer","","","","","2020-11-06","","","0937-4477","1434-4726","http://dx.doi.org/10.1007/s00405-020-06454-1;https://www.ncbi.nlm.nih.gov/pubmed/33156390;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645909;https://dx.doi.org/10.1007/s00405-020-06454-1;https://link.springer.com/article/10.1007/s00405-020-06454-1","10.1007/s00405-020-06454-1","33156390","","","PMC7645909","INTRODUCTION: Testing for COVID-19 is a cornerstone of pandemic control. If conducted inappropriately, nasopharyngeal swab collection can be painful and preanalytical sample collection errors may lead to false negative results. Our objective was to develop a realistic and easily available synthetic simulator for nasopharyngeal swab collection. MATERIALS AND METHODS: The nasopharyngeal swab collection simulator was designed through different development steps: segmentation, computer-aided design (CAD), and 3D printing. The model was 3D printed using PolyJet technology, which allows multi-material printing using hard and soft materials. RESULTS: The simulator splits in the parasagittal plane close to the septum to allow better visualization and understanding of nasal cavity landmarks. The model is able to simulate the softness and texture of different structural elements. The simulator allows the user to conduct realistic nasopharyngeal swab collection. A colored pad on the posterior wall of the nasopharynx provides real-time feedback to the user. The simulator also permits incorrect swab insertion, which is of obvious benefit from a training perspective. Comprehensive 3D files for printing and full instructions for manufacturing the simulator is freely available online via an open access link. CONCLUSION: In the context of the COVID-19 pandemic, we developed a nasopharyngeal swab collection simulator which can be produced by 3D printing via an open access link, which offers complete operating instructions.","COVID-19; Coronavirus; Nasopharyngeal swab collection; Simulator; Training","","","Obstetrics Department, Strasbourg University Hospital, Strasbourg, France. nicolas.sananes@chru-strasbourg.fr. INSERM UMR-S 1121 'Biomaterials and Bioengineering', Strasbourg University, Strasbourg, France. nicolas.sananes@chru-strasbourg.fr. Obstetrics Department, Strasbourg University Hospital, Strasbourg, France. CNRS UMR7104, INSERM U964, Institute of Genetics and Molecular and Cellular Biology (IGBMC), Illkirch-Graffenstaden, France. I-Cube UMR 7357, laboratoire des Sciences de l'ingénieur, de l'informatique et de l'imagerie, Strasbourg University, Strasbourg, France. Infectious Diseases Department, Strasbourg University Hospital, Strasbourg, France. INSERM UMR-S 1121 'Biomaterials and Bioengineering', Strasbourg University, Strasbourg, France. ENT Department, Strasbourg University Hospital, Strasbourg, France.","en","Research Article","","","","","","",""
"Journal Article","Cha V","","Asia's COVID-19 Lessons for the West: Public Goods, Privacy, and Social Tagging","Wash. Q.","The Washington quarterly","2020","","","","COVID Tracking Project","","","","Taylor & Francis","","","","","2020","","","0163-660X","","https://www.tandfonline.com/doi/full/10.1080/0163660X.2020.1770959?casa_token=b5uJvzmAi1IAAAAA:zermedTECOKvVfT56pAwHovfuCsBxn6d41xl2sLhbCyfpeNC-7-VUDYZeEY9L0vlYPJEk9gYYOh9","","","","","","… 12 “Most Recent Data,” The Covid Tracking Project (website), accessed May 2, 2020, https://covidtracking.com/data; Alyin Woodward and Shayanne Gal, “One Chart Shows How Many Coronavirus Tests per Capita Have Been Completed in Six Countries,” Business Insider …","","","","","","","","","","","","",""
"Journal Article","Roy S,Ghosh P","","Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking","PLoS One","PloS one","2020","15","10","e0241165","COVID Tracking Project","","","","journals.plos.org","","","","","2020-10-23","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0241165;https://www.ncbi.nlm.nih.gov/pubmed/33095811;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584177;https://dx.plos.org/10.1371/journal.pone.0241165;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241165","10.1371/journal.pone.0241165","33095811","","","PMC7584177","BACKGROUND: After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. METHODS: We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. FINDINGS: Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. INTERPRETATION: States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.","","","","Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America. Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Stear B,Hernandez K,Manian V,Taylor D,Conley C","","Estimating Unreported Deaths Associated with COVID-19","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.29.20184176v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/04/2020.08.29.20184176.full.pdf","","","","","","… https://doi.org/10.1101/2020.08.29.20184176 doi: medRxiv preprint Page 5. 5 recorded by four public pandemic tracking sites including the New York Times (7, 10), USA Facts (11), the Johns Hopkins CSSE dashboard (12) and the Atlantic COVID Tracking Project (13) …","","","","","","","","","","","","",""
"Journal Article","Kelly A,Martin AB,Teich ST","","Surveillance, intelligence, and intuition: Knowing when to re-engage in clinical operations","J. Dent. Educ.","Journal of dental education","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-06-07","","","0022-0337","1930-7837","http://dx.doi.org/10.1002/jdd.12248;https://www.ncbi.nlm.nih.gov/pubmed/32506445;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300848;https://doi.org/10.1002/jdd.12248;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300848/","10.1002/jdd.12248","32506445","","","PMC7300848","… Other colleges could replicate our approach with data available through the “The COVID tracking project .” 5 Testing data were not available at a geographic granularity smaller than state, although county‐level incidence rates as well as zip codes that make up the greater …","","","","Division of Population Oral Health, Department of Stomatology, Medical University of South Carolina, James B. Edwards College of Dental Medicine, Charleston, South Carolina, USA. Associate Dean for Clinical Affairs, Medical University of South Carolina, James B. Edwards College of Dental Medicine, Charleston, South Carolina, USA.","en","Research Article","","","","","","",""
"Journal Article","Beland LP,Brodeur A,Wright T","","Preregistration: The Short-Term Effect of COVID-19 on Employment and Wages","","","2020","","","","COVID Tracking Project","","","","osf.io","","","","","2020","","","","","https://osf.io/5y8n3/download","","","","","","… reports. We checked the accuracy of our data by comparing it to similar database created and maintained by other groups of researchers or institutions such as the COVID Tracking Project (https://covidtracking.com/). Current …","","","","","","","","","","","","",""
"Journal Article","Lu FS,Nguyen A,Link N,Santillana M","","Estimating the prevalence of COVID-19 in the United States: three complementary approaches","","","2020","","","","COVID Tracking Project","","","","dash.harvard.edu","","","","","2020","","","","","https://dash.harvard.edu/handle/1/42660046;https://dash.harvard.edu/bitstream/handle/1/42660046/Lu-et_al_prevalence_COVID-19_divergence_April_18th.pdf?sequence=1&isAllowed=y","","","","","","… Counts are taken up until April 14, 2020. COVID-19 Testing Counts: In addition, daily time series containing positive and negative COVID-19 test results within each state were obtained from the COVID Tracking Project [44] …","","","","","","","","","","","","",""
"Journal Article","Greene DN,Jackson ML,Hillyard DR,Delgado JC,Schmidt RL","","Decreasing median age of COVID-19 cases in the United States—Changing epidemiology or changing surveillance?","PLoS One","PloS one","2020","15","10","e0240783","COVID Tracking Project","","","","Public Library of Science","","","","","2020-10-15","2020-12-08","","1932-6203","","https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240783&type=printable;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240783;http://dx.doi.org/10.1371/journal.pone.0240783","10.1371/journal.pone.0240783","","","","","Background Understanding and monitoring the demographics of SARS-CoV-2 infection can inform strategies for prevention. Surveillance monitoring has suggested that the age distribution of people infected with SARS-CoV-2 has changed since the pandemic began, but no formal analysis has been performed. Methods Retrospective review of SARS-CoV-2 molecular testing results from a national reference laboratory was performed. Result distributions by age and positivity were compared between early period (March-April 2020) and late periods (June-July 2020) of the COVID-19 pandemic. Additionally, a sub-analysis compared changing age distributions between inpatients and outpatients. Results There were 277,601 test results of which 19320 (7.0%) were positive. The median age of infected people declined over time (p < 0.0005). In March-April, the median age of positive people was 40.8 years (Interquartile range (IQR): 29.0–54.1). In June-July, the median age of positive people was 35.8 years (IQR: 24.0–50.2). The positivity rate of patients under 50 increased from 6.0 to 10.6 percent and the positivity rate for those over 50 decreased from 6.3 to 5.0 percent between the early and late periods. The trend was only observed for outpatient populations. Conclusions We confirm that there is a trend toward decreasing age among persons with laboratory-confirmed SARS-CoV-2 infection, but that these trends seem to be specific to the outpatient population. Overall, this suggests that observed age-related trends are driven by changes in testing patterns rather than true changes in the epidemiology of SARS-CoV-2 infection. This calls for caution in interpretation of routine surveillance data until testing patterns stabilize.","","","","","","","","","","","","",""
"Journal Article","Liu X,Zheng X,Balachandran B","","COVID-19: data-driven dynamics, statistical and distributed delay models, and observations","Nonlinear Dyn.","Nonlinear dynamics","2020","","","1-17","COVID Tracking Project","","","","Springer","","","","","2020-08-06","","","0924-090X","","http://dx.doi.org/10.1007/s11071-020-05863-5;https://www.ncbi.nlm.nih.gov/pubmed/32836818;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409621;https://link.springer.com/article/10.1007/s11071-020-05863-5","10.1007/s11071-020-05863-5","32836818","","","PMC7409621","COVID-19 was declared as a pandemic by the World Health Organization on March 11, 2020. Here, the dynamics of this epidemic is studied by using a generalized logistic function model and extended compartmental models with and without delays. For a chosen population, it is shown as to how forecasting may be done on the spreading of the infection by using a generalized logistic function model, which can be interpreted as a basic compartmental model. In an extended compartmental model, which is a modified form of the SEIQR model, the population is divided into susceptible, exposed, infectious, quarantined, and removed (recovered or dead) compartments, and a set of delay integral equations is used to describe the system dynamics. Time-varying infection rates are allowed in the model to capture the responses to control measures taken, and distributed delay distributions are used to capture variability in individual responses to an infection. The constructed extended compartmental model is a nonlinear dynamical system with distributed delays and time-varying parameters. The critical role of data is elucidated, and it is discussed as to how the compartmental model can be used to capture responses to various measures including quarantining. Data for different parts of the world are considered, and comparisons are also made in terms of the reproductive number. The obtained results can be useful for furthering the understanding of disease dynamics as well as for planning purposes.","Delay integral equations; Dynamics and control of epidemics; Generalized logistic function; SEIQR model; System identification","","","State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China. Department of Mechanical Engineering, University of Maryland, College Park, MD 20742 USA.","en","Research Article","","","","","","",""
"Report","Barrios JM,Hochberg Y","","Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27008","nber.org","","","","","2020-04-20","2020-12-08","","","","https://www.nber.org/papers/w27008;http://dx.doi.org/10.3386/w27008;https://bfi.uchicago.edu/wp-content/uploads/BFI_WP_202032.pdf","10.3386/w27008","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Almario CV,Chey WD,Spiegel BMR","","Increased Risk of COVID-19 Among Users of Proton Pump Inhibitors","Am. J. Gastroenterol.","The American journal of gastroenterology","2020","115","10","1707-1715","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-10","","","0002-9270","1572-0241","http://dx.doi.org/10.14309/ajg.0000000000000798;https://www.ncbi.nlm.nih.gov/pubmed/32852340;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473791;https://Insights.ovid.com/pubmed?pmid=32852340;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473791/","10.14309/ajg.0000000000000798","32852340","","","PMC7473791","INTRODUCTION: Proton pump inhibitors (PPIs) increase the risk for enteric infections that is likely related to PPI-induced hypochlorhydria. Although the impact of acid suppression on severe acute respiratory syndrome coronavirus 2 is unknown thus far, previous data revealed that pH ≤3 impairs the infectivity of the similar severe acute respiratory syndrome coronavirus 1. Thus, we aimed to determine whether use of PPIs increases the odds for acquiring coronavirus disease 2019 (COVID-19) among community-dwelling Americans. METHODS: From May 3 to June 24, 2020, we performed an online survey described to participating adults as a \"national health survey.\" A multivariable logistic regression was performed on reporting a positive COVID-19 test to adjust for a wide range of confounding factors and to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). RESULTS: Of 53,130 participants, 3,386 (6.4%) reported a positive COVID-19 test. In regression analysis, individuals using PPIs up to once daily (aOR 2.15; 95% CI, 1.90-2.44) or twice daily (aOR 3.67; 95% CI, 2.93-4.60) had significantly increased odds for reporting a positive COVID-19 test when compared with those not taking PPIs. Individuals taking histamine-2 receptor antagonists were not at elevated risk. DISCUSSION: We found evidence of an independent, dose-response relationship between the use of antisecretory medications and COVID-19 positivity; individuals taking PPIs twice daily have higher odds for reporting a positive test when compared with those using lower-dose PPIs up to once daily, and those taking the less potent histamine-2 receptor antagonists are not at increased risk. These findings emphasize good clinical practice that PPIs should only be used when indicated at the lowest effective dose, such as the approved once-daily label dosage of over-the-counter and prescription PPIs. Further studies examining the association between PPIs and COVID-19 are needed.","","","","Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA. Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California, USA. Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, California, USA. Division of Health Services Research, Cedars-Sinai Medical Center, Los Angeles, California, USA. Division of Informatics, Cedars-Sinai Medical Center, Los Angeles, California, USA. Department of Medicine, Michigan Medicine, Ann Arbor, Michigan, USA. Division of Gastroenterology, Michigan Medicine, Ann Arbor, Michigan, USA. Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California, USA.","en","Research Article","","","","","","",""
"Journal Article","Barhak J","","The Reference Model: An Initial Use Case for COVID-19","Cureus","Cureus","2020","12","7","e9455","COVID Tracking Project","","","","search.proquest.com","","","","","2020-07-29","","","2168-8184","","http://dx.doi.org/10.7759/cureus.9455;https://www.ncbi.nlm.nih.gov/pubmed/32760637;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392354;http://search.proquest.com/openview/64f78d314f12909d35e595dd3e6fbea1/1?pq-origsite=gscholar&cbl=2045583&casa_token=fvMu2fANT-gAAAAA:AXGXZFuL6TcWF_2cgoIfD1atcQ7gjAjJ3GiDy4tjrToV51YJd-W38GC6N2ObLbnxX35xvldWyA","10.7759/cureus.9455","32760637","","","PMC7392354","The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques. The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors.","disease modeling; estimation; high performance computing; machine learning; monte-carlo; optimization; population modeling","","","Software Developer and Computational Disease Modeler, Jacob Barhak - Sole Proprietor, Austin, USA.","en","Research Article","","","","","","",""
"Journal Article","Fowler JH,Hill SJ,Obradovich N,Levin R","","The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.13.20063628v3.full.pdf","","","","","","Page 1. The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States James H. Fowler​1,2,*​, Seth J. Hill​2​, Remy Levin​3​, Nick Obradovich​4 1 ​Infectious Diseases and Global Public Health …","","","","","","","","","","","","",""
"Preprint Manuscript","Alamo T,Reina DG,Mammarella M,Abella A","","Open Data Resources for Fighting COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-13","","","","","http://arxiv.org/abs/2004.06111","","","2004.06111","","","We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic.","","","","","","","","arXiv","2004.06111","q-bio.OT","","","arXiv [q-bio.OT]"
"Journal Article","Peddireddy AS,Xie D,Patil P,Wilson ML,Machi D,Venkatramanan S,Klahn B,Porebski P,Bhattacharya P,Dumbre S,Marathe M","","From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-10-31","","","","","http://dx.doi.org/10.1101/2020.10.27.20220830;https://www.ncbi.nlm.nih.gov/pubmed/33140060;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605571;https://doi.org/10.1101/2020.10.27.20220830;https://www.medrxiv.org/content/10.1101/2020.10.27.20220830v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/31/2020.10.27.20220830.full.pdf","10.1101/2020.10.27.20220830","33140060","","","PMC7605571","The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.","","","","","en","Research Article","","","","","","",""
"Journal Article","Wu X,Nethery RC,Sabath BM,Braun D,Dominici F","","Exposure to air pollution and COVID-19 mortality in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/medrxiv/early/2020/04/27/2020.04.05.20054502.full.pdf","","","","","","Page 1. 1 Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study Xiao Wu, Rachel C Nethery, M Benjamin Sabath, Danielle Braun, Francesca Dominici Xiao Wu, doctoral student …","","","","","","","","","","","","",""
"Journal Article","Perniciaro SR,Weinberger DM","","Variations in state-level SARS-COV-2 testing recommendations in the United States, March-July 2020","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-11-15","","","","","http://dx.doi.org/10.1101/2020.09.04.20188326;https://www.ncbi.nlm.nih.gov/pubmed/33200142;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7668755;https://doi.org/10.1101/2020.09.04.20188326;https://www.medrxiv.org/content/10.1101/2020.09.04.20188326v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/07/2020.09.04.20188326.full.pdf","10.1101/2020.09.04.20188326","33200142","","","PMC7668755","BACKGROUND: Testing recommendations for COVID-19 in the United States varied by state and over time in the spring and summer of 2020. METHODS: We compiled data about COVID-19 testing, cases, and deaths, and excess pneumonia + influenza + COVID-19 deaths to assess relationships between testing recommendations, per capita tests performed, epidemic intensity, and excess mortality during the early phase of the COVID-19 pandemic in the United States. RESULTS: As of July 2020, 16 states recommended testing asymptomatic members of the general public. The rate of COVID-19 tests reported in each state correlates with more inclusive testing recommendations and with higher epidemic intensity. Higher per capita testing was associated with more complete reporting of COVID-19 deaths, which is a fundamental requirement for analyzing the pandemic. CONCLUSIONS: Reported deaths due to COVID-19 likely represent an undercount of the true burden of the pandemic. Coordinated, consistent guidelines for COVID-19 testing should be a high priority for state and national health systems.","","","","","en","Research Article","","","","","","",""
"Journal Article","Nabi KN","","Forecasting COVID-19 pandemic: A data-driven analysis","Chaos Solitons Fractals","Chaos, solitons, and fractals","2020","139","","110046","COVID Tracking Project","","","","Elsevier","","","","","2020-10","","","0960-0779","","http://dx.doi.org/10.1016/j.chaos.2020.110046;https://www.ncbi.nlm.nih.gov/pubmed/32834601;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315964;https://linkinghub.elsevier.com/retrieve/pii/S0960-0779(20)30443-4;https://www.sciencedirect.com/science/article/pii/S0960077920304434;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315964/","10.1016/j.chaos.2020.110046","32834601","","","PMC7315964","In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEIDIUQHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The purpose of this study is to give tentative predictions of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a newly developed algorithm based on well-known Trust-region-reflective (TRR) algorithm, which is one of the robust real-time optimization techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the middle of June with the peak size of ∼ 15, 774 (95% CI, 12,814-16,734) symptomatic infectious cases in Russia, ∼ 26, 449 (95% CI, 25,489-31,409) cases in Brazil, ∼ 9, 504 (95% CI, 8,378-13,630) cases in India and ∼ 2, 209 (95% CI, 2,078-2,840) cases in Bangladesh if current epidemic trends hold. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproductive number (R0) was found to be ∼ 4.234 (95% CI, 3.764-4.7) in Russia, ∼ 5.347 (95% CI, 4.737-5.95) in Brazil, ∼ 5.218 (95% CI, 4.56-5.81) in India, ∼ 4.649 (95% CI, 4.17-5.12) in the United Kingdom and ∼ 3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method has been applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission dynamics of the novel coronavirus disease (COVID-19). Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.","Asymptomatic carrier; COVID-19; Compartmental model; Coronavirus; Model calibration; Quarantined class; Sensitivity","","","Department of Mathematics, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.","en","Research Article","","","","","","",""
"Journal Article","Ahmad K,Erqou S,Shah N,Nazir U,Morrison AR,Choudhary G,Wu WC","","Association of poor housing conditions with COVID-19 incidence and mortality across US counties","PLoS One","PloS one","2020","15","11","e0241327","COVID Tracking Project","","","","journals.plos.org","","","","","2020-11-02","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0241327;https://www.ncbi.nlm.nih.gov/pubmed/33137155;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605696;https://dx.plos.org/10.1371/journal.pone.0241327;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241327","10.1371/journal.pone.0241327","33137155","","","PMC7605696","OBJECTIVE: Poor housing conditions have been linked with worse health outcomes and infectious disease spread. Since the relationship of poor housing conditions with incidence and mortality of COVID-19 is unknown, we investigated the association between poor housing condition and COVID-19 incidence and mortality in US counties. METHODS: We conducted cross-sectional analysis of county-level data from the US Centers for Disease Control, US Census Bureau and John Hopkins Coronavirus Resource Center for 3135 US counties. The exposure of interest was percentage of households with poor housing conditions (one or greater of: overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities). Outcomes were incidence rate ratios (IRR) and mortality rate ratios (MRR) of COVID-19 across US counties through 4/21/2020. Multilevel generalized linear modeling (with total population of each county as a denominator) was utilized to estimate relative risk of incidence and mortality related to poor housing conditions with adjustment for population density and county characteristics including demographics, income, education, prevalence of medical comorbidities, access to healthcare insurance and emergency rooms, and state-level COVID-19 test density. We report incidence rate ratios (IRRs) and mortality ratios (MRRs) for a 5% increase in prevalence in households with poor housing conditions. RESULTS: Across 3135 US counties, the mean percentage of households with poor housing conditions was 14.2% (range 2.7% to 60.2%). On April 21st, the mean (SD) number of cases and deaths of COVID-19 were 255.68 (2877.03) cases and 13.90 (272.22) deaths per county, respectively. In the adjusted models standardized by county population, with each 5% increase in percent households with poor housing conditions, there was a 50% higher risk of COVID-19 incidence (IRR 1.50, 95% CI: 1.38-1.62) and a 42% higher risk of COVID-19 mortality (MRR 1.42, 95% CI: 1.25-1.61). Results remained similar using earlier timepoints (3/31/2020 and 4/10/2020). CONCLUSIONS AND RELEVANCE: Counties with a higher percentage of households with poor housing had higher incidence of, and mortality associated with, COVID-19. These findings suggest targeted health policies to support individuals living in poor housing conditions should be considered in further efforts to mitigate adverse outcomes associated with COVID-19.","","","","The Providence Veterans Affairs Medical Center, Lifespan Hospitals and the Warren Alpert Medical School at Brown University, Providence, Rhode Island, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Oehmke JF,Moss CB,Singh LN,Oehmke TB,Post LA","","Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study","J. Med. Internet Res.","Journal of medical Internet research","2020","22","10","e21955","COVID Tracking Project","","","","jmir.org","","","","","2020-10-05","","","1439-4456","1438-8871","http://dx.doi.org/10.2196/21955;https://www.ncbi.nlm.nih.gov/pubmed/32924962;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546733;https://www.jmir.org/2020/10/e21955/;https://www.jmir.org/2020/10/e21955","10.2196/21955","32924962","","","PMC7546733","BACKGROUND: The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of \"sustained decline\" varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. OBJECTIVE: This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. METHODS: Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. RESULTS: The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. CONCLUSIONS: Reopening the United States comes with three certainties: (1) the \"social\" end of the pandemic and reopening are going to occur before the \"medical\" end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.","COVID-19; contagion; health policy; metrics; models; public health; reopening America; surveillance","","","Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Food and Resource Economics Department, University of Florida, Gainesville, FL, United States. Civil and Environmental Engineering, University of California, Berkley, Berkley, CA, United States.","en","Research Article","","","","","","",""
"Report","Dave DM,Friedson AI,Matsuzawa K,Sabia JJ,Safford S","","Black Lives Matter Protests, Social Distancing, and COVID-19","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27408","nber.org","","","","","2020-06-22","2020-12-08","","","","https://www.nber.org/papers/w27408;http://dx.doi.org/10.3386/w27408;https://www.nber.org/system/files/working_papers/w27408/w27408.pdf","10.3386/w27408","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Yadlowsky S,Shah N,Steinhardt J","","Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.03.24.20043067v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/03/27/2020.03.24.20043067.full.pdf","","","","","","Page 1. Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County By Steve Yadlowsky​1​, Nigam Shah​2​, and Jacob Steinhardt​3 syadlows@stanford. edu​, ​nigam@stanford.edu​, ​jsteinhardt@berkeley …","","","","","","","","","","","","",""
"Journal Article","Thunström L,Ashworth M,Shogren JF,Newbold S,Finnoff D","","Testing for COVID-19: willful ignorance or selfless behavior?","Behavioural Public Policy","Behavioural Public Policy ","2020","","","1-18","COVID Tracking Project","","","","Cambridge University Press","","","","","2020","2020-12-08","","2398-063X","2398-0648","https://www.cambridge.org/core/journals/behavioural-public-policy/article/testing-for-covid19-willful-ignorance-or-selfless-behavior/2C6249A9D0382BCA8C667825B2A07E12;http://dx.doi.org/10.1017/bpp.2020.15;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/2C6249A9D0382BCA8C667825B2A07E12/S2398063X20000159a.pdf/div-class-title-testing-for-covid-19-willful-ignorance-or-selfless-behavior-div.pdf","10.1017/bpp.2020.15","","","","","Widespread testing is key to controlling the spread of COVID-19. But should we worry about self-selection bias in the testing? The recent literature on willful ignorance says we should – people often avoid health information. In the context of COVID-19, such willful ignorance can bias testing data. Furthermore, willful ignorance often arises when selfish wants conflict with social benefits, which might be particularly likely for potential ‘super-spreaders’ – people with many social interactions – given people who test positive are urged to self-isolate for two weeks. We design a survey in which participants (n = 897) choose whether to take a costless COVID-19 test. We find that 70% would take a test. Surprisingly, the people most likely to widely spread COVID-19 – the extraverts, others who meet more people in their daily lives and younger people – are the most willing to take a test. People's ability to financially or emotionally sustain self-isolation does not matter to their decision. We conclude that people are selfless in their decision to test for COVID-19. Our results are encouraging – they imply that COVOD-19 testing may succeed in targeting those who generate the largest social benefits from self-isolation if infected, which strengthens the case for widespread testing.","","","","","","","","","","","","",""
"Journal Article","Bosch J,Wilson A,O'Neil K,Zimmerman PA","","COVID-19Predict-Predicting Pandemic Trends","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.09.20191593v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/11/2020.09.09.20191593.full.pdf","","","","","","… Reliability or precision of the IR (or similar measures by many other sources) is dependent on the level of testing performed within any individual state. The Covid Tracking Project (https://covidtracking.com/) accumulates and describes this information on a state-by-state basis16 …","","","","","","","","","","","","",""
"Journal Article","Wagner AB,Hill EL,Ryan SE,Sun Z,Deng G,et al.","","Social distancing merely stabilized COVID‐19 in the United States","Stat","Stat","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","0038-9986","","https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.302;https://www.researchgate.net/profile/Victor_Hernandez_Martinez2/publication/342927830_Social_distancing_merely_stabilized_COVID-19_in_the_US/links/5f6df40ca6fdcc00863a909c/Social-distancing-merely-stabilized-COVID-19-in-the-US.pdf","","","","","","… recorded by the (CDC 2020). We reference data on testing in New York State in Section 6. This data was obtained from the COVID Tracking Project ( Covid Tracking Project 2020). 3 METHODOLOGY We focus on estimating …","","","","","","","","","","","","",""
"Journal Article","Sheinson D,Wong W,Solon C,Cheng M,Elsea D,et al.","","Impact of public and private sector COVID-19 diagnostics and treatments on US healthcare resource utilization","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.11.09.20228452v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/12/2020.11.09.20228452.full.pdf","","","","","","… gathered to inform input parameter values and provide prior information for calibrating the model to observed data from The COVID Tracking Project [17] (see appendix in … cumulative mortality that tracked with observed data from The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Gurumurthy K,Mukherjee A","","The Bass Model: a parsimonious and accurate approach to forecasting mortality caused by COVID-19","International Journal of Pharmaceutical and Healthcare Marketing","","2020","ahead-of-print","ahead-of-print","","COVID Tracking Project","","","","emerald.com","","","","","2020-01-01","","","1750-6123","","https://doi.org/10.1108/IJPHM-06-2020-0056;http://dx.doi.org/10.1108/IJPHM-06-2020-0056;https://www.emerald.com/insight/content/doi/10.1108/IJPHM-06-2020-0056/full/html","10.1108/IJPHM-06-2020-0056","","","","","Purpose The novel coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges in terms of understanding its unique characteristics of transmission and predicting its spread. The purpose of this study is to present a simple, parsimonious and accurate model for forecasting mortality caused by COVID-19.Design/methodology/approach The presented Bass Model is compared it with several alternative existing models for forecasting the spread of COVID-19. This study calibrates the model for deaths for the period, March 21 to April 30 for the USA as a whole and as the US States of New York, California and West Virginia. The daily data from the COVID-19 Tracking Project has been used, which is a volunteer organization launched from The Atlantic. Every day, data is collected on testing and patient outcomes from all the 50 states, 5 territories and the District of Columbia. This data set is widely used by policymakers and scholars. The fit of the model (F-value and its significance, R-squared value) and the statistical significance of the variables (t-values) for each one of the four estimates are examined. This study also examines the forecast of deaths for a three-day period, May 1 to 3 for each one of the four estimates – US, and States of New York, California and West Virginia. Based on these metrics, the viability of the Bass Model is assessed. The dependent variable is the number of deaths, and the two independent variables are cumulative number of deaths and its squared value.Findings The findings of this paper show that compared to other forecasting methods, the Bass Model performs remarkably well. In fact, it may even be argued that the Bass Model does better with its forecast. The calibration of models for deaths in the USA, and States of New York, California and West Virginia are all found to be significant. The F values are large and the significance of the F values is low, that is, the probability that the model is wrong is very miniscule. The fit as measured by R-squared is also robust. Further, each of the two independent variables is highly significant in each of the four model calibrations. These forecasts also approximate the actual numbers reasonably well.Research limitations/implications This study illustrates the applicability of the Bass Model to estimate the diffusion of COVID-19 with some preliminary but important empirical analyses. This study argues that while the more sophisticated models may produce slightly better estimates, the Bass model produces robust and reasonably accurate estimates given the extreme parsimony of the model. Future research may investigate applications of the Bass Model for pandemic management using additional variables and other theoretical lenses.Practical implications The Bass Model offers effective forecasting of mortality resulting from COVID-19 to help understand how the curve can be flattened, how hospital capacity could be overwhelmed and how fatality rates might climb based on time and geography in the upcoming weeks and months.Originality/value This paper demonstrates the efficacy of the Bass Model as a parsimonious, accessible and theory-based approach that can predict the mortality rates of COVID-19 with minimal data requirements, simple calibration and accessible decision calculus. For all these reasons, this paper recommends further and continued examination of the Bass Model as an instrument for forecasting COVID-19 (and other epidemic/pandemic) mortality and health resource requirements. As this paper has demonstrated, there is much promise in this model.","","","","","","","","","","","","",""
"Journal Article","Kerr CC,Stuart RM,Mistry D,Abeysuriya RG,Hart G,et al.","","Covasim: an agent-based model of COVID-19 dynamics and interventions","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.10.20097469v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/15/2020.05.10.20097469.full.pdf","","","","","","Page 1. / Covasim: an agent-based model of COVID-19 dynamics and interventions Cliff C. Kerr 1* , Robyn M. Stuart 2,3 , Dina Mistry 1 , Romesh G. Abeysuriya 3 , Gregory Hart 1 , Katherine Rosenfeld 1 , Prashanth Selvaraj …","","","","","","","","","","","","",""
"Journal Article","Berry-James RM,Blessett B,Emas R,McCandless S,Nickels AE,Norman-Major K,Vinzant P","","Stepping up to the plate: Making social equity a priority in public administration’s troubled times","Journal of Public Affairs Education","Journal of Public Affairs Education","2020","","","1-11","COVID Tracking Project","","","","Routledge","","","","","2020-09-22","","","1523-6803","","https://doi.org/10.1080/15236803.2020.1820289;http://dx.doi.org/10.1080/15236803.2020.1820289;https://www.tandfonline.com/doi/full/10.1080/15236803.2020.1820289","10.1080/15236803.2020.1820289","","","","","… 13% of cases. In Iowa, Black residents represent 3% of the population but are 11% of COVID-19 cases; Latinx people make up only 6% of the population but are 27% of cases (The COVID Tracking Project , 2020). Further, the …","","","","","","","","","","","","",""
"Preprint Manuscript","Fabic MS,Choi Y","","Trends in age distribution of COVID-19 cases, hospitalizations, and deaths by race in the United States","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08","","","","","http://dx.doi.org/10.31235/osf.io/7edgu;https://osf.io/preprints/socarxiv/7edgu/;https://osf.io/preprints/socarxiv/7edgu/download","10.31235/osf.io/7edgu","","","","","COVID-19 cases are quickly growing across the United States with numerous states reporting that the proportion of cases among young people is ballooning. COVID-19 data are typically presented cumulatively and by only one demographic characteristic. Understanding and communicating complex demographic trends is imperative to recognize population-level vulnerabilities and inform tailored public health responses. Using the latest COVID-19 Case Surveillance Public Use Data by the Centers for Disease Control and Prevention (CDC), we aim to: a) assess one dimension of reporting quality-- data completeness; and b) examine national time-trends in the age pattern of COVID-19 cases, hospitalizations, and deaths overall as well as by race and ethnicity. Reporting of race and ethnicity in COVID-19 cases has been persistently poor, multiple months into the pandemic. Our analysis also shows unequal and changing age-patterns among cases, hospitalizations, and deaths by race and ethnicity. Age-pattern differences between whites and other races are widening.","COVID-19; demographics; disparity; ethnicity; race; surveillance","","","","","","","","","","","",""
"Journal Article","Qeadan F,Honda T,Gren LH,Dailey-Provost J,Benson LS,VanDerslice JA,Porucznik CA,Waters AB,Lacey S,Shoaf K","","Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2020","17","8","","COVID Tracking Project","","","","mdpi.com","","","","","2020-04-16","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph17082750;https://www.ncbi.nlm.nih.gov/pubmed/32316165;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215611;https://www.mdpi.com/resolver?pii=ijerph17082750;https://www.mdpi.com/1660-4601/17/8/2750;https://www.mdpi.com/1660-4601/17/8/2750/pdf","10.3390/ijerph17082750","32316165","","","PMC7215611","Differences in jurisdictional public health actions have played a significant role in the relative success of local communities in combating and containing the COVID-19 pandemic. We forecast the possible COVID-19 outbreak in one US state (Utah) by applying empirical data from South Korea and Italy, two countries that implemented disparate public health actions. Forecasts were created by aligning the start of the pandemic in Utah with that in South Korea and Italy, getting a short-run forecast based on actual daily rates of spread, and long-run forecast by employing a log-logistic model with four parameters. Applying the South Korea model, the epidemic peak in Utah is 169 cases/day, with epidemic resolution by the end of May. Applying the Italy model, new cases are forecast to exceed 200/day by mid-April, with the potential for 250 new cases a day at the epidemic peak, with the epidemic continuing through the end of August. We identify a 3-month variation in the likely length of the pandemic, a 1.5-fold difference in the number of daily infections at outbreak peak, and a 3-fold difference in the expected cumulative cases when applying the experience of two developed countries in handling this virus to the Utah context.","COVID-19; pandemic; predictive modeling","","","Division of Public Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA. Division of Physician Assistant Studies, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA.","en","Research Article","","","","","","",""
"Journal Article","Chiang A","","COVID-19 Outbreak-When Will It End and How to End It","andrewchiang.net","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.andrewchiang.net/when-will-it-end-and-how-to-end-it","","","","","","Suggest a testing milestone which is needed to effectively reduce the growth of COVID-19 infection without resorting to costly lockdown or shelter-in-place.","","","","","","","","","","","","",""
"Preprint Manuscript","Von Batten K","","The Effects of Statewide Stay-at-Home Orders, Mandatory Protective Face Mask Provisions, and COVID-19 Testing on the Number of Confirmed COVID-19 Infections","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-01","2020-12-08","","","","https://papers.ssrn.com/abstract=3616422;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3616422;http://dx.doi.org/10.2139/ssrn.3616422;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3616422","10.2139/ssrn.3616422","","","","","In response to the COVID-19 pandemic, forty-one states and the District of Columbia issued statewide stay-at-home orders. This study's findings indicate that statewide stay-at-home orders are least effective without mandatory face-mask provisions, and mandatory face masks are more effective than stay-at-home orders to reduce the number of COVID-19 infections. The analysis shows a moderate negative correlation between the number of confirmed COVID-19 infections and the number of mandatory face mask and face-covering provisions in statewide orders. A weak negative correlation between the number of confirmed COVID-19 and stay-at-home orders still in effect as of May 14, 2020. A very strong positive correlation was found between the number of confirmed COVID-19 infections and COVID-19 testing. A Stepwise multiple regression analysis was performed, in place of a Poisson regression, due to a failure to fit. The regression results indicate that confirmed COVID-19 infection numbers increased by 0.1639 infections each test performed and decreased by 33,723 because of mandatory face masks and face-covering provisions. Statewide stay-at-home orders were founded to be the least significant variable and removed by the Stepwise multiple regression analysis.","COVID-19, stay-at-home order, protective face masks, COVID-19 testing","","","","","","","","","","","","Mandatory Protective Face Mask Provisions, and"
"Journal Article","Tibbetts JH","","Researchers Continue Quest to Contain Spread of COVID-19: Digital technologies aim to accelerate contact tracing","Bioscience","Bioscience","2020","70","8","633-639","COVID Tracking Project","","","","Oxford Academic","","","","","2020-06-30","2020-12-08","","0006-3568","","https://academic.oup.com/bioscience/article-abstract/70/8/633/5864935;http://dx.doi.org/10.1093/biosci/biaa071;https://academic.oup.com/bioscience/article/70/8/633/5864935?casa_token=Dl1rkwV7wnYAAAAA:Ys3z9Ds-VG-uWKD0Ar4SmCKktk4GMpfoyKej4-r3umt3OCHj27AhXAZ2fNe82qV4SYfhZ_CC_lLr","10.1093/biosci/biaa071","","","","","As researchers and public-health experts struggle to contain a global pandemic, some are harnessing the latest digital technologies for epidemiological detectiv","","","","","en","","","","","","","",""
"Journal Article","Faust JS,Del Rio C","","Assessment of Deaths From COVID-19 and From Seasonal Influenza","JAMA Intern. Med.","JAMA internal medicine","2020","180","8","1045-1046","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-08-01","","","2168-6106","2168-6114","http://dx.doi.org/10.1001/jamainternmed.2020.2306;https://www.ncbi.nlm.nih.gov/pubmed/32407441;https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/10.1001/jamainternmed.2020.2306;https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2766121;https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2766121","10.1001/jamainternmed.2020.2306","32407441","","","","… BMJ. 2005;331(7529):1412. doi:10.1136/bmj.331.7529.1412Google ScholarCrossref. 9. The COVID Tracking Project . US historical data. Accessed April 27, 2020. https://covidtracking.com/data/us-daily/. 10. World Health Organization …","","","","Harvard Medical School, Brigham and Women's Hospital, Division of Health Policy and Public Health, Department of Emergency Medicine, Boston, Massachusetts. Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia. Hubert Department of Global Health, Rollins School of Public Health of Emory University, Atlanta, Georgia.","en","Research Article","","","","","","",""
"Journal Article","Chande A,Lee S,Harris M,Nguyen Q,Beckett SJ,Hilley T,Andris C,Weitz JS","","Real-time, interactive website for US-county-level COVID-19 event risk assessment","Nat Hum Behav","Nature human behaviour","2020","","","","COVID Tracking Project","","","","nature.com","","","","","2020-11-09","","","2397-3374","","http://dx.doi.org/10.1038/s41562-020-01000-9;https://www.ncbi.nlm.nih.gov/pubmed/33168955;https://doi.org/10.1038/s41562-020-01000-9;https://www.nature.com/articles/s41562-020-01000-9","10.1038/s41562-020-01000-9","33168955","","","","Large events and gatherings, particularly those taking place indoors, have been linked to multitransmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geolocalized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real-time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy makers make prudent decisions (for example, increasing mask-wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.","","","","School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. Applied Bioinformatics Laboratory, Atlanta, GA, USA. School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA, USA. Department of Biology, Stanford University, Palo Alto, CA, USA. School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA, USA. clio@gatech.edu. School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA. clio@gatech.edu. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. jsweitz@gatech.edu. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA. jsweitz@gatech.edu.","en","Research Article","","","","","","",""
"Journal Article","Erfani P,Uppal N,Lee CH,Mishori R,Peeler KR","","COVID-19 Testing and Cases in Immigration Detention Centers, April-August 2020","JAMA","JAMA: the journal of the American Medical Association","2020","","","","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-10-29","","","0098-7484","1538-3598","http://dx.doi.org/10.1001/jama.2020.21473;https://www.ncbi.nlm.nih.gov/pubmed/33119038;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596672;https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2020.21473;https://jamanetwork.com/journals/jama/article-abstract/2772627;https://jamanetwork.com/journals/jama/fullarticle/2772627","10.1001/jama.2020.21473","33119038","","","PMC7596672","… ICE guidance on COVID-19. Accessed August 31, 2020. https://www.ice.gov/coronavirus. 2. The COVID Tracking Project . US historical data … Accessed August 31, 2020. https://www.ice.gov/coronavirus. 2. The COVID Tracking Project . US historical data …","","","","Harvard Medical School, Boston, Massachusetts. Department of Family Medicine, Georgetown University School of Medicine, Washington, DC. Division of Medical Critical Care, Boston Children's Hospital, Boston, Massachusetts.","en","Research Article","","","","","","",""
"Journal Article","Rivera JM,Gupta S,Ramjee D,El Hayek GY,El Amiri N,Desai AN,Majumder MS","","Evaluating interest in off-label use of disinfectants for COVID-19","Lancet Digit Health","The Lancet. Digital health","2020","2","11","e564-e566","COVID Tracking Project","","","","thelancet.com","","","","","2020-11","","","2589-7500","","http://dx.doi.org/10.1016/S2589-7500(20)30215-6;https://www.ncbi.nlm.nih.gov/pubmed/33015597;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521872;https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(20)30215-6;https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30215-6/fulltext","10.1016/S2589-7500(20)30215-6","33015597","","","PMC7521872","… Evaluating interest in off-label use of disinfectants for COVID-19. Jessica Malaty Rivera Jessica Malaty Rivera. Contact. Affiliations The COVID Tracking Project , The Atlantic, Washington, DC 20037, USA. Search for articles by this author …","","","","The COVID Tracking Project, The Atlantic, Washington, DC 20037, USA. Boston Children's Computational Health Informatics Program, Boston, MA, USA. Department of Justice, Law and Criminology, American University, Washington, DC, USA. National Collaborative Perinatal Neonatal Network, Beirut, Lebanon. Department of Pediatrics and Adolescent Medicine, American University of Beirut, Beirut, Lebanon. Child Health Evaluative Sciences Program, SickKids Hospital, Toronto, ON, Canada. Department of Internal Medicine, University of California, Davis, Sacramento, CA, USA. Harvard Medical School, Harvard University, Boston, MA, USA. Boston Children's Computational Health Informatics Program Boston, MA, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Alhaery M","","A COVID-19 Reopening Readiness Index: The Key to Opening up the Economy","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-22","2020-12-08","","","","https://papers.ssrn.com/abstract=3607605;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3607605;http://dx.doi.org/10.2139/ssrn.3607605;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3607605;https://www.medrxiv.org/content/medrxiv/early/2020/05/26/2020.05.22.20110577.full.pdf","10.2139/ssrn.3607605","","","","","With respect to reopening the economy as a result of the COVID-19 restrictions, governmental response and messaging have been inconsistent, and policies have varied by state as this is a uniquely polarizing topic. Considering the urgent need to return to normalcy, a method was devised to determine the degree of progress any state has made in containing the spread of COVID-19. Using various measures for each state including mortality, hospitalizations, testing capacity, number of infections and infection rate has allowed for the creation of a composite COVID -19 Reopening Readiness Index. This index can serve as a comprehensive reliable and simple-to-use metric to assess the level of containment in any state and to determine the level of risk in further opening. As states struggle to contain the outbreak and at the same time face great pressure in resuming economic activity, an index that provides a data-driven and objective insight is urgently needed.","COVID-19, Health index","","","","","","","","","","","","Available at SSRN 3607605"
"Journal Article","Pierce MC","","Don't Waste a Crisis: Opportunities to Enhance BME Student Learning Through COVID-19","Biomedical Engineering Education","","2020","","","","COVID Tracking Project","","","","Springer","","","","","2020","","","","","https://link.springer.com/article/10.1007/s43683-020-00021-0","","","","","","… As the COVID-19 pandemic began to develop, students reviewed and discussed incidence and mortality figures from sources including the Johns Hopkins University COVID-19 Dashboard and The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Russi BS,Anne Watkins BS","","Estimating the early death toll of COVID-19 in the United States","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.04.15.20066431v2.full.pdf","","","","","","… laboratory-confirmed deaths due to COVID-19 in each state from several sources, including the Covid Tracking Project ,12 and NCHS13. State-specific testing information was obtained was The Covid Tracking Project 12. Excess mortality and morbidity analysis …","","","","","","","","","","","","",""
"Journal Article","Zhao Y,Ji Z,Wang P,Meng X,Ma Z,Li X,Sun H,Li W","","Epidemiological modeling analysis reveals the transmission potential of COVID-19 asymptomatic patients: a prospective study of epidemiological transmission …","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-103012/latest.pdf","","","","","","… number11,12. The COVID-19 data sources we used were collected by the COVID Tracking Project , an organization that records epidemic data in the United States. The data source has sufficient authority and influence to meet the requirements of our research. Methods …","","","","","","","","","","","","",""
"Journal Article","Zheng DX,Jella TK,Mitri EJ,Camargo Jr CA","","National analysis of COVID-19 and older emergency physicians","Am. J. Emerg. Med.","The American journal of emergency medicine","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-11-04","","","0735-6757","1532-8171","http://dx.doi.org/10.1016/j.ajem.2020.10.074;https://www.ncbi.nlm.nih.gov/pubmed/33187774;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641536;https://linkinghub.elsevier.com/retrieve/pii/S0735-6757(20)30974-8;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641536/","10.1016/j.ajem.2020.10.074","33187774","","","PMC7641536","… References. 1. The COVID Tracking Project . https://covidtracking.com/. 2. Wu C., Chen X., Cai Y. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med …","COVID-19; Coronavirus; Coronavirus disease 2019; Emergency department; Emergency medicine; SARS-CoV-2","","","Case Western Reserve University School of Medicine, Cleveland, OH, USA; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Case Western Reserve University School of Medicine, Cleveland, OH, USA. Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ccamargo@partners.org.","en","Research Article","","","","","","",""
"Journal Article","Suh E,Alhaery M","","A COVID-19 Reopening Readiness Index: The Key to Opening up the Economy","","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Matt_Alhaery/publication/341554537_A_COVID-19_Reopening_Readiness_Index_The_Key_to_Opening_up_the_Economy/links/5ec7163e458515626cbf2fbe/A-COVID-19-Reopening-Readiness-Index-The-Key-to-Opening-up-the-Economy.pdf","","","","","","… Steps for Calculating Indexes Data from The COVID Tracking Project (COVID Tracking, 2020) was used to create the indicators and the associated sub-indexes. Step 1. Generate the three-day rolling average of daily counts …","","","","","","","","","","","","",""
"Journal Article","Prasad A,Civantos AM,Byrnes Y,Chorath K,Poonia S,Chang C,Graboyes EM,Bur AM,Thakkar P,Deng J,Seth R,Trosman S,Wong A,Laitman BM,Shah J,Stubbs V,Long Q,Choby G,Rassekh CH,Thaler ER,Rajasekaran K","","Snapshot Impact of COVID-19 on Mental Wellness in Nonphysician Otolaryngology Health Care Workers: A National Study","OTO Open","OTO open","2020","4","3","2473974X20948835","COVID Tracking Project","","","","journals.sagepub.com","","","","","2020-07","","","2473-974X","","http://dx.doi.org/10.1177/2473974X20948835;https://www.ncbi.nlm.nih.gov/pubmed/32839747;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415941;https://journals.sagepub.com/doi/10.1177/2473974X20948835?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://journals.sagepub.com/doi/abs/10.1177/2473974X20948835;https://journals.sagepub.com/doi/pdf/10.1177/2473974X20948835","10.1177/2473974X20948835","32839747","","","PMC7415941","Objective: Nonphysician health care workers are involved in high-risk patient care during the COVID-19 pandemic, placing them at high risk of mental health burden. The mental health impact of COVID-19 in this crucial population has not been studied thus far. Thus, the objective of this study is to assess the psychosocial well-being of these providers. Study Design: National cross-sectional online survey (no control group). Setting: Academic otolaryngology programs in the United States. Subjects and Methods: We distributed a survey to nonphysician health care workers in otolaryngology departments across the United States. The survey incorporated a variety of validated mental health assessment tools to measure participant burnout (Mini-Z assessment), anxiety (Generalized Anxiety Disorder-7), distress (Impact of Event Scale), and depression (Patient Health Questionnaire-2). Multivariable logistic regression analysis was performed to determine predictive factors associated with these mental health outcomes. Results: We received 347 survey responses: 248 (71.5%) nurses, 63 (18.2%) administrative staff, and 36 (10.4%) advanced practice providers. A total of 104 (30.0%) respondents reported symptoms of burnout; 241 (69.5%), symptoms of anxiety; 292 (84.1%), symptoms of at least mild distress; and 79 (22.8%), symptoms of depression. Upon further analysis, development of these symptoms was associated with factors such as occupation, practice setting, and case load. Conclusion: Frontline otolaryngology health care providers exhibit high rates of mental health complications, particularly anxiety and distress, in the wake of COVID-19. Adequate support systems must be put into place to address these issues.","COVID-19; aerosolization; health care workers; mental health; psychiatric distress","","","Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Otolaryngology, Medical University of South Carolina, Charleston, South Carolina, USA. Department of Otolaryngology, School of Medicine, University of Kansas, Kansas City, Kansas, USA. Department of Otolaryngology, George Washington University, Washington, DC, USA. Department of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Otolaryngology, University of San Francisco, San Francisco, California, USA. Department of Otolaryngology, Mt. Sinai Health System, New York City, New York, USA. Department of Otolaryngology, Cleveland Clinic, Cleveland, Ohio, USA. Department of Otolaryngology, University of Miami, Miami, Florida, USA. Department of Otolaryngology, Mayo Clinic, Rochester, Minnesota, USA.","en","Research Article","","","","","","",""
"Journal Article","Li AY,Hannah TC,Durbin JR,Dreher N,McAuley FM,Marayati NF,Spiera Z,Ali M,Gometz A,Kostman JT,Choudhri TF","","Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US","Am. J. Med. Sci.","The American journal of the medical sciences","2020","360","4","348-356","COVID Tracking Project","","","","Elsevier","","","","","2020-10","","","0002-9629","1538-2990","http://dx.doi.org/10.1016/j.amjms.2020.06.015;https://www.ncbi.nlm.nih.gov/pubmed/32709397;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305735;https://linkinghub.elsevier.com/retrieve/pii/S0002-9629(20)30257-3;https://www.sciencedirect.com/science/article/pii/S0002962920302573;https://www.amjmedsci.com/article/S0002-9629(20)30257-3/fulltext","10.1016/j.amjms.2020.06.015","32709397","","","PMC7305735","BACKGROUND: There has been much interest in environmental temperature and race as modulators of Coronavirus disease-19 (COVID-19) infection and mortality. However, in the United States race and temperature correlate with various other social determinants of health, comorbidities, and environmental influences that could be responsible for noted effects. This study investigates the independent effects of race and environmental temperature on COVID-19 incidence and mortality in United States counties. METHODS: Data on COVID-19 and risk factors in all United States counties was collected. 661 counties with at least 50 COVID-19 cases and 217 with at least 10 deaths were included in analyses. Upper and lower quartiles for cases/100,000 people and halves for deaths/100,000 people were compared with t-tests. Adjusted linear and logistic regression analyses were performed to evaluate the independent effects of race and environmental temperature. RESULTS: Multivariate regression analyses demonstrated Black race is a risk factor for increased COVID-19 cases (OR=1.22, 95% CI: 1.09-1.40, P=0.001) and deaths independent of comorbidities, poverty, access to health care, and other risk factors. Higher environmental temperature independently reduced caseload (OR=0.81, 95% CI: 0.71-0.91, P=0.0009), but not deaths. CONCLUSIONS: Higher environmental temperatures correlated with reduced COVID-19 cases, but this benefit does not yet appear in mortality models. Black race was an independent risk factor for increased COVID-19 cases and deaths. Thus, many proposed mechanisms through which Black race might increase risk for COVID-19, such as socioeconomic and healthcare-related predispositions, are inadequate in explaining the full magnitude of this health disparity.","Black Race; COVID-19; Coronavirus; Environmental temperature; SARS-CoV-2","","","Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: Adam.Li@icahn.mssm.edu. Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York. EastSide Rehab PC, New York, New York. Artificial Intelligence, ProtectedBy.AI, Reston, Virginia.","en","Research Article","","","","","","",""
"Journal Article","Espana G,Cavany S,Oidtman RJ,Barbera C,et al.","","Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.22.20179960v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/25/2020.08.22.20179960.full.pdf","","","","","","Page 1. Impacts of K-12 school reopening on the COVID-19 epidemic in 1 Indiana, USA 2 Guido España#, Sean Cavany*, Rachel Oidtman*, Carly Barbera, Alan Costello, Anita Lerch, Marya Poterek, Quan Tran, Annaliese Wieler Sean Moore, T. Alex Perkins# …","","","","","","","","","","","","",""
"Journal Article","Godefroy R,Lewis J","","Estimates of COVID-19 Cases Across Four Canadian Provinces","Journal of Epidemiology","","2020","","","","COVID Tracking Project","","","","webdepot.umontreal.ca","","","","","2020","","","","","https://www.webdepot.umontreal.ca/Usagers/lewisj/MonDepotPublic/bgl_covid_canada.pdf","","","","","","… Manski, C., and F. Molinari. 2020. “Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem.” Journal of Econometrics, Forthcoming. Meyer, R., E. Kissane, and A. Madrigal. 2020. “The COVID Tracking Project .” https:/","","","","","","","","","","","","",""
"Journal Article","Han Y,Li VOK,Lam JCK,Guo P,Bai R,Fok WWT","","Who is more susceptible to Covid-19 infection and mortality in the States?","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.01.20087403v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/06/2020.05.01.20087403.full.pdf","","","","","","… Covid-19 testing capacity is the daily number of people who get tested. The data can be obtained from the COVID Tracking Project which collects data from the health departments of different states (27) … 27. The Atlantic (2020) The COVID Tracking Project . 28 …","","","","","","","","","","","","",""
"Journal Article","Noh J,Danuser G","","Estimation of the fraction of COVID-19 infected people in US states and countries worldwide","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.26.20202382v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/28/2020.09.26.20202382.full.pdf","","","","","","… Ann Epidemiol. 2020;48:23-9 e4. Epub 214 2020/07/11. doi: 10.1016/j.annepidem.2020.06. 004. PubMed PMID: 32648546; PMCID: PMC7297691. 215 6. The COVID Tracking Project . https://covidtracking.com/ [Accessed September 3, 2020] …","","","","","","","","","","","","",""
"Journal Article","Srivastava A,Chowell G","","Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-05-25","","","","","http://dx.doi.org/10.1101/2020.05.25.20112433;https://www.ncbi.nlm.nih.gov/pubmed/32511500;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273268;https://doi.org/10.1101/2020.05.25.20112433;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273268/","10.1101/2020.05.25.20112433","32511500","","","PMC7273268","The growth rates of COVID-19 across different geographical regions (e.g., states in a nation, countries in a continent) follow different shapes and patterns. The overall summaries at coarser spatial scales that are obtained by simply averaging individual curves (across regions) obscure nuanced variability and blurs the spatial heterogeneity at finer spatial scales. We employ statistical methods to analyze shapes of local COVID-19 growth rate curves and statistically group them into distinct clusters, according to their shapes. Using this information, we derive the so-called elastic averages of curves within these clusters, which correspond to the dominant incidence patterns. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic at two spatial scales: A state-level analysis within the USA and a country-level analysis within Europe during mid-February to mid-May, 2020. Our analyses reveal a few dominant incidence trajectories that characterize transmission dynamics across states in the USA and across countries in Europe. This approach results in broad classifications of spatial areas into different trajectories and adds to the methodological toolkit for guiding public health decision making at different spatial scales.","","","","","en","Research Article","","","","","","",""
"Journal Article","Madhavan S,Bastarache L,Brown JS,Butte AJ,Dorr DA,Embi PJ,Friedman CP,Johnson KB,Moore JH,Kohane IS,Payne PRO,Tenenbaum JD,Weiner MG,Wilcox AB,Ohno-Machado L","","Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers","J. Am. Med. Inform. Assoc.","Journal of the American Medical Informatics Association: JAMIA","2020","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2020-11-03","","","1067-5027","1527-974X","http://dx.doi.org/10.1093/jamia/ocaa287;https://www.ncbi.nlm.nih.gov/pubmed/33260207;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665546;https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocaa287;https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocaa287/5952684;https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocaa287/34644693/ocaa287.pdf","10.1093/jamia/ocaa287","33260207","","","PMC7665546","Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.","covid-19; data network; ehr; policy; public health","","","Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA. University of California Health System (UC Health), University of California, San Francisco, California, USA. Departments of Medical Informatics and Clinical Epidemiology and Medicine, Oregon Health and Science University, Portland, Oregon, USA. Indiana University School of Medicine, Regenstrief Institute, Inc, Indianapolis, Indiana, USA. Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA. Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. Institute for Informatics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA. North Carolina Department of Health and Human Services, Raleigh, North Carolina, USA. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA. Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA. Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA. Department of Biomedical Informatics, University of California San Diego Health, La Jolla, California, USA. Division of Health Services Research & Development, VA San Diego Healthcare System, San Diego, California, USA.","en","Research Article","","","","","","",""
"Journal Article","Jacobsen R","","The Continuing Saga of COVID-19 in the USA","POLICY","","2020","","","","COVID Tracking Project","","","","politikaspolecnost.cz","","","","","2020","","","","","https://www.politikaspolecnost.cz/wp-content/uploads/2020/07/The-Continuing-Saga-of-COVID-19-in-the-USA_IPPS.pdf","","","","","","… 2019, https://www.brookings.edu/product/everything-you-need-to-know-about-surprise-billing/ 18 The Covid Tracking Project . National Overview … New York Times, 2020, https://www.nytimes. com/2020/07/14/us/politics/trump-cdc- coronavirus.html The Covid Tracking Project …","","","","","","","","","","","","",""
"Preprint Manuscript","Qin X,Yam KC,Xu M,Zhang H","","The Increase in COVID-19 Cases is Associated with Domestic Violence","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08","","","","","psyarxiv.com/yfkdx;http://dx.doi.org/10.31234/osf.io/yfkdx;https://psyarxiv.com/yfkdx;https://psyarxiv.com/yfkdx/download?format=pdf","10.31234/osf.io/yfkdx","","","","","Numerous anecdotal reports suggest that domestic violence has increased globally since the COVID-19 pandemic, but rarely are there cross-country empirical support for this claim. Using two unique datasets which comprises official domestic violence data from Southern China (N = 152 daily data points from January 1st to May 31st, 2020) and Google Trends data across four English-speaking countries (i.e., Australia, Canada, the United Kingdom, and the United States; N = 728 daily data points from January 1st to June 30th, 2020), we test the association between daily confirmed cases of COVID-19 and daily reports of domestic violence. We find that daily new cases are positively associated with domestic violence in Australia, Canada, the United Kingdom, and the United States, but not in China. However, one nuance of our findings in China is that this association is lagged. We speculate that it is because that China is the first to experience the pandemic during which many people were not acutely aware of or affected by COVID-19. These findings suggest that the COVID-19 health toll is beyond its direct costs on its infectees and provide insights into social policies on public health crises. Governments need to balance their COVID-19 responses with corresponding assistance toward women and children who might be at risk of domestic violence in this difficult time.","","","","","","","","","","","","",""
"Review","Alamo T,Reina DG,Mammarella M,Abella A","","Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic","Electronics","Electronics","2020","9","5","827","COVID Tracking Project","","","","Multidisciplinary Digital Publishing Institute","","","","","2020-05-17","2020-12-08","","","","https://www.mdpi.com/2079-9292/9/5/827;http://dx.doi.org/10.3390/electronics9050827;https://www.mdpi.com/2079-9292/9/5/827/pdf","10.3390/electronics9050827","","","","","We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.","","","","","en","Review","","","","","","",""
"Journal Article","Arik S,Li CL,Yoon J,Sinha R,et al.","","Interpretable Sequence Learning for COVID-19 Forecasting","Advances in","","2020","","","","COVID Tracking Project","","","","proceedings.neurips.cc","","","","","2020","","","","","http://proceedings.neurips.cc/paper/2020/hash/d9dbc51dc534921589adf460c85cd824-Abstract.html;http://proceedings.neurips.cc/paper/2020/file/d9dbc51dc534921589adf460c85cd824-Paper.pdf","","","","","","Page 1. Interpretable Sequence Learning for COVID-19 Forecasting Sercan ¨O. Arık, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev …","","","","","","","","","","","","",""
"Journal Article","Javan EM,Fox SJ,Meyers LA","","Estimating the unseen emergence of COVID-19 in the US","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.06.20053561v4.full-text","","","","","","medRxiv - The Preprint Server for Health Sciences.","","","","","","","","","","","","",""
"Journal Article","Jiménez MC,Cowger TL,Simon LE,Behn M,Cassarino N,Bassett MT","","Epidemiology of COVID-19 Among Incarcerated Individuals and Staff in Massachusetts Jails and Prisons","JAMA Netw Open","JAMA network open","2020","3","8","e2018851","COVID Tracking Project","","","","jamanetwork.com","","","","","2020-08-03","","","2574-3805","","http://dx.doi.org/10.1001/jamanetworkopen.2020.18851;https://www.ncbi.nlm.nih.gov/pubmed/32821919;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442924;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.18851;https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2769617;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2769617","10.1001/jamanetworkopen.2020.18851","32821919","","","PMC7442924","… COVID-19 updates and information. Updated July 22, 2020. Accessed July 11, 2020. https://www.mass.gov/info-details/covid-19-updates-and-information#daily-updates-. 6. The COVID Tracking Project . Accessed July 17, 2020. https://covidtracking.com/.","","","","Division of Women's Health, Brigham and Women's Hospital, Boston, Massachusetts. Department of Medicine, Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. François-Xavier Bagnoud Center for Health and Human Rights, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, Massachusetts.","en","Research Article","","","","","","",""
"Journal Article","Chen LS,Yen MF,Lai CC,Hsu CY,Chen HH","","Easing social distancing index after COVID-19 pandemic","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.11.20128165v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/12/2020.06.11.20128165.full.pdf","","","","","","… reports. Available from: https://www.who.int/emergencies/diseases/novel- coronavirus-2019/ situation-reports (accessed Jun 2, 2020). 14. The COVID Tracking Project . Covid tracking data. Available from: https://github.com/COVID19Tracking/covid-tracking …","","","","","","","","","","","","",""
"Journal Article","Diaz DB,Kinfemichael B,Lorne FT","","COVID-19 Positivity Rate as a measure of the potency of the virus in Humans' War against the virus","jbssrnet.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://jbssrnet.com/wp-content/uploads/2020/11/4-1.pdf","","","","","","… Figure 3: Daily Confirmed COVID-19 Cases, Cumulative Positivity Rate, and Daily Positivity Rate in the United States Source: Johns Hopkins University CSSE (Confirmed cases), The Atlantic: The COVID Tracking Project (Tests performed) …","","","","","","","","","","","","",""
"Preprint Manuscript","Bahloul M,Chahid A,Laleg-Kirati TM","","Fractional-order SEIQRDP model for simulating the dynamics of COVID-19 epidemic","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-04","","","","","http://arxiv.org/abs/2005.01820","","","2005.01820","","","The novel coronavirus disease (COVID-19) is known as the causative virus of outbreak pneumonia initially recognized in the mainland of China, late December 2019. COVID-19 reaches out to many countries in the world, and the number of daily cases continues to increase rapidly. In order to simulate, track, and forecast the trend of the virus spread, several mathematical and statistical models have been developed. Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model is one of the most promising dynamic systems that has been proposed for estimating the transmissibility of the COVID-19. In the present study, we propose a Fractional-order SEIQRDP model to analyze the COVID-19 epidemic. The Fractional-order paradigm offers a flexible, appropriate, and reliable framework for pandemic growth characterization. In fact, fractional-order operator is not local and consider the memory of the variables. Hence, it takes into account the sub-diffusion process of confirmed and recovered cases growth. The results of the validation of the model using real COVID-19 data are presented, and the pertinence of the proposed model to analyze, understand, and predict the epidemic is discussed.","","","","","","","","arXiv","2005.01820","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Lai MJ","","Lily Wang","faculty.sites.iastate.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://faculty.sites.iastate.edu/lilywang/software","","","","","","… We collect the US COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them …","","","","","","","","","","","","",""
"Journal Article","Mavragani A,Gillas K","","On the predictability of COVID-19 in USA: A Google Trends analysis","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-27189/latest.pdf","","","","","","… and Chartsbin [42]. Data for the US analysis on COVID-19 are retrieved by “The COVID Tracking Project ”, providing detailed structured data on COVID-19 cases and deaths nationally and at state level [43]. As Google Trends …","","","","","","","","","","","","",""
"Journal Article","Wieczorek M,Siłka J,Woźniak M","","Neural network powered COVID-19 spread forecasting model","Chaos Solitons Fractals","Chaos, solitons, and fractals","2020","140","","110203","COVID Tracking Project","","","","Elsevier","","","","","2020-11","","","0960-0779","","http://dx.doi.org/10.1016/j.chaos.2020.110203;https://www.ncbi.nlm.nih.gov/pubmed/32834663;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428770;https://linkinghub.elsevier.com/retrieve/pii/S0960-0779(20)30599-3;https://www.sciencedirect.com/science/article/pii/S0960077920305993;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428770/","10.1016/j.chaos.2020.110203","32834663","","","PMC7428770","Virus spread prediction is very important to actively plan actions. Viruses are unfortunately not easy to control, since speed and reach of spread depends on many factors from environmental to social ones. In this article we present research results on developing Neural Network model for COVID-19 spread prediction. Our predictor is based on classic approach with deep architecture which learns by using NAdam training model. For the training we have used official data from governmental and open repositories. Results of prediction are done for countries but also regions to provide possibly wide spectrum of values about predicted COVID-19 spread. Results of the proposed model show high accuracy, which in some cases reaches above 99%.","60G25; 68T05; 68T37; COVID-19; Neural network; Prediction","","","Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland.","en","Research Article","","","","","","",""
"Journal Article","Hinz S,Basam MM,Aguilera KY,LaBarge M","","Internet-based tool for visualizing county and state level COVID-19 trends in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.11.20095851v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/18/2020.05.11.20095851.full.pdf","","","","","","… (2020). County government non-pharmaceutical intervention data were provided by The COVID Tracking Project and is accessible on their website (https://covidtracking.com/ api). State and county population and demographic information …","","","","","","","","","","","","",""
"Journal Article","Godefroy R,Lewis J","","Estimates of COVID-19 Cases Across Canadian Provinces","","","2020","","","","COVID Tracking Project","","","","cireqmontreal.com","","","","","2020","","","","","http://www.cireqmontreal.com/wp-content/uploads/2020/05/COVID-LewisBenatiaGodefroy.pdf","","","","","","… Lu, X., L. Zhang, H. Du, J. Zhang, Y. Li, and et al. 2020. “SARS-CoV-2 Infection in Children.” New England Journal of Medicine, doi: 10.1056. Meyer, R., E. Kissane, and A. Madrigal. 2020. “The COVID Tracking Project .” https://covidtracking.com/. 13 Page 15. Newey, W. 2009 …","","","","","","","","","","","","",""
"Journal Article","Shardlow AM","","The Impact of Social Distancing on The Course of The Covid-19 Pandemic in Four European Countries","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.03.20089680v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/06/2020.05.03.20089680.full.pdf","","","","","","… It certainly does not validate the use of delayed social distancing, nor should it influence its relaxation. References [1] The Covid Tracking Project . The covid tracking project . https://covidtracking.com. Accessed: 2020-05-03. 14 …","","","","","","","","","","","","",""
"Journal Article","Smith J","","Comparing the IHME COVID-19 health service utilization forecasting team's predicted cumulative COVID-19 deaths to actual deaths","Authorea Preprints","","2020","","","","COVID Tracking Project","","","","authorea.com","","","","","2020","","","","","https://www.authorea.com/doi/full/10.22541/au.159674853.37745616;https://www.authorea.com/doi/pdf/10.22541/au.159674853.37745616","","","","","","… as well as the corresponding predicted lower and upper bounds, from the aforementioned medRxiv paper((and Christopher JL Murray, 2020), Table 1). I obtained the actual number of deaths in the United States for each state from the COVID Tracking Project's spreadsheet on …","","","","","","","","","","","","",""
"Journal Article","Asahi K,Undurraga EA,Wagner R","","Benchmarking the CoVID-19 pandemic across countries and states in the USA under heterogeneous testing","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.01.20087882v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/06/2020.05.01.20087882.full.pdf","","","","","","… The COVID Tracking Project . State by state data and annotations. USA. 2020. https://covidtracking. com/data. Accessed April 2020 … 2020. https://bit.ly/2XWhCm2. Accessed April 2020. 9. The COVID Tracking Project . State by state data and annotations. USA. 2020 …","","","","","","","","","","","","",""
"Journal Article","Xiao W,Nethery RC,Sabath BM,Braun D,et al.","","Exposure to air pollution and COVID-19 mortality in the United States","MedRxiv","","2020","","","","COVID Tracking Project","","","","covid-19.conacyt.mx","","","","","2020","","","","","https://covid-19.conacyt.mx/jspui/handle/1000/2858;https://covid-19.conacyt.mx/jspui/bitstream/1000/2858/1/1102845.pdf","","","","","","… datas ets/hospitals) County level number of hospital beds in 2019 The COVID tracking project (https://covidtracking.com/) State level number of COVID-19 tests performed up to and including April 4, 2020 Gridmet via google …","","","","","","","","","","","","",""
"Preprint Manuscript","Cai Y,Goehring G","","The 2020 Sturgis Motorcycle Rally and COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-05","","","","","http://arxiv.org/abs/2009.04917","","","2009.04917","","","The Sturgis Motorcycle Rally that took place from August 7-16 was one of the largest public gatherings since the start of the COVID-19 outbreak. Over 460,000 visitors from across the United States travelled to Sturgis, South Dakota to attend the ten day event. Using anonymous cell phone tracking data we identify the home counties of visitors to the rally and examine the impact of the rally on the spread of COVID-19. Our baseline estimate suggests a one standard deviation increase in Sturgis attendance increased COVID-19 case growth by 1.1pp in the weeks after the rally.","","","","","","","","arXiv","2009.04917","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Bennoune K","","“Lest We Should Sleep”: COVID-19 and Human Rights","Am. J. Int. Law","The American journal of international law","2020","114","4","666-676","COVID Tracking Project","","","","Cambridge University Press","","","","","2020-10","2020-12-08","","0002-9300","2161-7953","https://www.cambridge.org/core/journals/american-journal-of-international-law/article/lest-we-should-sleep-covid19-and-human-rights/598194F12F5ADBBDAB158B2147E22B49;http://dx.doi.org/10.1017/ajil.2020.68","10.1017/ajil.2020.68","","","","","Any meaningful human rights law approach to COVID-19 must be holistic and recognize the breadth of the challenges to both economic, social, and cultural rights, and civil and political rights. It must be grounded in the threat posed by the disease but also address responses to it, and implicate a wide range of state and nonstate actors. Such an analysis should offer a positive vision of effective global reaction, and counter attempts to hijack rights to oppose legitimate pandemic measures.","","","","","","","","","","","","",""
"Preprint Manuscript","Heroy S","","Metropolitan-scale COVID-19 outbreaks: how similar are they?","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-02","","","","","http://arxiv.org/abs/2004.01248","","","2004.01248","","","In this study, we use US county-level COVID-19 case data from January 21-March 25, 2020 to study the exponential behavior of case growth at the metropolitan scale. In particular, we assume that all localized outbreaks are in an early stage (either undergoing exponential growth in the number of cases, or are effectively contained) and compare the explanatory performance of different simple exponential and linear growth models for different metropolitan areas. While we find no relationship between city size and exponential growth rate (directly related to $R0$, which denotes average the number of cases an infected individual infects), we do find that larger cities seem to begin exponential spreading earlier and are thus in a more advanced stage of the pandemic at the time of submission. We also use more recent data to compute prediction errors given our models, and find that in many cities, exponential growth models trained on data before March 26 are poor predictors for case numbers in this more recent period (March 26-30), likely indicating a reduction in the number of new cases facilitated through social distancing.","","","","","","","","arXiv","2004.01248","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Ng G,Wang C","","The proportion testing positive for SARS-COV-2 among the tested population in the US: Benefits of the positive test ratio under scaled testing scenarios","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.21.20074070v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/26/2020.04.21.20074070.full.pdf","","","","","","… incidence—United States, February 12–April 7, 2020. MMWR Morb Mortal Wkly Rep 2020;69:465-71. https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e4.htm?s_cid= mm6915e4_x 4. The COVID Tracking Project . https://covidtracking.com …","","","","","","","","","","","","",""
"Journal Article","Stafford EG,Riviere J,Xu X,Gedara NIM,Kawakami J,et al.","","Pulmonary Adverse Event Data in Hypertension with Implications on COVID-19 Morbidity","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-37854/latest.pdf","","","","","","Page 1. TITLE: Pulmonary Adverse Event Data in Hypertension with Implications on COVID-19 Morbidity 1DATA Consortium Author Title Affiliation Emma G. Stafford, PharmD Teaching Assistant Professor University of Missouri-Kansas City School of Pharmacy …","","","","","","","","","","","","",""
"Journal Article","Mayer JD,Lewis ND","","An inevitable pandemic: geographic insights into the COVID-19 global health emergency","Eurasian Geography and Economics","Eurasian Geography and Economics","2020","61","4-5","404-422","COVID Tracking Project","","","","Routledge","","","","","2020-09-02","","","1538-7216","","https://doi.org/10.1080/15387216.2020.1786425;http://dx.doi.org/10.1080/15387216.2020.1786425;https://rsa.tandfonline.com/doi/abs/10.1080/15387216.2020.1786425?casa_token=SeKqhIAho5YAAAAA:Wnzu34GhkAiCtfdd1JH1Avu5ho8QjNppfGzk4lMz8P4I3KtkcWqNECLVSHNbLPrT8KKmiDSe42OD;https://www.tandfonline.com/doi/pdf/10.1080/15387216.2020.1786425?casa_token=tw7R_ZFIyjMAAAAA:vaUGn-eiKtwPOYSM4pcV_4pDfbv6eRYQ8_hp479Qexxe6L7JuIB1nj2Yj2J-qlHhIYEmer5X5wLO","10.1080/15387216.2020.1786425","","","","","ABSTRACT The emergence of SARS-CoV-2 and the resulting disease, COVID-19, in China in late 2019 provides unique and sometimes overwhelming challenges to global public health and to the health and well-being of populations. The World Health Organization?s subsequent declaration of this outbreak as a public health emergency of international importance was, if anything, an understatement. The emergence of this pathogen is best seen as the latest and globally one of the most important examples of emerging infectious diseases ? a phenomenon that has been at the forefront of global health for several decades. We trace the initial experience with COVID-19, highlighting experiences in Europe, Asia, and the Pacific. We emphasize selected countries, with varied demographic, socioeconomic, and political profiles, and identify instructive lessons from these national and regional experiences in terms of the efficacy of responses. Previous experience with serious epidemics of emerging infectious disease, well developed public health infrastructure, early, well-coordinated and transparent communication, rapidly established surveillance, case ascertainment, contact tracing, and containment are all vitally important in the control of this disease.","","","","","","","","","","","","",""
"Preprint Manuscript","Leifheit KM,Linton SL,Raifman J,Schwartz G,Benfer EA,Zimmerman FJ,Pollack C","","Expiring Eviction Moratoriums and COVID-19 Incidence and Mortality","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-30","2020-12-08","","","","https://papers.ssrn.com/abstract=3739576;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3739576;http://dx.doi.org/10.2139/ssrn.3739576;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3739576","10.2139/ssrn.3739576","","","","","Background: The COVID-19 pandemic and associated economic crisis has rendered millions of U.S. households unable to pay rent, placing them at risk for eviction. Evictions may accelerate COVID-19 transmission by increasing household crowding and decreasing individuals’ ability to comply with social distancing directives. We leveraged variation in the expiration of eviction moratoriums in U.S. states to test for associations between evictions and COVID-19 incidence and mortality. Methods: The study included 44 U.S. states that instituted eviction moratoriums., followed from March 13th to September 3rd, 2020. We modeled associations using a difference-in-difference approach with an event study specification. Negative binomial regression models of cases and deaths included fixed effects for state and week and controlled for time-varying indicators of testing, stay-at-home orders, school closures, and mask mandates. We then used model predictions to estimate cumulative cases and deaths associated with expiring eviction moratoriums. Findings: Twenty-seven states lifted eviction moratoriums during the study period. COVID-19 incidence in states that lifted their moratoriums was 1.6 (95% CI 1.0,2.3) times the incidence of states that maintained their moratoriums at 10 weeks post-lifting and grew to a ratio of 2.1 (CI 1.1,3.9) at ≥16 weeks. Mortality in states that lifted their moratoriums was 1.6 (CI 1.2,2.3) times the mortality of states that maintained their moratoriums at 7 weeks post-lifting and grew to a ratio of 5.4 (CI 3.1,9.3) at ≥16 weeks. These results translate to an estimated 433,700 excess cases (CI 365200,502200) and 10,700 excess deaths (CI 8900,12500) nationally.Interpretation: Lifting eviction moratoriums was associated with increased COVID-19 incidence and mortality, supporting the public health rationale for use of eviction moratoriums to prevent the spread of COVID-19.","COVID-19; Pandemic; SARS-CoV-2; housing; health; moratoriums; moratoria; eviction; homelessness; health equity; health justice; health inequity; policy; housing instability","","","","","","","","","","","","Available at SSRN"
"Preprint Manuscript","Mehrabadi MA,Dutt N,Rahmani AM","","The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-27","","","","","http://arxiv.org/abs/2007.13255","","","2007.13255","","","The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of .004 for Granger's causality test. Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.","","","","","","","","arXiv","2007.13255","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Zuo X,Chen Y,Ohno-Machado L,Xu H","","How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles","Brief. Bioinform.","Briefings in bioinformatics","2020","","","","COVID Tracking Project","","","","Oxford University Press","","","","","2020-12-02","2020-12-08","","1467-5463","","https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbaa331/6015891;http://dx.doi.org/10.1093/bib/bbaa331;https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbaa331/6015891?casa_token=QIvNvFOvp7MAAAAA:WHAcD9UyvR2izr6MCwIH1mU3TpVMdGy74ywnh3x4nDvwBdiFe_bQMQ5Budz2I_4otliyEOS9g-SX","10.1093/bib/bbaa331","","","","","AbstractObjective. This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitativ","","","","","en","","","","","","","",""
"Preprint Manuscript","Pang J,Li J,Xie Z,Huang Y,Cai Z","","Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-05","","","","","http://arxiv.org/abs/2011.02883","","","2011.02883","","","In this work, we propose a collaborative city digital twin based on FL, a novel paradigm that allowing multiple city DT to share the local strategy and status in a timely manner. In particular, an FL central server manages the local updates of multiple collaborators (city DT), provides a global model which is trained in multiple iterations at different city DT systems, until the model gains the correlations between various response plan and infection trend. That means, a collaborative city DT paradigm based on FL techniques can obtain knowledge and patterns from multiple DTs, and eventually establish a `global view' for city crisis management. Meanwhile, it also helps to improve each city digital twin selves by consolidating other DT's respective data without violating privacy rules. To validate the proposed solution, we take COVID-19 pandemic as a case study. The experimental results on the real dataset with various response plan validate our proposed solution and demonstrate the superior performance.","","","","","","","","arXiv","2011.02883","cs.LG","","","arXiv [cs.LG]"
"Journal Article","IHME COVID-19 Forecasting Team","","Modeling COVID-19 scenarios for the United States","Nat. Med.","Nature medicine","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020-10-23","","","1078-8956","1546-170X","http://dx.doi.org/10.1038/s41591-020-1132-9;https://www.ncbi.nlm.nih.gov/pubmed/33097835;https://doi.org/10.1038/s41591-020-1132-9;https://www.researchgate.net/profile/Simon_Hay/publication/346381819_Modeling_COVID-19_scenarios_for_the_United_States/links/5fc5894aa6fdcce95268fd58/Modeling-COVID-19-scenarios-for-the-United-States.pdf","10.1038/s41591-020-1132-9","33097835","","","","We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state-with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)-suggest that, cumulatively, 511,373 (469,578-578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284-170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731-133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario.","","","","","en","Research Article","","","","","","",""
"Preprint Manuscript","Khajenoori Y,Kamil L,Bhattacharjee J,Feng E,Pal S","","Comparative analysis of state public health measures and number of COVID-19 cases in California, Texas, and New York (April to July 2020)","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08","","","","","http://dx.doi.org/10.31219/osf.io/kycz5;https://osf.io/preprints/kycz5/;https://osf.io/kycz5/download?format=pdf","10.31219/osf.io/kycz5","","","","","In response to the spread of COVID-19 in the United States, every state has utilized varying degrees of public health policies yielding different trends in the number of cases. Due to the lack of a unified approach taken in response to the global pandemic in the United States, we can look at the general trends in case numbers from different states in the context of the public health measures that have been implemented. Through the use of multiple databases, we collected data from each states health department websites and policy data came from the COVID-19 US State Policy Database on the CDR, as well as the KFF state policy database in order to graph the number of daily new cases in three different states while marking the dates when the certain policies were implemented. The scope of this particular review focuses on California, New York, and Texas, each of which have taken different approaches and are reflective of three different areas of the continental United States. The four policies that are analyzed include shelter in place orders, mask mandates, the closure and reopening of non-essential businesses, and the closure and reopening of restaurants for in person dining. To further understand the reopening strategies of these three states, we have utilized the “National Coronavirus Response: A Roadmap to Reopening” guide to compare the points at which each state decided to open considering testing capacity, contact tracing, and case numbers/trend in cases at that point in time. Based on this data, we comparatively analyzed trends in cases and policy measures, taking into account other factors like tracing and testing capacity to evaluate the appropriateness of each state’s measures in its overall goal of reopening. Overall, we have found New York which began as the hotspot for COVID-19 cases, to ultimately be the most successful state in regard to reducing the number of daily new cases and surpassing goals for contact tracing and testing. Conversely California, which began as a success story, has seen a sharp rise in cases after moving into phases of reopening. Similarly, Texas has also seen a rise in cases over recent months with the relaxation of public health measures before meeting the markers for reopening. Both California and Texas have been far behind on testing and contact tracing capabilities. Not only abiding by public health policy recommendations but also being consistent with these measures throughout the course of the pandemic are correlated with lower numbers of cases when comparing New York with California and Texas. This finding implies that for future pandemics, and moving forward with the current pandemic, extreme caution should be taken in timing public health measures and tracking cases.","California; coronavirus; COVID-19; New York; pandemic; public health; Texas","","","","","","","","","","","",""
"Journal Article","Yuan HY,Wu L,Wang DP","","Surges in COVID-19 are led by lax government interventions in initial outbreaks","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.17.20156604v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/23/2020.07.17.20156604.full.pdf","","","","","","… clinical practice. Page 2. We obtained the daily new cases from “The COVID Tracking Project ”, https://covidtracking.com/, for the fourteen states of among the highest total cases in the United States. The study period is from the …","","","","","","","","","","","","",""
"Journal Article","Troske K,Coomes P","","Measuring the Spread of COVID-19 in Kentucky: Do We Have the Right Data?","","","2020","","","","COVID Tracking Project","","","","isfe.uky.edu","","","","","2020","","","","","http://isfe.uky.edu/sites/ISFE/files/research-pdfs/WITH%20CP%20Measuring%20the%20spread%20the%20COVID-19%20in%20KY%20-%20Ken%20and%20Paul_0.pdf","","","","","","… III. Data used to measure the spread of the virus in Kentucky Most of the data used in this paper come from the COVID Tracking Project (https://covidtracking.com/). The COVID Tracking Project is a voluntary organization that arose out of an …","","","","","","","","","","","","",""
"Journal Article","Price GN,van Holm E","","The Effect of Social Distancing On The Spread of Novel Coronavirus: Estimates From Linked State-Level Infection And American Time Use Survey Data","researchgate.net","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.researchgate.net/profile/Gregory_Price2/publication/340460649_The_Effect_of_Social_Distancing_On_The_Spread_of_Novel_Coronavirus_Estimates_From_Linked_State-Level_Infection_And_American_Time_Use_Survey_Data/links/5e8b3a7b92851c2f5283b176/The-Effect-of-Social-Distancing-On-The-Spread-of-Novel-Coronavirus-Estimates-From-Linked-State-Level-Infection-And-American-Time-Use-Survey-Data.pdf","","","","","","… potentially expose them to crowds from the American Time Use Survey linked with state-level data on positive tests from the COVID Tracking Project . We estimate … Use Survey linked with state-level data on positive tests from the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Kolker E","","Inform, Predict, and Monitor Reopening Decisions after COVID-19 Outbreak","Screening","Screening: journal of the International Society of Neonatal Screening","","","","","COVID Tracking Project","","","","alexander-analytics.com","","","","","","","","0925-6164","","https://www.alexander-analytics.com/community","","","","","","","","","","","","","","","","","","",""
"Journal Article","Fabic MS,Choi Y,Bishai D","","Deaths among COVID Cases in the United States: Racial and Ethnic Disparities Persist","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.11.15.20232066v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/17/2020.11.15.20232066.full.pdf","","","","","","… preprint Page 6. Page 6 of 6 References 1 The COVID Tracking Project . Infection and Mortality by Race and Ethnicity. Available: https://covidtracking.com/race/infection- and-mortality-data [Accessed: 26-Oct-2020] 2 Zakeri R, et. al …","","","","","","","","","","","","",""
"Journal Article","Soubeyrand S,Ribaud M,Baudrot V,Allard D,Pommeret D,Roques L","","COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s)","PLoS One","PloS one","2020","15","9","e0238410","COVID Tracking Project","","","","journals.plos.org","","","","","2020-09-11","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0238410;https://www.ncbi.nlm.nih.gov/pubmed/32915815;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485826;https://dx.plos.org/10.1371/journal.pone.0238410;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238410","10.1371/journal.pone.0238410","32915815","","","PMC7485826","Discrepancies in population structures, decision making, health systems and numerous other factors result in various COVID-19-mortality dynamics at country scale, and make the forecast of deaths in a country under focus challenging. However, mortality dynamics of countries that are ahead of time implicitly include these factors and can be used as real-life competing predicting models. We precisely propose such a data-driven approach implemented in a publicly available web app timely providing mortality curves comparisons and real-time short-term forecasts for about 100 countries. Here, the approach is applied to compare the mortality trajectories of second-line and front-line European countries facing the COVID-19 epidemic wave. Using data up to mid-April, we show that the second-line countries generally followed relatively mild mortality curves rather than fast and severe ones. Thus, the continuation, after mid-April, of the COVID-19 wave across Europe was likely to be mitigated and not as strong as it was in most of the front-line countries first impacted by the wave (this prediction is corroborated by posterior data).","","","","INRAE, BioSP, Avignon, France. Univ Lyon, UCBL, ISFA LSAF EA2429, Lyon, France.","en","Research Article","","","","","","",""
"Journal Article","Nakamura T","","The Impact of Rapid State Policy Response on Cumulative Deaths Caused by COVID-19","the-iyrc.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.the-iyrc.org/uploads/1/2/9/7/129787256/iyrc2020_-_takuto_nakamura.pdf","","","","","","… Using data from the COVID Tracking Project , the team graphed the log of confirmed cases of multiple counties and analysed the correlation between the counties that implemented stay-at- home orders and ones that didn't. The team found that, overall, counties that implemented …","","","","","","","","","","","","",""
"Journal Article","Lu FS,Nguyen AT,Link NB,Davis JT,Chinazzi M,et al.","","Estimating the cumulative incidence of COVID-19 in the United States using four complementary approaches","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.18.20070821v3.full.pdf","","","","","","… Counts are taken up until May 28, 2020. COVID-19 Testing Counts: In addition, daily time series containing positive and negative COVID-19 test results within each state were obtained from the COVID Tracking Project [45]. 13 …","","","","","","","","","","","","",""
"Journal Article","Lengerich BJ,Neiswanger W,Lengerich EJ,Xing EP","","Disentangling Increased Testing From Covid-19 Epidemic Spread","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.09.20141762v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/10/2020.07.09.20141762.full.pdf","","","","","","… Page 8. Data Availability Test count data is collected by the Covid Tracking Project 9. For each state, we set the population N according to the 2019 state population estimates projected from the 2010 US Census10. References …","","","","","","","","","","","","",""
"Journal Article","Hersh W","","COVID-19 and Informatics","dmice.ohsu.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://dmice.ohsu.edu/hersh/acofp-20-covid.pdf","","","","","","… Johns Hopkins University Center for Systems Science and Engineering – https://coronavirus. jhu.edu/map.html • University of Washington Institute for Health Metrics and Evaluation – https://covid19.healthdata.org/ • COVID Tracking Project – https://covidtracking.com/ • Our …","","","","","","","","","","","","",""
"Journal Article","Rousseau EA","","Analyzing the Response to COVID-19 in Pennsylvania State Correctional Institutions","","","2020","","","","COVID Tracking Project","","","","cupola.gettysburg.edu","","","","","2020","2020-12-08","","","","https://cupola.gettysburg.edu/student_scholarship/869/;https://cupola.gettysburg.edu/cgi/viewcontent.cgi?article=1947&context=student_scholarship","","","","","","To evaluate the effects of the COVID-19 outbreak on the Pennsylvania prison system, I collected data from the Pennsylvania Department of Corrections, the Pennsylvania Department of Health, and the Marshall Project. I supplemented the data with opinion pieces and journal articles discussing the specific issues that this pandemic imposes upon prisons in the United States. In sum, population data collected from the Department of Corrections showed that only a few State Correctional Institutions saw a steady decrease in population over the 25 day study period from from June 18th through July 13th (Pennsylvania Department of Corrections, 2020). Combining testing data collected in part from the DOC and in part from the Department of Health, I compared testing rates of surrounding communities (Pennsylvania Department of Health, 2020 & PA DOC, 2020). The Marshall Project data demonstrates that testing rates are higher and COVID-19 diagnosis rates are lower among incarcerated persons than among the general population (The Marshall Project, 2020). However, this finding did not hold true for correctional staff, who had a higher rate of diagnosis coupled with a lower testing rate compared to all of Pennsylvania (The Marshall Project, 2020).","","","","","","","Student Publications","","","","","",""
"Journal Article","Pathak I,Choi Y,Jiao D,Yeung D,Liu L","","Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-10-04","","","","","http://dx.doi.org/10.1101/2020.10.01.20205377;https://www.ncbi.nlm.nih.gov/pubmed/33024984;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536886;https://doi.org/10.1101/2020.10.01.20205377;https://www.medrxiv.org/content/10.1101/2020.10.01.20205377v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/04/2020.10.01.20205377.full.pdf","10.1101/2020.10.01.20205377","33024984","","","PMC7536886","IMPORTANCE: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). OBJECTIVE: We filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. Design/Setting/Participants/Exposure: We conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. Main Outcome and Measure: Our main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. RESULTS: We found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic populations age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. CONCLUSIONS AND RELEVANCE: The availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic.","","","","","en","Research Article","","","","","","",""
"Journal Article","Barret JP,Chong SJ,Depetris N,Fisher MD,Luo G,Moiemen N,Pham T,Qiao L,Wibbenmeyer L,Matsumura H","","Burn center function during the COVID-19 pandemic: An international multi-center report of strategy and experience","Burns","Burns: journal of the International Society for Burn Injuries","2020","46","5","1021-1035","COVID Tracking Project","","","","Elsevier","","","","","2020-08","","","0305-4179","1879-1409","http://dx.doi.org/10.1016/j.burns.2020.04.003;https://www.ncbi.nlm.nih.gov/pubmed/32416984;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151262;https://linkinghub.elsevier.com/retrieve/pii/S0305-4179(20)30274-6;https://www.sciencedirect.com/science/article/pii/S0305417920302746;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151262/","10.1016/j.burns.2020.04.003","32416984","","","PMC7151262","The novel coronavirus, SARS-CO V2 responsible for COVID-19 pandemic is rapidly escalating across the globe. Burn centers gearing for the pandemic must strike a balance between contributing to the pandemic response and preserving ongoing burn care in a safe and ethical fashion. The authors of the present communication represent seven burn centers from China, Singapore, Japan, Italy, Spain, the United Kingdom (UK), and the United States (US). Each center is located at a different point along the pandemic curve and serves different patient populations within their healthcare systems. We review our experience with the virus to date, our strategic approach to burn center function under these circumstances, and lessons learned. The purpose of this communication is to share experiences that will assist with continued preparations to help burn centers advocate for optimum burn care and overcome challenges as this pandemic continues.","Austere conditions; Burn surgery; COVID-19; Critical care; SARS-COV2","","","Department of Plastic Surgery and Burns, Hospital Universitari Vall d'Hebron, Department of Surgery, School of Medicine, Universitat Autonoma de Barcelona, Passeig de la Vall d'Hebron 119-129, 08035 Barcelona, Spain. Electronic address: jpbarret@vhebron.net. Department of Plastic Reconstructive and Aesthetic surgery, Singapore General Hospital, Academia 20 College Road, Singapore 169856, Singapore. Electronic address: chong_si_jack@hotmail.com. Anaesthesia and Intensive Care, Città della Salute di Torino, corso Bramante, 88-10126, Torino, Italy. Electronic address: nadia.depetris@gmail.com. Division of Plastics and Reconstructive Surgery, Department of Surgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA 52240 USA. Electronic address: mark-d-fisher@uiowa.edu. Institute of Burn Research, Southwest Hospital Army (Third Military) Medical University, Chongqing 400038, China. Electronic address: logxw@yahoo.com. University Hospitals Birmingham Foundation Trust, (Heritage Building) Queen Elizabeth Hospital, Mindelsohn Way, Edgbaston, Birmingham B15 2TH, UK. Electronic address: nmoiemen@aol.com. Harborview Medical Center, 325 Ninth Ave, Box 359796, Seattle, WA, USA. Electronic address: tpham94@uw.edu. Department of Burn and Plastic Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, 197 Ruijin Er Road, Shanghai 200025, China. Electronic address: ql10727@rjh.com.cn. Division of Acute Care Surgery, Department of Surgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, USA. Electronic address: lucy-wibbenmeyer@uiowa.edu. Department of Plastic and Reconstructive Surgery, Tokyo Medical University, 6-7-1 Nishishinjyuku, Shinjukuku, Tokyo, 160-0023, JAPAN. Electronic address: hmatsu-tki@umin.ac.jp.","en","Research Article","","","","","","",""
"Journal Article","Ellis C,Jacobs M,Keene K,Bell R,Dickerson D","","COVID-19 and the Potential Devastation of Rural Communities: Concern from the Southeastern Belts","","","2020","","","","COVID Tracking Project","","","","deepblue.lib.umich.edu","","","","","2020","","","","","https://deepblue.lib.umich.edu/handle/2027.42/154715;https://deepblue.lib.umich.edu/bitstream/handle/2027.42/154715/Ellis_DeepBlue_article.pdf?sequence=1","","","","","","… 2. Mervosh S, Lu D, Swales V. See which states and cities have told residents to stay at home. New York Times. April 1, 2020. Available at: https://www.nytimes.com/interactive/2020/us/ coronavirus-stay-at-home-order.html. 3. JThe Covid Tracking Project . 2020 …","","","","","","","","","","","","",""
"Journal Article","April B","","PEER-REVIEWED","stacks.cdc.gov","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://stacks.cdc.gov/view/cdc/94303;https://stacks.cdc.gov/view/cdc/94303/cdc_94303_DS1.pdf","","","","","","… assumptions. Methods: Analysis using data from the COVID Tracking Project to assess estimated cumulative SARS-CoV-2 infections by state and evaluate contributions of incomplete testing and imperfect test performance. Used …","","","","","","","","","","","","",""
"Journal Article","Yi SH,See I,Kent AG,Vlachos N,Whitworth JC,Xu K,Gouin KA,Zhang S,Slifka KJ,Sauer AG,Kutty PK,Perz JF,Stone ND,Stuckey MJ","","Characterization of COVID-19 in Assisted Living Facilities - 39 States, October 2020","MMWR Morb. Mortal. Wkly. Rep.","MMWR. Morbidity and mortality weekly report","2020","69","46","1730-1735","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-11-20","","","0149-2195","1545-861X","http://dx.doi.org/10.15585/mmwr.mm6946a3;https://www.ncbi.nlm.nih.gov/pubmed/33211679;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676639;https://doi.org/10.15585/mmwr.mm6946a3;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676639/","10.15585/mmwr.mm6946a3","33211679","","","PMC7676639","The coronavirus disease 2019 (COVID-19) pandemic has highlighted the vulnerability of residents and staff members in long-term care facilities (LTCFs) (1). Although skilled nursing facilities (SNFs) certified by the Centers for Medicare & Medicaid Services (CMS) have federal COVID-19 reporting requirements, national surveillance data are less readily available for other types of LTCFs, such as assisted living facilities (ALFs) and those providing similar residential care. However, many state and territorial health departments publicly report COVID-19 surveillance data across various types of LTCFs. These data were systematically retrieved from health department websites to characterize COVID-19 cases and deaths in ALF residents and staff members. Limited ALF COVID-19 data were available for 39 states, although reporting varied. By October 15, 2020, among 28,623 ALFs, 6,440 (22%) had at least one COVID-19 case among residents or staff members. Among the states with available data, the proportion of COVID-19 cases that were fatal was 21.2% for ALF residents, 0.3% for ALF staff members, and 2.5% overall for the general population of these states. To prevent the introduction and spread of SARS-CoV-2, the virus that causes COVID-19, in their facilities, ALFs should 1) identify a point of contact at the local health department; 2) educate residents, families, and staff members about COVID-19; 3) have a plan for visitor and staff member restrictions; 4) encourage social (physical) distancing and the use of masks, as appropriate; 5) implement recommended infection prevention and control practices and provide access to supplies; 6) rapidly identify and properly respond to suspected or confirmed COVID-19 cases in residents and staff members; and 7) conduct surveillance of COVID-19 cases and deaths, facility staffing, and supply information (2).","","","","CDC COVID-19 Response Team.","en","Research Article","","","","","","",""
"Journal Article","Devasia JT,Lakshminarayanan S,et al.","","How Modern Geographical Information Systems Based Mapping and Tracking Can Help to Combat Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 …","International Journal of","","2020","","","","COVID Tracking Project","","","","ijhsir.ahsas-pgichd.org","","","","","2020","","","","","https://ijhsir.ahsas-pgichd.org/index.php/ijhsir/article/view/64;https://ijhsir.ahsas-pgichd.org/index.php/ijhsir/article/download/64/74","","","","","","… Prevention (CDC) [33], European Centre for Disease Prevention and Control (ECDC) [34], National Health Commission of People Republic of China (NHC) [35], DXY.DX Doctor (DXY) [36], 1point3acres [37], Worldometers.info [38], BNO [39], and the COVID Tracking Project [40 …","","","","","","","","","","","","",""
"Journal Article","Mohler G,Short MB,Schoenberg F,Sledge D","","Analyzing the impacts of public policy on COVID-19 transmission in Indiana: The role of model and dataset selection","academia.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://www.academia.edu/download/63187241/Analyzing_the_Impacts_of_Public_Policy_on_COVID_19_Transmission_in_Indiana_revision20200503-68855-16gwogx.pdf","","","","","","… We then apply these models to both daily case and mortality COVID- 19 data in Indiana from three different sources: a COVID-19 data portal in wide use hosted at Johns Hopkins University [11] (abbreviated as “jhu” throughout), the Covid Tracking project [1] (abbreviated as …","","","","","","","","","","","","",""
"Journal Article","Edgar-Tanzil A","","Designing for a Pandemic: A Hands-Free Soap Dispenser","","","2020","","","","COVID Tracking Project","","","","cedar.wwu.edu","","","","","2020","2020-12-08","","","","https://cedar.wwu.edu/wwu_honors/409/;https://cedar.wwu.edu/cgi/viewcontent.cgi?article=1409&context=wwu_honors","","","","","","During the last quarter of 2019, a collection of unusual pneumonia cases went from a local concern to a global pandemic in a matter of 70 days. The infamous Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the virus that was first reported in Wuhan, China on December 31, 2019,and was announced as a pandemic by the World Health Organization on March 11, 2020. [1] The virus has propelled the entire globe into a state of panic as the Covid-19 disease-spread has been quick and relentless. With panic setting in full-force, one phrase has been repeated over-and-over: 洗你的手. помой свои руки. Lava tus manos. 손을 씻으십시오. Lavati le mani. Cuci tanganmu. was je handen. WASH YOUR HANDS. Media outlet after media outlet has echoed the CDC’s calls to action.[2] Aside from wearing masks, doctors and researchers have claimed that proper hand washing could help flatten the curve in areas that have been hit the hardest. The United States, Spain, and Italy are leading the world with the most Covid-19 cases, with the US taking first at 424,945 confirmed positive results and 12,902 deaths. [3] The issue with relying on hand washing to help flatten the curve is the simple fact that many do not wash their hands properly.[4] The goal of the project was to create a user-friendly device that corrects our hygienic routines, which could hopefully help prevent or sideline future pandemics. The project looked at current soap dispenser designs and worked to improve the design feature to enhance the cleansing experience. The research and analysis of the survey studies showed the public would be more receptive to devices that correct our hand washing habits in public venues. Several styles were sketched and refined in order to narrow down the aesthetic—a square shaped unit that is both modern and utilitarian. After making a cardboard prototype of the dispenser and testing the function and scale, it was further refined to meet user needs. The soap dispenser is equipped with a hands-free proximity sensor that triggers the soap to dispense, while timing the user’s handwashing for precisely 20 seconds, as recommended by the CDC. The dispenser features two timers for full accessibility, as required in public restrooms. There is an audio timer that increases in volume intensity to ensure the user will remain for the entire duration of the time, as well as a set of 5 lights to time 4","","","","","","","WWU Honors Program Senior Projects","","","","","",""
"Journal Article","Agapito G,Zucco C,Cannataro M","","COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2020","17","15","","COVID Tracking Project","","","","mdpi.com","","","","","2020-08-03","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph17155596;https://www.ncbi.nlm.nih.gov/pubmed/32756428;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432400;https://www.mdpi.com/resolver?pii=ijerph17155596;https://www.mdpi.com/1660-4601/17/15/5596;https://www.mdpi.com/1660-4601/17/15/5596/pdf","10.3390/ijerph17155596","32756428","","","PMC7432400","The management of the COVID-19 pandemic presents several unprecedented challenges in different fields, from medicine to biology, from public health to social science, that may benefit from computing methods able to integrate the increasing available COVID-19 and related data (e.g., pollution, demographics, climate, etc.). With the aim to face the COVID-19 data collection, harmonization and integration problems, we present the design and development of COVID-WAREHOUSE, a data warehouse that models, integrates and stores the COVID-19 data made available daily by the Italian Protezione Civile Department and several pollution and climate data made available by the Italian Regions. After an automatic ETL (Extraction, Transformation and Loading) step, COVID-19 cases, pollution measures and climate data, are integrated and organized using the Dimensional Fact Model, using two main dimensions: time and geographical location. COVID-WAREHOUSE supports OLAP (On-Line Analytical Processing) analysis, provides a heatmap visualizer, and allows easy extraction of selected data for further analysis. The proposed tool can be used in the context of Public Health to underline how the pandemic is spreading, with respect to time and geographical location, and to correlate the pandemic to pollution and climate data in a specific region. Moreover, public decision-makers could use the tool to discover combinations of pollution and climate conditions correlated to an increase of the pandemic, and thus, they could act in a consequent manner. Case studies based on data cubes built on data from Lombardia and Puglia regions are discussed. Our preliminary findings indicate that COVID-19 pandemic is significantly spread in regions characterized by high concentration of particulate in the air and the absence of rain and wind, as even stated in other works available in literature.","Italian COVID-19 data; climate data; data analysis; data integration; data warehouse; pollution data","","","Department of Legal, Economic and Social Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy. Data Analytics Research Center, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy;. Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy.","en","Research Article","","","","","","",""
"Journal Article","Ford DD","","Re: Gaiha et al, 2020; https://doi. org/10.1016/j. jadohealth. 2020.07. 002","","","2020","","","","COVID Tracking Project","","","","qeios-uploads.s3.eu-west-1 …","","","","","2020","","","","","https://qeios-uploads.s3.eu-west-1.amazonaws.com/publication-ancillary-files/A58MQC/20200907-letter-to-editor-jah-9-06-2020-finalsigned-for-qeios.pdf","","","","","","… Yet, the US had conducted less than 10.4 million tests (The COVID Tracking Project , 2020) by that date … BMJ. 2014;349:g7015. Available at: https://www.ncbi.nlm.nih.gov/pubmed/25498121 The COVID Tracking Project . The COVID Tracking Project at The Atlantic …","","","","","","","","","","","","",""
"Preprint Manuscript","Dutta M","","COVID-19 and Impact of School Closures on the Children of the United States; a Point of View with an Empirical Analysis","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-12","2020-12-08","","","","https://papers.ssrn.com/abstract=3596096;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3596096;http://dx.doi.org/10.2139/ssrn.3596096;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3596096","10.2139/ssrn.3596096","","","","","School closures due to the global pandemic of the novel coronavirus have impacted at least 124,000 U.S. public and private schools and affected at least 55.1 million students (Education Week, 2020b). As the COVID-19 hotspots continue to emerge across the United States, this study aims to understand the impact of school closures during the stay at home order on the health behavior of the school-age children as it relates to their mobility, physical activity, access to school meal services and psychological distress caused by the prolonged social distancing. The first segment of the study discusses the several aspects of social distancing and its effects on physiological and psychological health of the children. The following segment of the study presents an empirical analysis with the latest COVID-19 tracking results highlighting the different states of the USA at different stages of the novel Coronavirus outbreak. The empirical analysis is further expanded based on the publicly available state level data on school closures, childhood obesity rate and key socio-economic factors, including access to the playgrounds in neighborhood, walk score, prevalence of poverty and latest filing of the unemployment claims during the pandemic to identify the states at greater vulnerability. The concluding section discusses the need to advance the research further with future recommendations as the full impact of the pandemic continues to unfold.","Coronavirus, COVID19, Epidemic, pandemic, Infodemic, Social distancing","","","","","","","","","","","","papers.ssrn.com"
"Journal Article","Li Y,Wu Y,Wang X,Guo C,Wang L,Li J,Wang S","","Modelling the effect of lockdown on COVID-19 pandemic in 22 countries and cities","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-32891/latest.pdf","","","","","","… Page 5. the United Kingdom. (2) The COVID Tracking Project [33] gave the confirmed COVID-19 daily … Serial interval of COVID-19 among publicly reported confirmed cases. Emerg Infect Dis 2020; 26(6). [33] The COVID Tracking Project . Data API …","","","","","","","","","","","","",""
"Journal Article","Carson RT,Carson SL,Dye TK,Mayfield SL,Moyer DC,et al.","","COVID-19's US Temperature Response Profile","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.11.03.20225581v5.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/09/2020.11.03.20225581.full.pdf","","","","","","… counts matters. Panel (A) shows the originally reported ( COVID Tracking Project [CPT]) CPTDailyDeadit daily death counts for (Florida) in blue with the “Actual” death counts by death certificate date overlaid in red. Actual curves …","","","","","","","","","","","","",""
"Journal Article","Matzinger P,Skinner J","","Strong impact of closing schools, closing bars and wearing masks during the Covid-19 pandemic: results from a simple and revealing analysis","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-09-28","","","","","http://dx.doi.org/10.1101/2020.09.26.20202457;https://www.ncbi.nlm.nih.gov/pubmed/33024976;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536875;https://doi.org/10.1101/2020.09.26.20202457;https://www.medrxiv.org/content/10.1101/2020.09.26.20202457v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/28/2020.09.26.20202457.full.pdf","10.1101/2020.09.26.20202457","33024976","","","PMC7536875","Many complex mathematical and epidemiological methods have been used to model the Covid-19 pandemic. Among other results from these models has been the view that closing schools had little impact on infection rates in several countries1. We took a different approach. Making one assumption, we simply plotted cases, hospitalizations and deaths, on a log2 Y axis and a linear date-based X axis, and analyzed them using segmented regression, a powerful method that has largely been overlooked during this pandemic. Here we show that the data fit straight lines with correlation coefficients ranging from 92% - 99%, and that these lines broke at interesting intervals, revealing that school closings dropped infection rates in half, lockdowns dropped the rates 3 to 4 fold, and other actions (such as closing bars and mandating masks) brought the rates even further down. Hospitalizations and deaths paralleled cases, with lags of three to ten days. The graphs, which are easy to read, reveal changes in infection rates that are not obvious using other graphing methods, and have several implications for modeling and policy development during this and future pandemics. Overall, other than full lockdowns, three interventions had the most impact: closing schools, closing bars and wearing masks: a message easily understood by the public.","","","","","en","Research Article","","","","","","",""
"Journal Article","Vuksanaj K","","CORONA stocks","corona-stocks.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.corona-stocks.com/monte-carlo-simulations-to-democratize-covid-19-policies/","","","","","","… Simulation and the Monte Carlo Method. 3rd edit. Hoboken, NJ: John Wiley & Sons; 2016. 3. Our Data. The COVID Tracking Project . 4. COVID-19 Projections. Institute for Health Metrics and Evaluation, University of Washington. 5. COVID-19 Coronavirus Pandemic …","","","","","","","","","","","","",""
"Journal Article","Van Houtven CH,Boucher NA,Dawson WD","","Impact of the COVID-19 Outbreak on Long-Term Care in the United States","International Long-Term Care Policy Network","International Long-Term Care Policy Network","2020","","","","COVID Tracking Project","","","","pdxscholar.library.pdx.edu","","","","","2020","2020-12-08","","","","https://pdxscholar.library.pdx.edu/aging_pub/54/;https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1055&context=aging_pub","","","","","","The United States (US) currently has the most confirmed cases of COVID-19 of any country. Yet, adequate testing for the virus remains a major issue. Approximately 51.6 million Americans are over the age of 65 and 56 percent of adults over 65 are living with two or more chronic conditions (23 percent have 3 or more). Given the higher risk of death and complications associated with advanced age and underlying health conditions, COVID-19 has had an immense impact upon LTC in the United States. Yet, the level and intensity of impact has been sporadic in application. This is due in part to a highly disparate and fractured long-term care system and perennial systemic challenges that have been exacerbated by the pandemic. In terms of financing care, the US relies on a mix of public and private funding sources. Further, individual states and the federal government have overlapping responsibility for funding and regulation of care. Meanwhile, fragmentation between financing and ownership of health care entities versus long-term care entities hinders coordinated delivery of care across sectors; and social sectors and health care sectors are also not integrated. The challenges of the system’s design suggest that both a near-term and long-term response is needed to mitigate the impact of COVID-19 on the approximately 13 million Americans who require long-term care. This report provides an overview of the current challenges facing LTC and outlines several potential policy responses to the pandemic as well as for life post-pandemic.","","","","","","","","","","","","",""
"Journal Article","Crane LD,Decker RA,Flaaen A,Hamins-Puertolas A,et al.","","Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context","","","2020","","","","COVID Tracking Project","","","","federalreserve.gov","","","","","2020","","","","","https://www.federalreserve.gov/econres/feds/business-exit-during-the-covid-19-pandemic.htm;https://www.federalreserve.gov/econres/feds/files/2020089pap.pdf","","","","","","Page 1. Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, DC Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context …","","","","","","","","","","","","",""
"Journal Article","Benatia D,Godefroy R,Lewis J","","Estimates of COVID-19 Cases across Four Canadian Provinces","Can. Public Policy","Canadian public policy. Analyse de politiques","2020","46","S3","S203-S216","COVID Tracking Project","","","","University of Toronto Press","","","","","2020-10-01","","","0317-0861","","https://doi.org/10.3138/cpp.2020-035;http://dx.doi.org/10.3138/cpp.2020-035;https://www.utpjournals.press/doi/abs/10.3138/cpp.2020-035;https://www.utpjournals.press/doi/pdf/10.3138/cpp.2020-035","10.3138/cpp.2020-035","","","","","This article estimates population infection rates from coronavirus disease 2019 (COVID-19) across four Canadian provinces from late March to early May 2020. The analysis combines daily data on the number of conducted tests and diagnosed cases with a methodology that corrects for non-random testing. We estimate the relationship between daily changes in the number of conducted tests and the fraction of positive cases in the non-random sample (typically less than 1 percent of the population) and apply this gradient to extrapolate the predicted fraction of positive cases if testing were expanded to the entire population. Over the sample period, the estimated population infection rates were 1.7?2.6 percent in Quebec, 0.7?1.4 percent in Ontario, 0.5?1.2 percent in Alberta, and 0.2?0.4 percent in British Columbia. In each province, these estimates are substantially below the average positive case rate, consistent with non-random testing of higher-risk populations. The results also imply widespread undiagnosed COVID-19 infection. For each identified case by mid-April, we estimate there were roughly 12 population infections.","","","","","","","","","","","","",""
"Preprint Manuscript","Garland P,Babbitt D,Bondarenko M,Sorichetta A,Tatem AJ,Johnson O","","The COVID-19 pandemic as experienced by the individual","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-03","","","","","http://arxiv.org/abs/2005.01167","","","2005.01167","","","The ongoing COVID-19 pandemic has progressed with varying degrees of intensity in individual countries, suggesting it is important to analyse factors that vary between them. We study measures of `population-weighted density', which capture density as perceived by a randomly chosen individual. These measures of population density can significantly explain variation in the initial rate of spread of COVID-19 between countries within Europe. However, such measures do not explain differences on a global scale, particularly when considering countries in East Asia, or looking later into the epidemics. Therefore, to control for country-level differences in response to COVID-19 we consider the cross-cultural measure of individualism proposed by Hofstede. This score can significantly explain variation in the size of epidemics across Europe, North America, and East Asia. Using both our measure of population-weighted density and the Hofstede score we can significantly explain half the variation in the current size of epidemics across Europe and North America. By controlling for country-level responses to the virus and population density, our analysis of the global incidence of COVID-19 can help focus attention on epidemic control measures that are effective for individual countries.","","","","","","","","arXiv","2005.01167","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Lyu M,Hall R","","Dynamic Modeling of Reported Covid-19 Cases and Deaths with Continuously Varying Case Fatality and Transmission Rate Functions","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.25.20201905v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/27/2020.09.25.20201905.full.pdf","","","","","","Page 1. Dynamic Modeling of Reported COVID-19 Cases and Deaths with Continuously Varying Case Fatality and Transmission Rate Functions Mingdong Lyu1, Randolph Hall1, 1 Epstein Department of Industrial and Systems …","","","","","","","","","","","","",""
"Journal Article","Sewell DK,Miller A,CDC MInD-Healthcare Program","","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","PloS one","2020","15","11","e0241949","COVID Tracking Project","","","","journals.plos.org","","","","","2020-11-10","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0241949;https://www.ncbi.nlm.nih.gov/pubmed/33170871;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654811;https://dx.plos.org/10.1371/journal.pone.0241949;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241949","10.1371/journal.pone.0241949","33170871","","","PMC7654811","The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.","","","","Department of Biostatistics, University of Iowa, Iowa City, IA, United States of America. Department of Epidemiology, University of Iowa, Iowa City, IA, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Ravichandran K,Anbazhagan S","","Himani Agri, Ramkumar N. Rupner, Vinodh Kumar Obli Rajendran, Kuldeep Dhama and Bhoj Raj Singh, Global status of COVID-19 Diagnosis: An Overview","J. Pure Appl. Microbiol.","Journal of pure & applied microbiology","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","0973-7510","","https://www.researchgate.net/profile/Bhoj_Singh/publication/341254738_Global_status_of_COVID-19_Diagnosis_An_Overview/links/5eb5946d4585152169c0e845/Global-status-of-COVID-19-Diagnosis-An-Overview.pdf","","","","","","… testing at the right time55. The COVID Tracking Project , which is a volunteer organization launched by The Atlanfic, collects and issues the comprehensive testing data available for US states and territories56. The policy is to …","","","","","","","","","","","","",""
"Journal Article","Hitchings MDT,Dean NE,Garcia-Carreras B,et al.","","The usefulness of SARS-CoV-2 test positive proportion as a surveillance tool","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/medrxiv/early/2020/07/07/2020.07.06.20147843.full.pdf","","","","","","… Disclosures NED is on the advisory board of the COVID Tracking Project . Bibliography … 4. The White House & CDC. Opening Up America Again. (2020). 5. Meyer, R. & Madrigal, A. The COVID Tracking Project . The Atlantic https://covidtracking.com/ (2020). 6. US Census Bureau …","","","","","","","","","","","","",""
"Journal Article","Mitoma G,Marcus AS","","Human Rights before and after COVID-19: Getting Human Rights Education out of Quarantine","Int. J. Soc. Educ.","The International journal of social education: official journal of the Indiana Council for the Social Studies","2020","","","","COVID Tracking Project","","","","ERIC","","","","","2020","","","0889-0293","","https://eric.ed.gov/?id=EJ1266474;https://files.eric.ed.gov/fulltext/EJ1266474.pdf","","","","","","… Good article from Just Security. https://www.justsecurity.org/69602/the-human-rights-lessons- from- covid-19-equality-requires-economic-and-social-rights-protections/ The Covid Tracking Project . Volunteer organization with origins with The Atlantic …","","","","","","","","","","","","",""
"Journal Article","Kelton GJ","","COVID-19 Pandemic May 2020 Portfolio","","","2020","","","","COVID Tracking Project","","","","digitalcommons.bowdoin.edu","","","","","2020","","","","","https://digitalcommons.bowdoin.edu/cgi/viewcontent.cgi?article=1015&context=bowdoinstories","","","","","","… Americans to white Americans who have tested positive for the coronavirus is disproportionate, surpassing the general population ratio. In another data released by The Covid Tracking Project , Louisiana shows that African-Americans account for 70% of all deaths in the state …","","","","","","","","","","","","",""
"Preprint Manuscript","Ramjee D,Sanderson P,Malek I","","COVID-19 and Digital Contact Tracing: Regulating the Future of Public Health Surveillance","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-18","2020-12-08","","","","https://papers.ssrn.com/abstract=3733071;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3733071;http://dx.doi.org/10.2139/ssrn.3733071;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3733071","10.2139/ssrn.3733071","","","","","Digital surveillance tools⎼⎼technological means of monitoring, tracking, and notifying⎼⎼are at the forefront of public health response strategies for the COVID-19 pandemic. Comprehensive and effective digital public health surveillance requires that public health authorities, regulatory powers, and developers consider interdisciplinary approaches. This entails accounting for the use of tracking technologies and location and proximity data; notification systems from tech companies; and laws and regulations associated with health information, biometric privacy, and mobile data. Of particular importance is incorporation of epidemiological considerations in development and implementation of digital tools, including usability across mobile devices, interoperability, regulation of literacy and disability compatibility, and incentivization for adoption. It is both feasible and prudent that the United States establish a federal network for public health surveillance aided by digital tools, especially considering that waves of COVID-19 are expected to continue well into 2021 and while the threat of other emerging infectious diseases persists.","COVID-19, contact tracing, exposure notification, contact tracing apps, apps, digital surveillance, surveillance, public health, privacy, security, data privacy, pandemic","","","","","","","","","","","","Cardozo Law Review"
"Journal Article","Zimba R,Kulkarni S,Berry A,You W,Mirzayi C,Westmoreland D,Parcesepe A,Waldron L,Rane M,Kochhar S,Robertson M,Maroko AR,Grov C,Nash D","","Testing, Testing: What SARS-CoV-2 testing services do adults in the United States actually want?","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-09-18","","","","","http://dx.doi.org/10.1101/2020.09.15.20195180;https://www.ncbi.nlm.nih.gov/pubmed/32995800;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523137;https://doi.org/10.1101/2020.09.15.20195180;https://www.medrxiv.org/content/10.1101/2020.09.15.20195180v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/18/2020.09.15.20195180.full.pdf","10.1101/2020.09.15.20195180","32995800","","","PMC7523137","Importance: Ascertaining preferences for SARS-CoV-2 testing and incorporating findings into the design and implementation of strategies for delivering testing services may enhance testing uptake and engagement, a prerequisite to reducing onward transmission. Objective: To determine important drivers of decisions to obtain a SARS-CoV-2 test in the context of increasing community transmission. Design : A discrete choice experiment (DCE) was used to assess the relative importance of type of SARS-CoV-2 test, specimen type, testing venue, and results turnaround time. Uptake of an optimized testing scenario was simulated relative to the current typical testing scenario of polymerase chain reaction (PCR) via nasopharyngeal (NP) swab in a provider office or urgent care clinic with results in >5 days. Setting: ​ Online survey, embedded in an existing cohort study, conducted during July 30 - September 8, 2020. Participants: ​Participants (n=4,793) were enrolled in the CHASING COVID Cohort Study, a national longitudinal cohort of adults >18 years residing in the 50 US states, Washington, DC, Puerto Rico, or Guam. Main Outcome(s) and Measure(s): Relative importance of SARS-CoV-2 testing method attributes, utilities of specific attribute levels, and probability of choosing a testing scenario based on preferences estimated from the DCE, the current typical testing option, or choosing not to test. Results: ​Turnaround time for test results had the highest relative importance (30.4%), followed by test type (28.3%), specimen type (26.2%), and venue (15.0%). Participants preferred fast results on both past and current infection and using a noninvasive specimen, preferably collected at home. Simulations suggested that providing immediate or same day test results, providing both PCR and serology, or collecting oral specimens would substantially increase testing uptake over the current typical testing option. Simulated uptake of a hypothetical testing scenario of PCR and serology via a saliva sample at a pharmacy with same day results was 97.7%, compared to 0.6% for the current typical testing scenario, with 1.8% opting for no test. Conclusions and Relevance: ​Testing strategies that offer both PCR and serology with non-invasive methods and rapid turnaround time would likely have the most uptake and engagement among residents in communities with increasing community transmission of SARS-CoV-2.","","","","","en","Research Article","","","","","","",""
"Preprint Manuscript","Callaway B,Li T","","Understanding the Effects of Tennessee's Open Covid-19 Testing Policy: Bounding Policy Effects with Nonrandomly Missing Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-19","","","","","http://arxiv.org/abs/2005.09605","","","2005.09605","","","Increased testing for Covid-19 is seen as one of the most important steps to re-open the economy. The current paper considers Tennessee's \"open-testing\" policy where the state substantially increased testing while removing symptom requirements for individuals to be tested. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. Our results suggest that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases.","","","","","","","","arXiv","2005.09605","econ.EM","","","arXiv [econ.EM]"
"Journal Article","Gonzalez JE","","ESTIMATING PREVALENCE AND TIME COURSE OF SARS-CoV-2 BASED ON NEW HOSPITAL ADMISSIONS AND PCR TESTS: RELEVANCE TO VACCINATION …","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.15.20175653v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/22/2020.08.15.20175653.full.pdf","","","","","","… As the ratio's value decreases, the % recovered increases approaching the value obtained from % PCR positives without mitigating for testing number bias. This calculation's validity rests on the accuracy of the data reported in the COVID Tracking Project at the Atlantic (1) …","","","","","","","","","","","","",""
"Report","Barrios JM,Benmelech E,Hochberg YV,Sapienza P,Zingales L","","Civic Capital and Social Distancing during the Covid-19 Pandemic","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27320","nber.org","","","","","2020-06-08","2020-12-08","","","","https://www.nber.org/papers/w27320;http://dx.doi.org/10.3386/w27320;https://www.nber.org/system/files/working_papers/w27320/w27320.pdf","10.3386/w27320","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Mao Y,Jiang S,Nametzˆ D","","Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey","","","2020","","","","COVID Tracking Project","","","","preprints.org","","","","","2020","","","","","https://www.preprints.org/manuscript/202007.0124;https://www.preprints.org/manuscript/202007.0124/download/final_file","","","","","","Page 1. 1 Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey Ying Mao*, Susiyan Jiang, Daniel Nametzˆ, Yuxin Lin, Jake Hack, John Hensley, Ryan Monaghan, Tess Gutenbrunner …","","","","","","","","","","","","",""
"Journal Article","Wetzler HP,Wetzler EA,Cobb HW","","COVID-19: Dying is Bad--Losing Life is Worse","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.08.20050559v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/12/2020.06.08.20050559.full.pdf","","","","","","… https://doi.org/10.1101/2020.06.08.20050559 doi: medRxiv preprint Page 10. 10 26. US Historical Data | The COVID Tracking Project . https://covidtracking.com/data/us-daily Accessed June 1, 2020. 27. United States Coronavirus - Worldometer …","","","","","","","","","","","","",""
"Journal Article","de la Economía PR","","ECONOMIC ADVISORY TASK FORCE","prhta.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.prhta.org/20200520%20repor%20ddec%20etf%20reactivation%20report%20phase%202%20final.pdf","","","","","","Page 1. ECONOMIC ADVISORY TASK FORCE COVID-19 Pandemic Emergency in Puerto Rico Informe Comisión Especial Asesora de Asuntos Económicos Proceso de Reapertura de la Economía 18 de mayo de 2020 Resumen Ejecutivo Trasfondo …","","","","","","","","","","","","",""
"Journal Article","Kurita K,Managi S","","COVID-19 and stigma: Evolution of self-restraint behavior","","","2020","","","","COVID Tracking Project","","","","mpra.ub.uni-muenchen.de","","","","","2020-10-11","2020-12-08","","","","https://mpra.ub.uni-muenchen.de/id/eprint/103446;https://mpra.ub.uni-muenchen.de/103446/1/MPRA_paper_103446.pdf","","","","","","It is important to consider the social stigma against going-out people in the fight against COVID-19 because it reduces the spread of infection through individual self-restraint behavior. This study analyzes the interaction between self-restraint behavior, infection with viruses such as COVID-19, and stigma against going out by using the framework of replicator dynamics. We show that the non-legally binding policy reduces the number of people going out in the steady state. Our comparative static analysis suggests an important result, that intensifying the stigma cost does not necessarily reduce the number of players going out because of the indirect effect of decrease in infection risk. The social welfare analysis suggests that the level of population share of players going out in the interior equilibrium is larger than the socially optimal level without the state of emergency, and it is the same under the state of emergency.","COVID-19, Non-legally binding policy, Replicator dynamics, Self-restraint behavior, Stigma","","","","en","","","","","","","",""
"Journal Article","Agarwal DK,De S,Shukla O,Checker A,Mittal A,et al.","","Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.22.20159731v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/25/2020.07.22.20159731.full.pdf","","","","","","… and Family Welfare (MoHFW) of India via collated datasets [6]. Health and medical data for Indian states are aggregated from Kaggle [7]. State-wise case progression for the United States was obtained from the New York Times [8] and The COVID Tracking Project , updated daily …","","","","","","","","","","","","",""
"Journal Article","Tembine H","","COVID-19: Data-Driven Mean-Field-Type Game Perspective","Games","Games","2020","11","4","51","COVID Tracking Project","","","","Multidisciplinary Digital Publishing Institute","","","","","2020-11-03","2020-12-08","","2073-4336","","https://www.mdpi.com/2073-4336/11/4/51;http://dx.doi.org/10.3390/g11040051;https://www.mdpi.com/2073-4336/11/4/51/pdf","10.3390/g11040051","","","","","In this article, a class of mean-field-type games with discrete-continuous state spaces is considered. We establish Bellman systems which provide sufficiency conditions for mean-field-type equilibria in state-and-mean-field-type feedback form. We then derive unnormalized master adjoint systems (MASS). The methodology is shown to be flexible enough to capture multi-class interaction in epidemic propagation in which multiple authorities are risk-aware atomic decision-makers and individuals are risk-aware non-atomic decision-makers. Based on MASS, we present a data-driven modelling and analytics for mitigating Coronavirus Disease 2019 (COVID-19). The model integrates untested cases, age-structure, decision-making, gender, pre-existing health conditions, location, testing capacity, hospital capacity, and a mobility map of local areas, including in-cities, inter-cities, and internationally. It is shown that the data-driven model can capture most of the reported data on COVID-19 on confirmed cases, deaths, recovered, number of testing and number of active cases in 66+ countries. The model also reports non-Gaussian and non-exponential properties in 15+ countries.","","","","","en","","","","","","","",""
"Journal Article","Steinhardt J","","Is R0< 1 in California and New York?","stat.berkeley.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.stat.berkeley.edu/~jsteinhardt/publications/R0_CA_NY.pdf","","","","","","… These conclusions seem to contradict the apparent rise in hospitalized COVID-19 cases, as reported by the COVID Tracking Project [2] and other sources … Below is New York data from the COVID Tracking Project . 4 Page 5 …","","","","","","","","","","","","",""
"Journal Article","Rajasekaran K","","Mental Health Among Otolaryngology Resident and Attending Physicians During the COVID-19 Pandemic: A National Study","Authorea Preprints","","2020","","","","COVID Tracking Project","","","","authorea.com","","","","","2020","","","","","https://www.authorea.com/doi/full/10.22541/au.158931657.76546679;https://www.authorea.com/doi/pdf/10.22541/au.158931657.76546679","","","","","","… COVID-19 Projections. Seattle, WA: IHME, University of Washington. 2020. Accessed April 26, 2020. https://covid19.healthdata.org/projections 25. The COVID Tracking Project . 2020. Accessed April 26, 2020. https://covidtracking.com/ 26 …","","","","","","","","","","","","",""
"Journal Article","Loeffler-Wirth H,Schmidt M,Binder H","","Covid-19 trajectories: Monitoring pandemic in the worldwide context","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.04.20120725v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/05/2020.06.04.20120725.full.pdf","","","","","","Page 1. 1 Covid‐19 trajectories – Monitoring pandemic in the worldwide context Henry Loeffler‐Wirth 1*, Maria Schmidt 1, Hans Binder 1* 1 IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16 – 18, 04107 Leipzig, Germany …","","","","","","","","","","","","",""
"Journal Article","Chandler-Brown D,Bueno AM,Atay O,Tsao DS","","A highly scalable and rapidly deployable RNA extraction-free COVID-19 assay by quantitative Sanger sequencing","bioRxiv","","2020","","","","COVID Tracking Project","","","","biorxiv.org","","","","","2020","","","","","https://www.biorxiv.org/content/10.1101/2020.04.07.029199v1.abstract;https://www.biorxiv.org/content/biorxiv/early/2020/04/10/2020.04.07.029199.full.pdf","","","","","","… The Lancet Global Health. (2020) 12 The COVID Tracking Project (Accessed April 7, 2020). 13 Herper, M. and Branswell, H. Shortage of crucial chemicals creates new obstacle to US coronavirus testing. STAT News …","","","","","","","","","","","","",""
"Journal Article","Mao Y,Jiang S,Nametz D,Lin Y,Hack J,et al.","","Data-driven Analytics of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey","arXiv preprint arXiv","","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020","","","","","https://arxiv.org/abs/2006.13994;https://arxiv.org/pdf/2006.13994","","","","","","Page 1. 1 Data-driven Analytics of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey Ying Mao*, Susiyan Jiang, Daniel Nametzˆ, Yuxin Lin, Jake Hack, John Hensley, Ryan Monaghan, Tess Gutenbrunner …","","","","","","","","","","","","",""
"Journal Article","Kurtzman JT,Moran GW,Anderson CB,McKiernan JM","","A Novel and Successful Model for Redeploying Urologists to Establish a Closed Intensive Care Unit within the Emergency Department during the COVID-19 Crisis","J. Urol.","The Journal of urology","2020","204","5","901-902","COVID Tracking Project","","","","auajournals.org","","","","","2020-11","","","0022-5347","1527-3792","http://dx.doi.org/10.1097/JU.0000000000001188;https://www.ncbi.nlm.nih.gov/pubmed/32820972;https://www.jurology.com/doi/10.1097/JU.0000000000001188?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.auajournals.org/doi/10.1097/JU.0000000000001188;https://www.auajournals.org/doi/abs/10.1097/JU.0000000000001188;https://www.auajournals.org/doi/pdf/10.1097/JU.0000000000001188","10.1097/JU.0000000000001188","32820972","","","","… Available at https://www.nytimes.com/2020/03/12/nyregion/coronavirus-new-rochelle-containment. html. Google Scholar; 4. The COVID Tracking Project at The Atlantic: New York data. Available at https://covidtracking.com/data/state/new-york. Accessed April 13, 2020 …","","","","Department of Urology, New York Presbyterian/Columbia University Irving Medical Center, New York, New York.","en","Research Article","","","","","","",""
"Preprint Manuscript","Radulescu A","","Course of the first month of the COVID 19 outbreak in the New York State counties","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-06","","","","","http://arxiv.org/abs/2004.02366","","","2004.02366","","","We illustrate and study the evolution of reported infections over the month from March 1st to April 1st in the New York State as a whole, as well as in each individual county. We search for exponential trends, and try to understand whether there is any correlation of the timing and dynamics of these trends with state mandated measures on social distancing and testing. We conclude that the reports on April 1st may be dramatically under-representing the actual number of state-wide infections, and we propose reassessment of the data over the coming weeks, to monitor for effects of the PAUSE directive, and for the increasing number of casualties as a validating measure.","","","","","","","","arXiv","2004.02366","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Vijayakumar S,Packianathan S,Ahmed HZ,et al.","","Why is a higher incidence of COVID-19 reported in the USA","Arch. Intern. Med.","Archives of internal medicine","2020","","","","COVID Tracking Project","","","","academia.edu","","","","","2020","","","0003-9926","","http://www.academia.edu/download/63336931/why-is-a-higher-incidence-of-covid19-reported-in-the-usa20200517-71796-1riby84.pdf","","","","","","… 4. Coronavirus disease 2019 (COVID-19) Situation Report – 78. WHO (2020). 5. Most recent data. The COVID Tracking Project (2020). 6. ArcGIS Dashboards (2020). 7. Population, surface area and density. Statistical Yearbook (2019): 0-21 …","","","","","","","","","","","","",""
"Journal Article","Hall EW,Luisi N,Zlotorzynska M,Wilde G,Sullivan P,Sanchez T,Bradley H,Siegler AJ","","Willingness to Use Home Collection Methods to Provide Specimens for SARS-CoV-2/COVID-19 Research: Survey Study","J. Med. Internet Res.","Journal of medical Internet research","2020","22","9","e19471","COVID Tracking Project","","","","jmir.org","","","","","2020-09-03","","","1439-4456","1438-8871","http://dx.doi.org/10.2196/19471;https://www.ncbi.nlm.nih.gov/pubmed/32790639;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473702;https://www.jmir.org/2020/9/e19471/;https://www.jmir.org/2020/9/e19471","10.2196/19471","32790639","","","PMC7473702","BACKGROUND: Innovative laboratory testing approaches for SARS-CoV-2 infection and immune response are needed to conduct research to establish estimates of prevalence and incidence. Self-specimen collection methods have been successfully used in HIV and sexually transmitted infection research and can provide a feasible opportunity to scale up SARS-CoV-2 testing for research purposes. OBJECTIVE: The aim of this study was to assess the willingness of adults to use different specimen collection modalities for themselves and children as part of a COVID-19 research study. METHODS: Between March 27 and April 1, 2020, we recruited 1435 adults aged 18 years or older though social media advertisements. Participants completed a survey that included 5-point Likert scale items stating how willing they were to use the following specimen collection testing modalities as part of a research study: home collection of a saliva sample, home collection of a throat swab, home finger-prick blood collection, drive-through site throat swab, clinic throat swab, and clinic blood collection. Additionally, participants indicated how the availability of home-based collection methods would impact their willingness to participate compared to drive-through and clinic-based specimen collection. We used Kruskal-Wallis tests and Spearman rank correlations to assess if willingness to use each testing modality differed by demographic variables and characteristics of interest. We compared the overall willingness to use each testing modality and estimated effect sizes with Cohen d. RESULTS: We analyzed responses from 1435 participants with a median age of 40.0 (SD=18.2) years and over half of which were female (761/1435, 53.0%). Most participants agreed or strongly agreed that they would be willing to use specimens self-collected at home to participate in research, including willingness to collect a saliva sample (1259/1435, 87.7%) or a throat swab (1191/1435, 83.1%). Willingness to collect a throat swab sample was lower in both a drive-through setting (64%) and clinic setting (53%). Overall, 69.0% (990/1435) of participants said they would be more likely to participate in a research study if they could provide a saliva sample or throat swab at home compared to going to a drive-through site; only 4.4% (63/1435) of participants said they would be less likely to participate using self-collected samples. For each specimen collection modality, willingness to collect specimens from children for research was lower than willingness to use on oneself, but the ranked order of modalities was similar. CONCLUSIONS: Most participants were willing to participate in a COVID-19 research study that involves laboratory testing; however, there was a strong preference for home specimen collection procedures over drive-through or clinic-based testing. To increase participation and minimize bias, epidemiologic research studies of SARS-CoV-2 infection and immune response should consider home specimen collection methods.","COVID-19; SARS-CoV-2; infectious disease; public health; research; specimen collection; survey; test; virus","","","Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States. Department of Epidemiology & Biostatistics, School of Public Health, Georgia State University, Atlanta, GA, United States. Department of Behavioral, Social and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA, United States.","en","Research Article","","","","","","",""
"Journal Article","Scissors D","","Estimating the True Number of China's COVID-19 Cases","AEI Paper & Studies","","2020","","","","COVID Tracking Project","","","","questia.com","","","","","2020","","","","","https://www.questia.com/library/journal/1G1-624294139/estimating-the-true-number-of-china-s-covid-19-cases","","","","","","… (13.) Centers for Disease Control and Prevention, Geographic Information Systems, \"Laboratory-Confirmed COVID-19-Associatcd Hospitalizations,\" March 28, 2020, https://gis.cdc.gov/grasp/COVIDNct/COVID19_3.html; and COVID Tracking Project , \"Our Most …","","","","","","","","","","","","",""
"Journal Article","Pitzer VE,Chitwood M,Havumaki J,Menzies NA,Perniciaro S,Warren JL,Weinberger DM,Cohen T","","The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-04-24","","","","","http://dx.doi.org/10.1101/2020.04.20.20073338;https://www.ncbi.nlm.nih.gov/pubmed/32511624;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276051;https://doi.org/10.1101/2020.04.20.20073338;https://www.medrxiv.org/content/10.1101/2020.04.20.20073338v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/24/2020.04.20.20073338.full.pdf","10.1101/2020.04.20.20073338","32511624","","","PMC7276051","Estimates of the reproductive number for novel pathogens such as SARS-CoV-2 are essential for understanding the potential trajectory of the epidemic and the level of intervention that is needed to bring the epidemic under control. However, most methods for estimating the basic reproductive number (R0) and time-varying effective reproductive number (Rt) assume that the fraction of cases detected and reported is constant through time. We explore the impact of secular changes in diagnostic testing and reporting on estimates of R0 and Rt using simulated data. We then compare these patterns to data on reported cases of COVID-19 and testing practices from different United States (US) states. We find that changes in testing practices and delays in reporting can result in biased estimates of R0 and Rt. Examination of changes in the daily number of tests conducted and the percent of patients testing positive may be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of COVID-19.","","","","","en","Research Article","","","","","","",""
"Journal Article","Godefroy R,Lewis J","","Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach","","","2020","","","","COVID Tracking Project","","","","webdepot.umontreal.ca","","","","","2020","","","","","https://www.webdepot.umontreal.ca/Usagers/godefror/MonDepotPublic/bgl_covid.pdf","","","","","","… negative) and total positive test results across US states for the period March 31 to April 7. These data were obtained from the COVID Tracking Project , a site that … [8] Meyer R, Kissane E, Madrigal A. The COVID Tracking Project . https: //covidtracking.com/; Accessed: 2019-04-08 …","","","","","","","","","","","","",""
"Journal Article","Chitanvis SM","","Dynamical model for social distancing in the US during the COVID-19 epidemic","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.18.20105411v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/21/2020.05.18.20105411.full.pdf","","","","","","… Here the diffusion constant D =1[dimensionless]. A. Data extraction We used COVID19 state-wide hospitalization data from the Covid tracking project : https://covidtracking.com/ api/v1/states/daily.csv to plot and obtain fits to τ(t)1/2. B. Data Analysis …","","","","","","","","","","","","",""
"Journal Article","Rahmandad H,Lim TY,Sterman J","","Estimating COVID-19 under-reporting across 86 nations: implications for projections and control","Available at SSRN 3635047","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3666582;https://www.medrxiv.org/content/10.1101/2020.06.24.20139451v2.full.pdf","","","","","","Page 1. 1 Preprint, Ver. 1 (June 24, 2020) - the manuscript has not been peer-reviewed yet. Estimating the global spread of COVID-19 Hazhir Rahmandad1*, Tse Yang Lim1, John Sterman1 1Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA …","","","","","","","","","","","","",""
"Preprint Manuscript","Fellows IE,Slayton RB,Hakim AJ","","The COVID-19 Pandemic, Community Mobility and the Effectiveness of Non-pharmaceutical Interventions: The United States of America, February to May 2020","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-09","","","","","http://arxiv.org/abs/2007.12644","","","2007.12644","","","Background: The impact of individual non-pharmaceutical interventions (NPI) such as state-wide stay-at-home orders, school closures and gathering size limitations, on the COVID-19 epidemic is unknown. Understanding the impact that above listed NPI have on disease transmission is critical for policy makers, particularly as case counts increase again in some areas. Methods: Using a Bayesian framework, we reconstructed the incidence and time-varying reproductive number (Rt) curves to investigate the relationship between Rt, individual mobility as measured by Google Community Mobility Reports, and NPI. Results: We found a strong relationship between reproductive number and mobility, with each 10% drop in mobility being associated with an expected 10.2% reduction in Rt compared to baseline. The effects of limitations on the size of gatherings, school and business closures, and stay-at-home orders were dominated by the trend over time, which was associated with a 48% decrease in the reproductive number, adjusting for the NPI. Conclusions: We found that the decrease in mobility associated with time may be due to individuals changing their behavior in response to perceived risk or external factors.","","","","","","","","arXiv","2007.12644","q-bio.PE","","","arXiv [q-bio.PE]"
"Preprint Manuscript","Zhang Y,Sun Y,Barua S,Bertini E,Parker AG","","Mapping the Landscape of COVID-19 Crisis Visualizations","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08","","","","","http://dx.doi.org/10.31219/osf.io/kd3y9;https://osf.io/preprints/kd3y9/;https://osf.io/kd3y9/download?format=pdf","10.31219/osf.io/kd3y9","","","","","A great number of visualizations have been created to communicate the constantly changing crisis of the COVID-19 pandemic. With the prevalence of these crisis visualizations, there is a critical need to organize and understand what and how visualizations have been produced and disseminated to the public, as information consumption can impact peoples' attitudes, responses to crisis and risk, behaviors, and thus ultimately the trajectory of the pandemic. We curated a list of 668 visualizations that communicate information about the pandemic. We performed a content analysis of these visualizations and derived six categories of intended messages in communication about the pandemic, including informing about severity; forecasting trends and influences; explaining the course of the disease; mirroring impact of the crisis; and communicating risk, vulnerability, and equity. We also identify issues and opportunities arising from COVID-19 crisis visualizations.","","","","","","","","","","","","",""
"Journal Article","Liang P","","The Safety Benefit of Social Distancing During the COVID-19 Pandemic","","","2020","","","","COVID Tracking Project","","","","voices.uchicago.edu","","","","","2020","","","","","https://voices.uchicago.edu/philipliang/files/2020/08/The_Safety_Benefit_of_Social_Distancing_During_the_COVID_19_Pandemic.pdf","","","","","","… the infected. Date 0 corresponds to March 4. I use data from the COVID tracking project to estimate the number of infected and 2018 Census estimates to estimate the size and age structure of the US population. I assume none …","","","","","","","","","","","","",""
"Preprint Manuscript","Coeurjolly JF","","Digit analysis for Covid-19 reported data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-11","","","","","http://arxiv.org/abs/2005.05009","","","2005.05009","","","The coronavirus which appeared in December 2019 in Wuhan has spread out worldwide and caused the death of more than 280,000 people (as of May, 11 2020). Since February 2020, doubts were raised about the numbers of confirmed cases and deaths reported by the Chinese government. In this paper, we examine data available from China at the city and provincial levels and we compare them with Canadian provincial data, US state data and French regional data. We consider cumulative and daily numbers of confirmed cases and deaths and examine these numbers through the lens of their first two digits and in particular we measure departures of these first two digits to the Newcomb-Benford distribution, often used to detect frauds. Our finding is that there is no evidence that cumulative and daily numbers of confirmed cases and deaths for all these countries have different first or second digit distributions. We also show that the Newcomb-Benford distribution cannot be rejected for these data.","","","","","","","","arXiv","2005.05009","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Harbert RS,Cunningham SW,Tessler M","","Spatial modeling cannot currently differentiate SARS-CoV-2 coronavirus and human distributions on the basis of climate in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.08.20057281v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/10/2020.04.08.20057281.full.pdf","","","","","","… examine, especially as multiple sources are compiling data and making it public ​(Dong et al., 2020; The COVID Tracking Project , 2020; The New York Times, 2020)​. Organisms are distributed within their environment based on both direct and indirect …","","","","","","","","","","","","",""
"Journal Article","Uribe-Tirado A,Del Río Riande G,et al.","","Open Science since COVID-19: Open Access+ Open Data","Available at SSRN","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3621047;http://eprints.rclis.org/40043/1/The%20Open%20Science%20since%20Covid-19.%20Open%20Access%20and%20Open%20Data.pdf","","","","","","Page 1. OPEN SCIENCE SINCE COVID-19: OPEN ACCESS + OPEN DATA RECOPILACIÓN ACTUALIZADA. VERSIÓN II Open Science since Covid-19: Open Access + Open Data Alejandro Uribe-Tirado – Universidad de Antioquia. CoLaV …","","","","","","","","","","","","",""
"Journal Article","Katz J,Lu D,Sanger-Katz M","","USA: Excess death data compared to confirmed COVID-19 fatalities","flutrackers.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://flutrackers.com/forum/forum/-2019-ncov-new-coronavirus/united-states-2019-ncov/855716-usa-excess-death-data-compared-to-confirmed-covid-19-fatalities","","","","","","Login. Logging in... Remember me. Log in. Forgot password or user name? Log in with. Logo. Search in titles only Search in US COVID-19: Dec. 2019 - Sept. 12, 2020 only Search. Advanced Search …","","","","","","","","","","","","",""
"Journal Article","Kochańczyk M,Lipniacki T","","Evaluation of national responses to COVID‐19 pandemic based on Pareto optimality","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.06.27.20141747v1.full.pdf","","","","","","… for Disease Prevention and Control) and the number of tests (collected from national government reports). For the states of the USA, analogous data were extracted from The COVID Tracking Project [5]. Mobility reduction has …","","","","","","","","","","","","",""
"Journal Article","Cevasco KE,North HM,Zeitoun SA,Wofford RN,Matulis GA,Gregory AF,Hassan MH,Abdo AD,Farris D,Roess AA,von Fricken ME","","COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020","PLoS One","PloS one","2020","15","10","e0240786","COVID Tracking Project","","","","journals.plos.org","","","","","2020-10-16","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0240786;https://www.ncbi.nlm.nih.gov/pubmed/33064753;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567344;https://dx.plos.org/10.1371/journal.pone.0240786;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240786","10.1371/journal.pone.0240786","33064753","","","PMC7567344","BACKGROUND: The Centers for Disease Control and Prevention (CDC) publishes COVID-19 non-pharmaceutical intervention (NPI) guidance for specific institutional audiences to limit community spread. Audiences include: business, clinical, public health, education, community, and state/local government. The swift, severe, and global nature of COVID-19 offers an opportunity to systematically obtain a national view of how larger institutions of higher education adopted NPI guidance at the onset of the pandemic. METHOD: An original database of COVID-19-related university NPI policy changes was compiled. Survey team members manually combed university websites and official statements capturing implementation decisions and dates for five NPI variables from 575 U.S. universities, across 50 states and the District of Columbia, during March of 2020. The universities included in this study were selected from the Department of Education Integrated Postsecondary Education Data System (IPEDS), which provides a set of university explanatory variables. Using IPEDS as the basis for the organizational data allows consistent mapping to event-time and institutional characteristic variables including public health announcements, geospatial, census, and political affiliation. RESULTS: The dataset enables event-time analysis and offers a variety of variables to support institutional level study and identification of underlying biases like educational attainment. A descriptive analysis of the dataset reveals that there was substantial heterogeneity in the decisions that were made and the timing of these decisions as they temporally related to key state, national, and global emergency announcements. The WHO pandemic declaration coincided with the largest number of university decisions to implement NPIs. CONCLUSION: This study provides descriptive observations and produced an original dataset that will be useful for future research focused on drivers and trends of COVID-19 NPIs for U.S. Universities. This preliminary analysis suggests COVID-19 university decisions appeared to be made largely at the university level, leading to major variations in the nature and timing of the responses both between and within states, which requires further study.","","","","Department of Global and Community Health, College of Health and Human Services, George Mason University, Fairfax, Virginia, United States of America. Department of Biology, College of Science, George Mason University, Fairfax, Virginia, United States of America. Environmental Health and Safety Office, George Mason University, Fairfax, Virginia, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Zhang T,Gerlowski D,Acs Z","","Small Business and the COVID-19 Pandemic: The Role of Work from Home","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-83264/latest.pdf","","","","","","… Philanthropies, Stavros Niarchos Foundation. 4 Those websites Include 1point3acres, Worldometers.info, BNO, and the COVID Tracking Project . 5 See https://www.whitehouse. gov/openingamerica/. Page 14. land. According to EPA …","","","","","","","","","","","","",""
"Journal Article","Schuetz AN,Hemarajata P,Mehta N,Campbell S,Mitchell S,Palavecino E,Butler-Wu S,Miller MB","","When Should Asymptomatic Persons Be Tested for COVID-19?","J. Clin. Microbiol.","Journal of clinical microbiology","2020","","","","COVID Tracking Project","","","","American Society for Microbiology Journals","","","","","2020-10-06","2020-12-08","","0095-1137","1098-660X","https://jcm.asm.org/content/early/2020/10/05/JCM.02563-20.abstract;http://dx.doi.org/10.1128/JCM.02563-20;https://www.ncbi.nlm.nih.gov/pubmed/33023910;https://jcm.asm.org/content/jcm/early/2020/10/05/JCM.02563-20.full.pdf","10.1128/JCM.02563-20","33023910","","","","On August 24, 2020, the Centers for Disease Control and Prevention (CDC) updated its website to highlight that asymptomatic individuals, even those with exposure to a COVID-19 positive contact, do not necessarily need to be tested unless they have medical conditions associated with increased risk of severe illness from COVID-19. The CDC subsequently updated its guidance on September 19, 2020 to support testing of asymptomatic persons, including close contacts of persons with documented SARS-CoV-2 infection. In this editorial, the American Society for Microbiology Clinical and Public Health Microbiology Committee9s Subcommittee on Laboratory Practices comments on testing of asymptomatic individuals relative to current medical knowledge of the virus and mitigation measures. Specific points are provided concerning such testing when undertaking contact tracing and routine surveillance. Limitations to consider when testing asymptomatic persons are covered, including the need to prioritize testing of contacts of positive COVID-19 cases. We urge the CDC to consult with primary stakeholders of COVID-19 testing when making such impactful changes in testing guidance.","","","","","en","","","","","","","",""
"Journal Article","Ison D","","Statistical procedures for evaluating trends in coronavirus disease-19 cases in the United States","Int. J. Health Sci. ","International journal of health sciences","2020","14","5","23-31","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-09","","","1658-3639","","https://www.ncbi.nlm.nih.gov/pubmed/32952502;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475210;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475210/","","32952502","","","PMC7475210","Objectives: In late 2019, a novel respiratory disease was identified as it began to spread rapidly within China's Hubei Province soon thereafter, being designated coronavirus disease 2019 (COVID-19). Unfortunately, trends in cases and rates of infection have been consistently misunderstood, particularly within the media, due to little, if any, statistical analysis of trends. Critical analysis of data is necessary to determine how to best manage local restrictions, particularly if there are resurgences of infection. As such, researchers have been calling for data-driven, statistical analysis of trends of disease to provide more context and validity for significant policy decisions. Methods: This quantitative study sought to explore different statistical methods that can be used to evaluate trend data to improve decision-making and public information on the spread of COVID-19. Analyses were conducted using Spearman's rho, Mann-Whitney U tests, Mann-Kendal tests, and Augmented Dickey-Fuller tests with follow up Kwiatkowski-Phillips-Schmidt-Shin tests. Results: The results indicated a mix of both surprising and expected findings. Variations among COVID case reporting for each day of the week were identified but not deemed significant. Spearman correlation data appeared to perform well in identifying monotonic trend while Mann-Kendal tests appeared to provide the most intelligible results. Conclusions: This study provides examples of statistical tools and procedures to more thoroughly examine trends in COVID-19 case rate data. It is advocated that such metrics be made available to health and policy stakeholders for potential use for public health decisions.","Coronavirus disease 2019; U.S; public health; statistics; trends","","","Graduate School, Northcentral University, Torrey Pines Road, La Jolla, CA 92037, USA.","en","Research Article","","","","","","",""
"Journal Article","Gupta K,Bellino P,Charness ME","","Adverse Effects of Nasopharyngeal Swabs: 3-D Printed Versus Commercial Swabs","Infection Control & Hospital","","2020","","","","COVID Tracking Project","","","","cambridge.org","","","","","2020","","","","","https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/article/adverse-effects-of-nasopharyngeal-swabs-3d-printed-versus-commercial-swabs/9BCD4F5B7066D43F2FB68802D8A2A2EC;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9BCD4F5B7066D43F2FB68802D8A2A2EC/S0899823X20002974a.pdf/div-class-title-adverse-effects-of-nasopharyngeal-swabs-3-d-printed-versus-commercial-swabs-div.pdf","","","","","","… Rhinorrhea 5 1 Downloaded from https://www.cambridge.org/core. 13 Jun 2020 at 05:14:56, subject to the Cambridge Core terms of use. Page 4. References 1. The COVID Tracking project . https://covidtracking.com/ Accessed May 9, 2020 2. Callahan CJ, Lee R, Zulauf KE, et al …","","","","","","","","","","","","",""
"Preprint Manuscript","Béland LP,Brodeur A,Wright T","","Covid-19, Stay-at-Home Orders and Employment: Evidence from CPS Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-23","2020-12-08","","","","https://papers.ssrn.com/abstract=3608531;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3608531;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3608531;https://ir.library.carleton.ca/pub/27086/cewp20-04.pdf","","","","","","In this paper, we examine the short-term consequences of COVID-19 and evaluate the impacts of stay-at-home orders on employment and wages in the United States. Guided by a pre-analysis plan, we document that COVID-19 increased the unemployment rate, decreased hours of work and labor force participation, especially for younger workers, non-white, not married and less-educated workers. We built four indexes (exposure to disease, proximity to coworkers, work remotely and critical workers) to study the impact of COVID-19. We find that workers that can work remotely are significantly less likely to have their labor market outcomes affected, while workers working in proximity to coworkers are more affected.The unemployment effects are significantly larger for states that implemented stay-at-home orders. Our estimates suggest that, as of early May, these policies increased unemployment by nearly 4 percentage points, but reduced COVID-19 cases by 186,600– 311,000, and deaths by 17,851–23,325. We apply our estimates to compute lost income ($18.6–$21.4 billion), reduced government income tax revenues ($3.4–$5.5 billion), increased unemployment insurance benefit payments ($5–$5.8 billion) and reduced hospital costs ($0.7–$1.2 billion). Despite the jobs lost, age adjusted value of statistical life suggests that stay-at-home orders are cost effective.","COVID-19, unemployment, wages, remote work, exposure to disease, essential workers, stay-at-home orders, lockdown","","","","","","","","","","","",""
"Preprint Manuscript","Uribe-Tirado A,del Rio G,Raiher S,Ochoa Gutiérrez J","","Open Science since Covid-19: Open Access + Open Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06","","","","","http://dx.doi.org/10.31235/osf.io/a5nqw;https://osf.io/preprints/socarxiv/a5nqw/;https://osf.io/preprints/socarxiv/a5nqw/download","10.31235/osf.io/a5nqw","","","","","The coronavirus crisis has created different initiatives that promote access to open publications and open data, solve collaboratively and from different places, being an example of the benefits of open science. From the initial version of the Compilation on Open Science from COVID-19: Open Access + Open Data (Version I: April 3, 2020) published by Alejandro Uribe-Tirado (http://eprints.rclis.org/39864/), it seemed to us, a good practice to update this first input openly and collaboratively, using the platform: https://etherpad.wikimedia.org/p/covid19. This new version (Version II: June 3, 2020), is the result of this joint work.","coronavirus; CoV-2; COVID-19; open access; open data; open science; SARS","","","","","","","","","","","","osf.io"
"Journal Article","Atala A,Henn A,Lundberg M,Ahsan T,et al.","","Regen med therapeutic opportunities for fighting COVID‐19","Stem Cells","Stem cells","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","0250-6793","","https://stemcellsjournals.onlinelibrary.wiley.com/doi/abs/10.1002/sctm.20-0245;https://stemcellsjournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sctm.20-0245","","","","","","Abstract This perspective from a Regenerative Medicine Manufacturing Society working group highlights regenerative medicine therapeutic opportunities for fighting COVID‐19. This article addresses w...","","","","","","","","","","","","",""
"Journal Article","Griffin AL","","Trustworthy maps","Journal of Spatial Information Science","Journal of Spatial Information Science","2020","2020","20","5-19","COVID Tracking Project","","","","josis.org","","","","","2020-06-25","2020-12-08","","1948-660X","","http://www.josis.org/index.php/josis/article/viewArticle/654;http://dx.doi.org/10.5311/JOSIS.2020.20.654;http://www.josis.org/index.php/josis/article/download/654/273","10.5311/JOSIS.2020.20.654","","","","","Maps get used for decision making about the world's most pressing problems (e.g., climate change, refugee crises, biodiversity loss, rising inequality, pandemic disease). Although maps have historically been a trusted source of information, changes in society (e.g., lower levels of trust in decision makers) and in mapmaking technologies and practices (e.g., anyone can now make their own maps) mean that we need to spend some time thinking about how, when, and why people trust maps and mapmaking processes. This is critically important if we want stakeholders to engage constructively with the information we present in maps, because they are unlikely to do so if they do not trust what they see. Here I outline three questions about trust and maps that I think need research attention. First, how can we focus map readers' attention on the trustworthiness of mapped data, especially if trustworthiness changes as in the case of real-time data sources? Second, does presenting uncertainty information on maps affect the level of trust map readers have in the map, and if so, does trust vary depending on how the uncertainty information is presented? Finally, how does virality affect trust? Are viral maps less trusted? The time and resources required to develop a better understanding of how trust in maps might be changing will be repaid. The world needs good information to guide policy- and decision-making. Well designed maps can help stakeholders to work together to solve problems, but only if they are trusted.","","","","","","","","","","","","",""
"Journal Article","Davis BS,Officer CG","","Covid-19 testing brief","","","2020","","","","COVID Tracking Project","","","","avalonhcs.com","","","","","2020","","","","","https://www.avalonhcs.com/COVID-19/Avalon%20COVID%20Newsletter%2018%20May%20-%20Business%20Dev.pdf","","","","","","Page 1. Covid-19 testing brief Contents from Avalon Healthcare Solutions May 18, 2020 Avalon is the expert in laboratory and medical specialty drug benefit management. Our solutions are driven by evidence-based medical science. Avalon's core program includes full …","","","","","","","","","","","","",""
"Journal Article","Oehmke JF,Oehmke TB,Singh LN,Post LA","","Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation","J. Med. Internet Res.","Journal of medical Internet research","2020","22","9","e20924","COVID Tracking Project","","","","Journal of Medical Internet Research","","","","","2020","2020-12-08","","1439-4456","","https://www.jmir.org/2020/9/e20924;http://dx.doi.org/10.2196/20924","10.2196/20924","","","","","Background: SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective: The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods: Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results: A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ 2 10 =1489.84, P <.001), and a Sargan chi-square test indicated that the model identification is valid (χ 2 946 =935.52, P =.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 ( P <.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 ( P =.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions: DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites. [J Med Internet Res 2020;22(9):e20924]","","","","","en","","","","","","","",""
"Journal Article","Brzezinski A,Deiana G,Kecht V,et al.","","The covid-19 pandemic: government vs. community action across the united states","Covid Economics: Vetted","","2020","","","","COVID Tracking Project","","","","inet.ox.ac.uk","","","","","2020","","","","","https://www.inet.ox.ac.uk/files/BrzezinskiKechtDeianaVanDijcke_18042020_CEPR_2.pdf","","","","","","… collected from three different sources: the official US Government COVID-19 dedicated page15, the Johns Hopkins Coronavirus Research Center16 and the COVID Tracking Project .17 We collect measures on positive tests, negative tests and number of deaths; and this for both …","","","","","","","","","","","","",""
"Journal Article","Fenoll AA,Grossbard S","","Intergenerational residence patterns and COVID-19 fatalities in the EU and the US","Econ. Hum. Biol.","Economics and human biology","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020","","","1570-677X","","https://www.sciencedirect.com/science/article/pii/S1570677X20302045?casa_token=2xzvFKpujn4AAAAA:sVNQIskyiK_qMrTRXesgUZPe-mAF-g7oiqwnipU0dlMRyaO1snV2dC9c1ksS_dOaIfjELtXYpA","","","","","","… for both the EU and the US. The number of tests in EU countries comes from the University of Oxford while information on tests in US states is from the COVID Tracking Project (The Atlantic). This data also allowed us to calculate …","","","","","","","","","","","","",""
"Journal Article","Chambless L","","Why do per capita COVID-19 Case Rates Differ Between US States?","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.16.20213892v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/03/2020.10.16.20213892.full.pdf","","","","","","… Page 3. 3 Because of the possible association between the level of testing for COVID-19 and the number of cases found, we would have liked to also analyze the rate of COVID-19 hospitalizations by state, but fourteen states do not report this data (The Covid Tracking Project ) …","","","","","","","","","","","","",""
"Journal Article","Bernheim BD,Buchmann N,et al.","","The effects of large group meetings on the spread of COVID-19: The case of Trump rallies","Nina and Freitas-Groff","","2020","","","","COVID Tracking Project","","","","8newsnow.com","","","","","2020","","","","","https://www.8newsnow.com/wp-content/uploads/sites/59/2020/10/COVIDrallies_10_30_2000.pdf","","","","","","… State-level testing data is provided by the COVID Tracking Project at The Atlantic, which collects information from state departments of public health. We obtain county-level testing data for Wisconsin from the state departments of public health …","","","","","","","","","","","","",""
"Preprint Manuscript","Chernozhukov V,Kasaha H,Schrimpf P","","Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-28","","","","","http://arxiv.org/abs/2005.14168","","","2005.14168","10.1016/j.jeconom.2020.09.003","","This paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people's voluntarily behavioral response to new information of transmission risks. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our counterfactual experiments suggest that nationally mandating face masks for employees on April 1st could have reduced the growth rate of cases and deaths by more than 10 percentage points in late April, and could have led to as much as 17 to 55 percent less deaths nationally by the end of May, which roughly translates into 17 to 55 thousand saved lives. Our estimates imply that removing non-essential business closures (while maintaining school closures, restrictions on movie theaters and restaurants) could have led to -20 to 60 percent more cases and deaths by the end of May. We also find that, without stay-at-home orders, cases would have been larger by 25 to 170 percent, which implies that 0.5 to 3.4 million more Americans could have been infected if stay-at-home orders had not been implemented. Finally, not having implemented any policies could have led to at least a 7 fold increase with an uninformative upper bound in cases (and deaths) by the end of May in the US, with considerable uncertainty over the effects of school closures, which had little cross-sectional variation.","","","","","","","","arXiv","2005.14168","econ.EM","","","arXiv [econ.EM]"
"Journal Article","Cevasco KE,North HM,Zeitoun SA,Gregory AF,et al.","","How US Public Universities Responded to the COVID-19 Pandemic in March 2020: Lessons Learned from the Variations in Timing of Key Decisions","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-28634/latest.pdf","","","","","","… campus student housing. COVID–19 state case data were extracted from the ' COVID Tracking Project ' website (https://covidtracking.com/),, which reports current, retrospective, and cumulative numbers by state,. This information …","","","","","","","","","","","","",""
"Preprint Manuscript","Aparicio Fenoll A,Grossbard SA","","Intergenerational Residence Patterns and Covid-19 Fatalities in the EU and the Us","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-14","2020-12-08","","","","https://papers.ssrn.com/abstract=3648792;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3648792;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3648792;https://www.econstor.eu/bitstream/10419/223894/1/dp13452.pdf","","","","","","We study how patterns of intergenerational residence possibly influence fatalities from Covid-19. We use aggregate data on Covid-19 deaths, the share of young adults living with their parents, and a number of other statistics, for the 27 countries in the European Union, the UK, and all US states. Controlling for population size, we find that more people died from Covid in countries or states with higher rates of intergenerational co-residence. This positive correlation persists even when controlling for date of first death, presence of lockdown, Covid tests pc, hospital beds per capita, proportion of elderly, GDP pc, government's political orientation, percentage urban, and rental prices. The positive association between co-residence and fatalities is led by the US. Our estimates pass the Oster test for selection on unobservables.","COVID-19, intergenerational co-residence, family arrangements","","","","","","","","","","","",""
"Journal Article","Anderson SC,Edwards AM,Yerlanov M,Mulberry N,Stockdale JE,Iyaniwura SA,Falcao RC,Otterstatter MC,Irvine MA,Janjua NZ,Coombs D,Colijn C","","Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing","PLoS Comput. Biol.","PLoS computational biology","2020","16","12","e1008274","COVID Tracking Project","","","","journals.plos.org","","","","","2020-12-03","","","1553-734X","1553-7358","http://dx.doi.org/10.1371/journal.pcbi.1008274;https://www.ncbi.nlm.nih.gov/pubmed/33270633;https://dx.plos.org/10.1371/journal.pcbi.1008274;https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008274","10.1371/journal.pcbi.1008274","33270633","","","","Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the \"contact ratio\" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.","","","","Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada. Department of Biology, University of Victoria, Victoria, Canada. Department of Mathematics, Simon Fraser University, Burnaby, Canada. Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada. British Columbia Centre for Disease Control, Vancouver, Canada. School of Population and Public Health, University of British Columbia, Vancouver, Canada. British Columbia Children's Hospital Research Institute, Vancouver, Canada.","en","Research Article","","","","","","",""
"Journal Article","Wang Z,Zhang X,Teichert GH,Carrasco-Teja M,Garikipati K","","System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19","Comput. Mech.","Computational Mechanics","2020","66","5","1153-1176","COVID Tracking Project","","","","Springer","","","","","2020-11-01","","","0178-7675","1432-0924","https://doi.org/10.1007/s00466-020-01894-2;http://dx.doi.org/10.1007/s00466-020-01894-2;https://link.springer.com/article/10.1007/s00466-020-01894-2","10.1007/s00466-020-01894-2","","","","","We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al. in Comput Methods Appl Mech Eng 356:44–74, 2019; arXiv:2001.04816[cs.CE]) as well as Bayesian machine learning methods.","","","","","","","","","","","","",""
"Journal Article","Barnes SR,Beland LP,Huh J,Kim D","","The Effect of COVID-19 Lockdown on Mobility and Traffic Accidents: Evidence from Louisiana","","","2020","","","","COVID Tracking Project","","","","econstor.eu","","","","","2020","","","","","https://www.econstor.eu/handle/10419/222470;https://www.econstor.eu/bitstream/10419/222470/1/GLO-DP-0616.pdf","","","","","","… We obtain the number of cases and deaths across Louisiana due to COVID-19 using data from the COVID Tracking Project .4 We use this data set to investigate if the effect of the COVID- 19 lockdown has heterogeneous impacts on mobility and traffic accidents in parishes with …","","","","","","","","","","","","",""
"Journal Article","Weitz JS,Park SW,Eksin C,Dushoff J","","Moving Beyond a Peak Mentality: Plateaus, Shoulders, Oscillations and Other 'Anomalous' Behavior-Driven Shapes in COVID-19 Outbreaks","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-05-08","","","","","http://dx.doi.org/10.1101/2020.05.03.20089524;https://www.ncbi.nlm.nih.gov/pubmed/32511479;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273247;https://doi.org/10.1101/2020.05.03.20089524;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273247/","10.1101/2020.05.03.20089524","32511479","","","PMC7273247","The COVID-19 pandemic has caused more than 300,000 reported deaths globally, of which more than 83,000 have been reported in the United States as of May 16, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions (both at national and local levels) the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in some cases, to be consistent with plateau- or shoulder-like phenomena. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, epidemic dynamics can be characterized by plateaus, shoulders, and lag-driven oscillations after exponential rises at the outset of disease dynamics. We also show that incorporating long-term awareness can avoid peak resurgence and accelerate epidemic decline. We suggest that awareness of epidemic severity is likely to play a critical role in disease dynamics, beyond that imposed by intervention-driven policies.","","","","","en","Research Article","","","","","","",""
"Review","Natesan S,Bhatia R,Sundararajan A,Dhama K,Malik YS,Vora K","","Ramping up of SARS CoV-2 testing for the diagnosis of COVID-19 to better manage the next phase of pandemic and reduce the mortality in India","Virusdisease","Virusdisease","2020","","","1-9","COVID Tracking Project","","","","Springer","","","","","2020-08-08","","","2347-3584","","http://dx.doi.org/10.1007/s13337-020-00622-x;https://www.ncbi.nlm.nih.gov/pubmed/32837973;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413832;https://dx.doi.org/10.1007/s13337-020-00622-x;https://link.springer.com/article/10.1007/s13337-020-00622-x","10.1007/s13337-020-00622-x","32837973","","","PMC7413832","The coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome coronavirus-2, a new member of the Coronavirus family. The virus was first identified in Wuhan, China, where the epidemic originated. The viral genome was sequenced and a real time reverse transcription polymerase chain reaction assay was developed and used for the detection of virus. Different countries took different approaches for the diagnosis of COVID-19. Some countries prioritized extensive testing for COVID-19 at a very early phase of the pandemic whereas other countries took a long time to build the testing capacity and to implement the testing extensively. The assay design formats were available in the public domain and thereby allowing researchers to replicate them to make diagnostic kits. Consequently, several antigen or antibody-based diagnostic tests were also developed for the diagnosis of COVID-19. However, there were some validation and regulatory challenges while bringing these assays into the market. During the course of the pandemic, it became clear that the countries which implemented testing at an early stage of the pandemic were capable of controlling the spread more effectively than those that implemented them at later stages. As several countries implemented a lockdown for controlling the spread of the virus, it is critical to build the testing capability to meet the extensive need of testing while exiting the lockdown. Testing and isolation of positive cases are the most effective ways of preventing the spread of virus and gradually returning life back to normality.","Antibody; Antigen; COVID-19; Diagnosis; Laboratory testing; RT-qPCR; SARS CoV-2; Serological test","","","Indian Institute of Public Health Gandhinagar, Lekawada, Gandhinagar, Gujarat 382042 India. Biomac Life Sciences Pvt Ltd, Sargasan, Gandhinagar, Gujarat 382421 India. Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243122 India. Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243 122 India.","en","Review","","","","","","",""
"Journal Article","Klompmaker JO,Hart JE,Holland I,Sabath MB,Wu X,et al.","","County-level exposures to greenness and associations with COVID-19 incidence and 1 mortality in the United States 2","europepmc.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://europepmc.org/articles/pmc7480038/bin/94768-2020.08.26.20181644-1.docx","","","","","","… Foundation-Level Data (HIFLD). In addition, we used state level information on number of COVID-19 tests performed up to June 7, 2020 from the COVID tracking project (https://covidtracking.com/). Based on previous studies …","","","","","","","","","","","","",""
"Journal Article","Acosta KL","","Racism: A Public Health Crisis","City & Community","City & Community","2020","19","3","506-515","COVID Tracking Project","","","","SAGE Publications","","","","","2020-09-01","","","1535-6841","","https://doi.org/10.1111/cico.12518;http://dx.doi.org/10.1111/cico.12518;https://journals.sagepub.com/doi/abs/10.1111/cico.12518;https://journals.sagepub.com/doi/full/10.1111/cico.12518","10.1111/cico.12518","","","","","The impact of COVID?19 on racially minoritized communities in the United States has forced us all to look square in the face of the systemic racism that is embedded in every fabric of our society. As the number of infected people continues to rise, the racial disparities are glaringly obvious. Black and Latinx communities have been hit considerably harder by this pandemic. Both racial/ethnic groups have seen rates of infection well above their percentage in the general population and African Americans have seen rates of death from COVID?19 as high as twice their percentage in the general population. These numbers bear witness to the high cost of racism in the United States.","","","","","","","","","","","","",""
"Journal Article","Lu FS,Nguyen AT,Link NB,Lipsitch M,Santillana M","","Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","europepmc.org","","","","","2020-06-18","","","","","http://dx.doi.org/10.1101/2020.04.18.20070821;https://www.ncbi.nlm.nih.gov/pubmed/32587997;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310656;https://doi.org/10.1101/2020.04.18.20070821;https://europepmc.org/articles/pmc7310656/bin/77586-2020.04.18.20070821-1.pdf","10.1101/2020.04.18.20070821","32587997","","","PMC7310656","Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.","","","","","en","Research Article","","","","","","",""
"Journal Article","Sha D,Miao X,Lan H,Stewart K,Ruan S,Tian Y,et al.","","Spatiotemporal Analysis of Medical Resource Deficiencies in the US under COVID-19 Pandemic","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.24.20112136v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/25/2020.05.24.20112136.full.pdf","","","","","","… The confirmed and death cases reflect cumulative statistics since January 22, 2020, the day after the first confirmed cases were reported in Washington State. Furthermore, state level test and hospitalization data were extracted from the COVID Tracking Project [25] …","","","","","","","","","","","","",""
"Preprint Manuscript","Besserve M,Buchholz S,Schölkopf B","","Assaying Large-scale Testing Models to Interpret Covid-19 Case Numbers. A Cross-country Study","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-03","","","","","http://arxiv.org/abs/2012.01912","","","2012.01912","","","Large-scale testing is considered key to assessing the state of the current COVID-19 pandemic, yet interpreting such data remains elusive. We modeled competing hypotheses regarding the underlying testing mechanisms, thereby providing different prevalence estimates based on case numbers, and used them to predict SARS-CoV-2-attributed death rate trajectories. Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor. In contrast, models accounting for testing capacity, limiting the pool of tested individuals, performed better. This puts forward the percentage of positive tests as a robust indicator of epidemic dynamics in absence of country-specific information. We next demonstrated this strongly affects data interpretation. Notably absolute case numbers trajectories consistently overestimated growth rates at the beginning of two COVID-19 epidemic waves. Overall, this supports non-trivial testing mechanisms can be inferred from data and should be scrutinized.","","","","","","","","arXiv","2012.01912","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Ealy H,McEvoy M,Sava M,Gupta S,Chong D,et al.","","Are Children Really Recovering 99.9584% of the Time From COVID-19?","namelyliberty.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://namelyliberty.com/are-children-really-recovering-99-9584-of-the-time-from-covid-19/","","","","","","NEWS: Today's News; Yesterday's News; We The People vs Gavin Newsom; View All Sources. VIDEO: Greg Harvey; Highwire with Del Bigtree; Healthy American Peggy Hall; X22 Report; Corey's Digs; Ben Swann; The Dilley Show; …","","","","","","","","","","","","",""
"Journal Article","Cohen AN,Kessel B","","False positives in reverse transcription PCR testing for SARS-CoV-2","medRxiv","","2020","","","","COVID Tracking Project","","","","frchan.bet","","","","","2020","","","","","https://frchan.bet/.media/d718ce0ec28f9c7b205944820a71d89b658af5043f5f8968d772fe8b5184644a.pdf","","","","","","… Test data are from The COVID Tracking Project (https://covidtracking.com/about-data accessed May 14, 2020). 0% 20% 40% 60% 80% 100 … Test data are from The COVID Tracking Project (https://covidtracking.com/about-data accessed May 14, 2020). 0% 20% 40% 60% 80 …","","","","","","","","","","","","",""
"Journal Article","Scrase SDR","","COVID-19 DAY 101 UPDATE JUNE 19, 2020","","","2020","","","","COVID Tracking Project","","","","nmnn.net","","","","","2020","","","","","https://www.nmnn.net/news/wp-content/uploads/2020/06/NMHSD062220.pdf","","","","","","Page 1. STATE OF NEW MEXICO Human Services Department Governor Michelle Lujan Grisham David R. Scrase, MD, Cabinet Secretary Angela Medrano, Deputy Cabinet Secretary Kari Armijo, Deputy Cabinet Secretary Contact …","","","","","","","","","","","","",""
"Preprint Manuscript","Chen N,Hu M,Zhang C","","Capacitated SIR Model with an Application to COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-14","2020-12-08","","","","https://papers.ssrn.com/abstract=3692751;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3692751;http://dx.doi.org/10.2139/ssrn.3692751;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3692751;https://www.researchgate.net/profile/Ningyuan_Chen/publication/344268188_Capacitated_SIR_Model_with_an_Application_to_COVID-19/links/5f62748592851c07896d7b1f/Capacitated-SIR-Model-with-an-Application-to-COVID-19.pdf","10.2139/ssrn.3692751","","","","","The classical SIR model and its variants have seen great success in understanding and predicting infectious diseases' spread. We extend the SIR model to incorporate the limited testing capacity, which is one of the most notable challenges in the current COVID-19 outbreak. Specifically, based on the SIR model, we impose a testing capacity that is shared among a mix of the infected and virus-free people. In this capacitated SIR model, we show first- and second-order structural properties of two measures, the total infections (confirmed or not) and the case number of undiagnosed infections, with respect to the testing capacity, degree of testing the uninfected (or level of hospital panic run), incubation/testing turnaround time, and infection rate. In particular, we show that in the early stage of a pandemic, the total number of infections is concavely decreasing in the testing capacity and concavely increasing in the degree of testing the uninfected/asymptomatic; the policies to increase the testing capacity and those to reduce the infection rate can be substitutable or complementary, depending on the chosen measure. We use the COVID-19 data to calibrate our model and point out its public policy implications. For example, CDC modified its testing guideline on August 25, 2020, to exclude people who do not have symptoms, and on September 18, 2020, reversed the course by reinforcing the need to test the asymptomatic. Our result suggests that such one-size-fits-all testing guidelines may not be appropriate for all states depending on their testing capacity, and implementing any unequivocal policy would have different impacts depending on the current degree of testing the uninfected/asymptomatic that varies largely across states.","COVID-19, testing capacity, compartmental model, SIR, structural result","","","","","","","","","","","","Available at SSRN 3692751"
"Journal Article","Cain D","","COVID-19 and the Constitution: State Police Powers and Judicial Scrutiny","truman.missouri.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://truman.missouri.edu/sites/default/files/publication/covid-19-and-the-constitution-state-police-powers-and-judicial-scrutiny.pdf","","","","","","… Retrieved from cnbc.com/2020/02/24/ the-nyse-can-halt-trading-amid-stock-selloffs-but-were- far-from-that.html. 3 The COVID Tracking Project . (2020). Our most up-to-date data and annotations. The COVID Tracking Project . Accessed on 25 March 2020 …","","","","","","","","","","","","",""
"Journal Article","Chowell G,Rothenberg R,Roosa K,Tariq A,et al.","","Sub-epidemic model forecasts for COVID-19 pandemic spread in the USA and European hotspots, February-May 2020","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.03.20146159v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/04/2020.07.03.20146159.full.pdf","","","","","","… We also retrieved daily cumulative case count data from The COVID Tracking Project (11) from February 27, 2020 to May 24, 2020 for five representative COVID-19 hotspot states in … The COVID Tracking Project . (Accessed May 3, 2020, at https://covidtracking.com/data.) 10 12 …","","","","","","","","","","","","",""
"Journal Article","Nakano T,Ikeda Y","","Novel indicator of change in COVID-19 spread status","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.25.20080200v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/05/2020.04.25.20080200.full.pdf","","","","","","… https://doi.org/10.1101/2020.04.25.20080200 doi: medRxiv preprint Page 7. 2. The COVID Tracking Project , https://covidtracking.com/data/us-daily 3. NIPPON TELEVISION NETWORK CORPORATION, COVID-19 special site …","","","","","","","","","","","","",""
"Journal Article","Mollalo A,Rivera KM,Vahedi B","","Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2020","17","12","","COVID Tracking Project","","","","mdpi.com","","","","","2020-06-12","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph17124204;https://www.ncbi.nlm.nih.gov/pubmed/32545581;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344609;https://www.mdpi.com/resolver?pii=ijerph17124204;https://www.mdpi.com/1660-4601/17/12/4204;https://www.mdpi.com/1660-4601/17/12/4204/pdf","10.3390/ijerph17124204","32545581","","","PMC7344609","Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.","COVID-19 (Coronavirus); GIS; United States; artificial neural networks; multilayer perceptron","","","Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA. Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA 93106, USA.","en","Research Article","","","","","","",""
"Journal Article","Cohen AN,Kessel B,Milgroom MG","","Diagnosing COVID-19 infection: the danger of over-reliance on positive test results","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.26.20080911v3.full.pdf?fbclid=IwAR0wYN8S9ElX2X3q","","","","","","… trajectories based on the previous-7-day moving average, showing states where the reliability of positive test results has declined significantly (New York), sharply (Oregon), and precipitously (Hawai'i). Test data are from The COVID Tracking Project (https://covidtracking.com …","","","","","","","","","","","","",""
"Report","Friedson AI,McNichols D,Sabia JJ,Dave D","","Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w26992","nber.org","","","","","2020-04-20","2020-12-08","","","","https://www.nber.org/papers/w26992;http://dx.doi.org/10.3386/w26992;https://www.econstor.eu/bitstream/10419/216472/1/dp13160.pdf","10.3386/w26992","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Kochańczyk M,Lipniacki T","","Pareto‐based evaluation of national responses to COVID‐19 pandemic shows that saving lives and protecting economy are non‐trade‐off objectives","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.06.27.20141747v2.full.pdf","","","","","","Page 1. Pareto‐based evaluation of national responses to COVID‐19 pandemic shows that saving lives and protecting economy are non‐trade‐off objectives Marek Kochańczyk & Tomasz Lipniacki Department of Biosystems and Soft Matter …","","","","","","","","","","","","",""
"Journal Article","Victor JN","","The Pandemic Makes Politics Worse","","","2020","","","","COVID Tracking Project","","","","studies.aljazeera.net","","","","","2020","","","","","https://studies.aljazeera.net/sites/default/files/articles/documents/2020-06/The%20Pandemic%20Makes%20Politics%20Worse.pdf","","","","","","… Government, George Mason University, Washington DC. References 1. 'The COVID Tracking Project ', The COVID Tracking Project https://covidtracking.com [accessed 18 June 2020]. 2. Gallup, Inc., 'Presidential Approval Ratings …","","","","","","","","","","","","",""
"Journal Article","Patterson Jr S","","The Politics of Pandemics: The Effect of Stay-At-Home Orders on COVID-19 Mitigation","","","2020","","","","COVID Tracking Project","","","","shawnpattersonjr.com","","","","","2020","","","","","http://www.shawnpattersonjr.com/s/Politics_of_Pandemics.pdf","","","","","","Page 1. The Politics of Pandemics: The Effect of Stay-At-Home Orders on COVID-19 Mitigation* Shawn Patterson, Jr.† August 26, 2020 Abstract The COVID-19 pandemic has upended every aspect of American life. State govern …","","","","","","","","","","","","",""
"Journal Article","Li D,Gaynor SM,Quick C,Chen JT,Stephenson BJK,et al.","","Unraveling US National COVID-19 Racial/Ethnic Disparities using County Level Data Among 328 Million Americans","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.12.02.20234989v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/12/04/2020.12.02.20234989.full.pdf","","","","","","Page 1. 1 Unraveling US National COVID-19 Racial/Ethnic Disparities using County Level 1 Data Among 328 Million Americans 2 3 Short Title: US county COVID-19 health disparities 4 5 Daniel Li,1,2 Sheila M. Gaynor,1 Corbin …","","","","","","","","","","","","",""
"Journal Article","Philippidis A","","5 COVID-19 Test Developers to Watch: The coronavirus pandemic compels diagnostics companies to bring new assays, technologies to market","Clinical OMICs","Clinical OMICs","2020","7","3","14-18","COVID Tracking Project","","","","Mary Ann Liebert, Inc., publishers","","","","","2020-05-01","","","","","https://doi.org/10.1089/clinomi.07.03.19;http://dx.doi.org/10.1089/clinomi.07.03.19;https://www.liebertpub.com/doi/full/10.1089/clinomi.07.03.19?casa_token=0qnKxZuxHt4AAAAA:XTR9FoH1QNlG1UN5Xl6tz2ooDhxrTpFslIFvqzHlCXZmtENKze87GEuIWPQcMeMGYWvD5Jh07K3BWQ","10.1089/clinomi.07.03.19","","","","","… A consensus of experts has agreed that the US needs much more COVID-19 testing than has been conducted as of mid-April, when The COVID Tracking Project tallied nearly 4.5 million total test results (4,493,106 as of 3:13 pm ET, April 23) …","","","","","","","","","","","","",""
"Preprint Manuscript","Batson J,Bottman N,Cooper Y,Janda F","","A comparison of group testing architectures for COVID-19 testing","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-06","","","","","http://arxiv.org/abs/2005.03051","","","2005.03051","","","An important component of every country's COVID-19 response is fast and efficient testing - to identify and isolate cases, as well as for early detection of local hotspots. For many countries, producing a sufficient number of tests has been a serious limiting factor in their efforts to control COVID-19 infections. Group testing is a well-established mathematical tool, which can provide a substantial and inexpensive expansion of testing capacity. In this note, we compare several popular group testing schemes in the context of qPCR testing for COVID-19. We find that in practical settings, for identification of individuals with COVID-19, Dorfman testing is the best choice at prevalences up to 30%, while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing is the best choice at prevalences up to 30% given a fixed and relatively small number of tests. For instance, at a prevalence of up to 2%, Dorfman testing gives an efficiency gain of 3.5--8; at 1% prevalence, Gibbs-Gower testing gives an efficiency gain of 18, even when capping the pool size at a feasible number . This note is intended as a helpful handbook for labs implementing group testing methods.","","","","","","","","arXiv","2005.03051","stat.ME","","","arXiv [stat.ME]"
"Journal Article","Zanettini C,Omar M,Dinalankara W,Imada EL,Colantuoni E,Parmigiani G,Marchionni L","","Influenza Vaccination and COVID19 Mortality in the USA","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-06-26","","","","","http://dx.doi.org/10.1101/2020.06.24.20129817;https://www.ncbi.nlm.nih.gov/pubmed/32607525;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325191;https://doi.org/10.1101/2020.06.24.20129817;https://www.medrxiv.org/content/10.1101/2020.06.24.20129817v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/26/2020.06.24.20129817.full.pdf","10.1101/2020.06.24.20129817","32607525","","","PMC7325191","COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them. In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment. Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment. In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.","","","","","en","Research Article","","","","","","",""
"Journal Article","McCormack WT,Bredella MA,Ingbar DH,Jackson RD,Meagher EA,Morris CD,Nagel JD,Pusek S,Rubio DM,Sandberg K,William Schnaper H,Tsevat J,Umans JG,McIntosh S","","Immediate impact of the COVID-19 pandemic on CTSA TL1 and KL2 training and career development","Journal of Clinical and Translational Science","Journal of Clinical and Translational Science","2020","","","1-6","COVID Tracking Project","","","","Cambridge University Press","","","","","2020","2020-12-08","","2059-8661","","https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science/article/immediate-impact-of-the-covid19-pandemic-on-ctsa-tl1-and-kl2-training-and-career-development/35E639277CE2803BC9A5914D84BD4AB5;http://dx.doi.org/10.1017/cts.2020.504;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/35E639277CE2803BC9A5914D84BD4AB5/S205986612000504Xa.pdf/immediate_impact_of_the_covid19_pandemic_on_ctsa_tl1_and_kl2_training_and_career_development.pdf","10.1017/cts.2020.504","","","","","Clinical and Translational Science Award (CTSA) TL1 trainees and KL2 scholars were surveyed to determine the immediate impact of the COVID-19 pandemic on training and career development. The most negative impact was lack of access to research facilities, clinics, and human subjects, plus for KL2 scholars lack of access to team members and need for homeschooling. TL1 trainees reported having more time to think and write. Common strategies to maintain research productivity involved time management, virtual connections with colleagues, and shifting to research activities not requiring laboratory/clinic settings. Strategies for mitigating the impact of the COVID-19 pandemic on training and career development are described.","TL1; KL2; COVID-19; research training; career development","","","","","","","","","","","",""
"Journal Article","Wagner AB,Hill EL,Ryan SE,Sun Z,Deng G,et al.","","Social Distancing Has Merely Stabilized COVID-19 in the US","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.27.20081836v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/30/2020.04.27.20081836.full.pdf","","","","","","… For the analysis in Appendix A on the confirmed case delay, we use data on COVID-19 cases by illness onset recorded by the CDC [24]. We reference data on testing in New York State in Section 6. This data was obtained from the COVID Tracking Project [25]. 3 Methodology …","","","","","","","","","","","","",""
"Journal Article","Tannous H,Akiki S,Boulos RE,Eid CEK,El Hasbani G,et al.","","SARS-CoV-2 historical global testing and genomic variability","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-89765/latest.pdf","","","","","","… We can list here most notably: Our World in Data (OWID) [1], Johns Hopkins University (JHU) [2], Covid Tracking Project (CTP) (covidtracking.com), Wikipedia (wikipedia.org/wiki/COVID- 19_testing) and Worldometers (worldometers.info/coronavirus) …","","","","","","","","","","","","",""
"Journal Article","Rivera R,Rosenbaum J,Quispe W","","Estimating Excess Deaths in the United States Early in the COVID-19 Pandemic","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.04.20090324v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/08/2020.05.04.20090324.full.pdf","","","","","","Page 1. 1 Estimating Excess Deaths in the United States early in the COVID-19 Pandemic Roberto Rivera, Ph.D. (1)* Janet E. Rosenbaum, Ph.D (2) Walter Quispe, Ph.D. (1) 1. College of Business, University of Puerto Rico at Mayagüez, Mayagüez, Puerto Rico …","","","","","","","","","","","","",""
"Journal Article","Beech BM,Woodard L","","Contact Tracing: A Clarion Call for National Training Standards","Ethn. Dis.","Ethnicity & disease","2020","30","3","437-440","COVID Tracking Project","","","","ethndis.org","","","","","2020-07-09","","","1049-510X","1945-0826","http://dx.doi.org/10.18865/ed.30.3.437;https://www.ncbi.nlm.nih.gov/pubmed/32742148;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360188;https://www.ethndis.org/edonline/index.php/ethndis/article/view/1394;https://www.ethndis.org/edonline/index.php/ethndis/article/download/1394/1892","10.18865/ed.30.3.437","32742148","","","PMC7360188","As of late May 2020, more than 1.5 million people had tested positive for coronavirus infection in the United States; however, only 3% of Americans had been tested. However, testing is only one of the key elements in the effort to control communicable diseases. There is a need to investigate others who may have been exposed to the virus; this can be accomplished through a foundational public health strategy - contact tracing. Most public health students and professionals have been introduced to the concept of contact tracing; however, competency in this area is undetermined. The purpose of this perspective is to call for national standards for contact tracing training programs that lead to a widely recognized certification process.","COVID-19; Contact Tracing; Population Health; Training","","","Office of the Provost, University of Houston, Houston, TX. University of Houston College of Medicine, Houston, TX.","en","Research Article","","","","","","",""
"Journal Article","Amer F,Hammoud S,Farran B,Boncz I,Endrei D","","Assessment of Countries' Preparedness and Lockdown Effectiveness in Fighting COVID-19","Disaster Med. Public Health Prep.","Disaster medicine and public health preparedness","2020","","","1-8","COVID Tracking Project","","","","cambridge.org","","","","","2020-06-24","","","1935-7893","1938-744X","http://dx.doi.org/10.1017/dmp.2020.217;https://www.ncbi.nlm.nih.gov/pubmed/32576332;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411444;https://www.cambridge.org/core/product/identifier/S1935789320002177/type/journal_article;https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/assessment-of-countries-preparedness-and-lockdown-effectiveness-in-fighting-covid19/25C6E77E6C9A1672F6F39888491CB731;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/25C6E77E6C9A1672F6F39888491CB731/S1935789320002177a.pdf/div-class-title-assessment-of-countries-preparedness-and-lockdown-effectiveness-in-fighting-covid-19-div.pdf","10.1017/dmp.2020.217","32576332","","","PMC7411444","OBJECTIVES: The aim of this study was to assess the risks in confronting the coronavirus disease 2019 (COVID-19) pandemic and the ongoing lockdown effectiveness in each of Italy, Germany, Spain, France, and the United States using China's lockdown model simulation, and cases forecast until the plateau phase. METHODS: Quantitative and qualitative historical data analysis. Total Risk Assessment (TRA) evaluation tool was used to assess the pre-pandemic stage risks, pandemic threshold fast responsiveness, and the ongoing performance until plateau. The Infected Patient Ratio (IPR) tool was developed to measure the number of patients resulting from 1 infector during the incubation period. Both IPR and TRA were used together to forecast inflection points, plateau phases, intensive care units' and ventilators' breakpoints, and the Total Fatality Ratio. RESULTS: In Italy, Spain, France, Germany, and the United States, an inflection point is predicted within the first 15 d of April, to arrive at a plateau after another 30 to 80 d. Variations in IPR drop are expected due to variations in lockdown timing by each country, the extent of adherence to it, and the number of performed tests in each. CONCLUSIONS: Both qualitative (TRA) and quantitative (IPR) tools can be used together for assessing and minimizing the pandemic risks and for more precise forecasting.","COVID-19; ICU capacity; forecast; lockdown; pandemic","","","Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, Pécs, Hungary. Faculty of Modern Philology and Social Sciences, University of Pannonia, Veszprém, Hungary.","en","Research Article","","","","","","",""
"Journal Article","Bender M","","Second waves in the US","New Sci.","New scientist ","2020","246","3287","7","COVID Tracking Project","","","","Elsevier","","","","","2020-06-20","","","0262-4079","","http://www.sciencedirect.com/science/article/pii/S0262407920310848;http://dx.doi.org/10.1016/S0262-4079(20)31084-8;https://www.sciencedirect.com/science/article/pii/S0262407920310848","10.1016/S0262-4079(20)31084-8","","","","","Twenty-one US states have seen a surge in covid-19 cases in recent weeks, many after lifting restrictions, reports Maddie Bender","","","","","","","","","","","","",""
"Journal Article","Benfer EA,Vlahov D,Long M,et al.","","Pandemic Housing Policy: Examining the Relationship Among Eviction, Housing Instability, Health Inequity, and COVID-19 Transmission","Journal of Urban","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3736457","","","","","","Page 1. 1 Pandemic Housing Policy: Examining the Relationship Among Eviction, Housing Instability, Health Inequity, and COVID-19 Transmission Journal of Urban Health (in press 2020) Emily A. Benfer JD LLM,a David Vlahov …","","","","","","","","","","","","",""
"Journal Article","Álamo Cantarero T,Gutiérrez Reina D,Mammarella M,et al.","","Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic","","","2020","","","","COVID Tracking Project","","","","idus.us.es","","","","","2020","","","","","https://idus.us.es/handle/11441/96875;https://idus.us.es/bitstream/handle/11441/96875/Electronics_Alamo_Reina_Mammarella_Abella_2020_COVID%2019%20open-data%20%20%281%29.pdf?sequence=2&isAllowed=y","","","","","","Page 1. electronics Review Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic Teodoro Alamo 1,*,† , Daniel G. Reina 2,†, Martina Mammarella 3,† and Alberto Abella 4,† 1 Departamento …","","","","","","","","","","","","",""
"Journal Article","Pett E,Leung HL,Taylor E,Chong MSF,Hla TTW,et al.","","Critical care transfers and COVID-19: managing capacity challenges through critical care networks","","","2020","","","","COVID Tracking Project","","","","preprints.org","","","","","2020","","","","","https://www.preprints.org/manuscript/202010.0125;https://www.preprints.org/manuscript/202010.0125/download/final_file","","","","","","… 6. Mapping the coronavirus outbreak. Graphic: Steven Bernard and Cale Tilford. Sources: ECDC; Covid Tracking Project ; FT. research. Available from: https://www.ft.com/content/ a26fbf7e-48f8-11ea-aeb3- 955839e06441 [Accessed 5 July 2020] …","","","","","","","","","","","","",""
"Journal Article","Fu X","","Global analysis of daily new COVID-19 cases reveals many static-phase countries including US and UK potentially with unstoppable epidemics","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.08.20095356v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/12/2020.05.08.20095356.full.pdf","","","","","","… info/coronavirus/). Levels of daily positive COVID-19 tests in the two most affected US states (New York and New Jersey) were collected from the website of the COVID Tracking Project (https://covidtracking.com/api). The data …","","","","","","","","","","","","",""
"Journal Article","Villas-Boas SB,Sears J,Villas-Boas M,Villas-Boas V","","Are We# StayingHome to Flatten the Curve?","","","2020","","","","COVID Tracking Project","","","","escholarship.org","","","","","2020","","","","","https://escholarship.org/uc/item/5h97n884;https://escholarship.org/content/qt5h97n884/qt5h97n884.pdf","","","","","","Page 1. UC Berkeley CUDARE Working Papers Title Are We #StayingHome to Flatten the Curve? Permalink https://escholarship.org/uc/item/5h97n884 Authors Villas-Boas, Sofia B Sears, James Villas-Boas, Miguel et al. Publication Date 2020-04-05 eScholarship.org …","","","","","","","","","","","","",""
"Preprint Manuscript","Jin X,Wang YX,Yan X","","Inter-Series Attention Model for COVID-19 Forecasting","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-25","","","","","http://arxiv.org/abs/2010.13006","","","2010.13006","","","COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (\textbf{ACTS}), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, \textbf{ACTS} outperforms all the leading COVID-19 forecasters highlighted by CDC.","","","","","","","","arXiv","2010.13006","cs.LG","","","arXiv [cs.LG]"
"Journal Article","Picone M,Inoue S,DeFelice C,Naujokas MF,Sinrod J,Cruz VA,Stapleton J,Sinrod E,Diebel SE,Wassman , Jr ER","","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.","Population health management","2020","23","5","350-360","COVID Tracking Project","","","","liebertpub.com","","","","","2020-10","","","1942-7891","1942-7905","http://dx.doi.org/10.1089/pop.2020.0189;https://www.ncbi.nlm.nih.gov/pubmed/32897820;https://www.liebertpub.com/doi/10.1089/pop.2020.0189?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.liebertpub.com/doi/abs/10.1089/POP.2020.0189;https://www.liebertpub.com/doi/pdf/10.1089/pop.2020.0189","10.1089/pop.2020.0189","32897820","","","","Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.","COVID-19; content analysis; data mining; disease natural histories; social listening; social media","","","TREND Community, Philadelphia, Pennsylvania, USA. Documented.space, Brooklyn, New York, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Khan ZS,Van Bussel F,Hussain F","","On the dramatic change in COVID-19 mortality rate: modeled and examined for Europe and the US","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-10","","","","","http://arxiv.org/abs/2011.05368","","","2011.05368","","","Our compartment model shows that the COVID-19 case mortality rate decreased by at least 80 percent in most of the US states and at least 90 percent in most European countries. These drops are much larger and faster than those previously reported and surprisingly do not correlate well with our model parameters (such as the contact rate or testing rate) or state/national metrics such as the population density, GDP, and median age. Surprisingly, these changes mostly occurred between mid-April and mid-June -- at a time when lockdown relaxations in many states and countries caused surges of new cases. Plausible causes for this drop are discussed, such as improvements in treatments, face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases.","","","","","","","","arXiv","2011.05368","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Adolph C,Amano K,Bang-Jensen B,Fullman N,et al.","","Governor partisanship explains the adoption of statewide mandates to wear face coverings","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.31.20185371v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/02/2020.08.31.20185371.full.pdf","","","","","","… Alt Measure: Deaths per Million, 7-Day Avg. New York Times COVID Tracking Project Johns Hopkins University New York Times COVID Tracking Project Johns Hopkins University COVID Tracking Project Democratic governor hazard ratio epidemiological hazard ratio …","","","","","","","","","","","","",""
"Journal Article","Anand S,Montez-Rath M,Han J,Bozeman J,Kerschmann R,Beyer P,Parsonnet J,Chertow GM","","Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study","Lancet","The Lancet","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020-09-25","","","0140-6736","1474-547X","http://dx.doi.org/10.1016/S0140-6736(20)32009-2;https://www.ncbi.nlm.nih.gov/pubmed/32987007;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518804;https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(20)32009-2;https://www.sciencedirect.com/science/article/pii/S0140673620320092;https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32009-2/fulltext?fbclid=IwAR0A_fjgRX-AHOQ_OAO7OiSzSiVzKbT2zd_yCrDKDouMJFw3JC4x__JpH5k","10.1016/S0140-6736(20)32009-2","32987007","","","PMC7518804","BACKGROUND: Many patients receiving dialysis in the USA share the socioeconomic characteristics of underserved communities, and undergo routine monthly laboratory testing, facilitating a practical, unbiased, and repeatable assessment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence. METHODS: For this cross-sectional study, in partnership with a central laboratory that receives samples from approximately 1300 dialysis facilities across the USA, we tested the remainder plasma of 28 503 randomly selected adult patients receiving dialysis in July, 2020, using a spike protein receptor binding domain total antibody chemiluminescence assay (100% sensitivity, 99·8% specificity). We extracted data on age, sex, race and ethnicity, and residence and facility ZIP codes from the anonymised electronic health records, linking patient-level residence data with cumulative and daily cases and deaths per 100 000 population and with nasal swab test positivity rates. We standardised prevalence estimates according to the overall US dialysis and adult population, and present estimates for four prespecified strata (age, sex, region, and race and ethnicity). FINDINGS: The sampled population had similar age, sex, and race and ethnicity distribution to the US dialysis population, with a higher proportion of older people, men, and people living in majority Black and Hispanic neighbourhoods than in the US adult population. Seroprevalence of SARS-CoV-2 was 8·0% (95% CI 7·7-8·4) in the sample, 8·3% (8·0-8·6) when standardised to the US dialysis population, and 9·3% (8·8-9·9) when standardised to the US adult population. When standardised to the US dialysis population, seroprevalence ranged from 3·5% (3·1-3·9) in the west to 27·2% (25·9-28·5) in the northeast. Comparing seroprevalent and case counts per 100 000 population, we found that 9·2% (8·7-9·8) of seropositive patients were diagnosed. When compared with other measures of SARS-CoV-2 spread, seroprevalence correlated best with deaths per 100 000 population (Spearman's ρ=0·77). Residents of non-Hispanic Black and Hispanic neighbourhoods experienced higher odds of seropositivity (odds ratio 3·9 [95% CI 3·4-4·6] and 2·3 [1·9-2·6], respectively) compared with residents of predominantly non-Hispanic white neighbourhoods. Residents of neighbourhoods in the highest population density quintile experienced increased odds of seropositivity (10·3 [8·7-12·2]) compared with residents of the lowest density quintile. County mobility restrictions that reduced workplace visits by at least 5% in early March, 2020, were associated with lower odds of seropositivity in July, 2020 (0·4 [0·3-0·5]) when compared with a reduction of less than 5%. INTERPRETATION: During the first wave of the COVID-19 pandemic, fewer than 10% of the US adult population formed antibodies against SARS-CoV-2, and fewer than 10% of those with antibodies were diagnosed. Public health efforts to limit SARS-CoV-2 spread need to especially target racial and ethnic minority and densely populated communities. FUNDING: Ascend Clinical Laboratories.","","","","Division of Nephrology, Stanford University, Palo Alto, CA, USA. Electronic address: sanand2@stanford.edu. Division of Nephrology, Stanford University, Palo Alto, CA, USA. Ascend Clinical Laboratory, Redwood City, CA, USA. Division of Infectious Diseases & Geographic Medicine, Stanford University, Palo Alto, CA, USA; Department of Medicine, and Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA. Division of Nephrology, Stanford University, Palo Alto, CA, USA; Department of Medicine, and Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA.","en","Research Article","","","","","","",""
"Journal Article","Huntley RE,Ludwig DC,Dillon JK","","The Early Effect of COVID-19 on Oral and Maxillofacial Surgery Residency Training-Results from a National Survey","Journal of Oral and Maxillofacial","","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020","","","","","https://www.sciencedirect.com/science/article/pii/S0278239120305504;https://www.joms.org/article/S0278-2391(20)30550-4/fulltext","","","","","","… 2020. Available at: https://www.aaoms.org/docs/education_research/edu_training/ aaoms_faculty_resident_summary.pdf. Accessed May 1, 2020. Google Scholar. 9 The COVID Tracking Project : Most Recent Data. 2020. Available at: https://covidtracking.com/data …","","","","","","","","","","","","",""
"Journal Article","Jella TK,Desai A,Jella T,Steinmetz M,Kimmell K,Wright J,Wright CH,Council of State Neurosurgical Societies (CSNS)","","Geospatial Distribution of Neurosurgeons Age 60 and Older Relative to the Spread of COVID-19","World Neurosurg.","World neurosurgery","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020-10-14","","","1878-8750","1878-8769","http://dx.doi.org/10.1016/j.wneu.2020.10.037;https://www.ncbi.nlm.nih.gov/pubmed/33065354;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553865;https://linkinghub.elsevier.com/retrieve/pii/S1878-8750(20)32229-4;https://www.sciencedirect.com/science/article/pii/S1878875020322294","10.1016/j.wneu.2020.10.037","33065354","","","PMC7553865","OBJECTIVE: To perform an ecological study to analyze the geospatial distribution of neurosurgeons ≥60 years old and compare these data with the spread of 2019 novel coronavirus disease (COVID-19) across the United States. METHODS: Data regarding distribution of COVID-19 cases were collected from the Environmental Systems Research Institute, and demographic statistics were collected from the American Association of Medical Colleges 2019 State Workforce Reports. These figures were analyzed using geospatial mapping software. RESULTS: As of July 5, 2020, the 10 states with the highest number of COVID-19 cases showed older neurosurgical workforce proportions (the proportion of active surgeons ≥60 years old) of 20.6%-38.9%. Among states with the highest number of COVID-19 deaths, the older workforce proportions were 25.0%-43.4%. Connecticut demonstrated the highest with 43.4% of neurosurgeons ≥60 years old. CONCLUSIONS: Regional COVID-19 hotspots may coincide with areas where a substantial proportion of the neurosurgical workforce is ≥60 years old. Continuous evaluation and adjustment of local and national clinical practice guidelines are warranted throughout the pandemic era.","Age distribution; At-risk; COVID-19; Coronavirus; Neurosurgeons; Pandemic","","","Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. College of Arts and Sciences, Emory University, Atlanta, Georgia, USA. Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA. Department of Neurosurgery, Rochester Regional Health and University of Rochester Medical Center, Rochester, New York, USA. Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA; Department of Neurosurgery, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA. Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA; Department of Neurosurgery, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA. Electronic address: christina.lee.huang@gmail.com.","en","Research Article","","","","","","",""
"Preprint Manuscript","Vinod HD,Theiss K","","A Novel Solution to Biased Data in COVID-19 Incidence Studies","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-29","2020-12-08","","","","https://papers.ssrn.com/abstract=3637682;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3637682;http://dx.doi.org/10.2139/ssrn.3637682;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3637682;http://legacy.fordham.edu/economics/vinod/virus1.pdf","10.2139/ssrn.3637682","","","","","Complete novelty and uncertainty of the COVID-19 pandemic have created many challenging scientific problems, including biased data arising from a lack of randomized testing over the general population. We describe the bias problem and its solution from Econometrics literature, which seems to have been neglected by epidemiology experts. We study a large COVID-19 US data set, providing nationwide forecasts of deaths to illustrate the model's power. Our two-equation model overcomes the bias by using the inverse Mills ratio and improves forecasts of new deaths in all nine out of nine weekly out-of-sample comparisons. It can be applied to a variety of problems associated with the pandemic. A focused study of trends in deaths predicted by lagged cumulative infections reveals that forty-two states have negative trends and that seven of nine states with undesirable positive trends have Republican governors.","Inverse Mills Ratio, Selection Models, Poisson probit model, outcome equation","","","","","","","","","","","","Available at SSRN 3637682"
"Journal Article","Brunner J,Chia N","","Confidence in the dynamic spread of epidemics under biased sampling conditions","PeerJ","PeerJ","2020","8","","e9758","COVID Tracking Project","","","","arxiv.org","","","","","2020-08-14","","","2167-8359","","http://dx.doi.org/10.7717/peerj.9758;https://www.ncbi.nlm.nih.gov/pubmed/32864224;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430303;https://doi.org/10.7717/peerj.9758;http://arxiv.org/abs/2006.02961","10.7717/peerj.9758","32864224","2006.02961","","PMC7430303","The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under 10,000 biased samples each day, and an estimate of disease spread can be made with additional 1,000-2,000 unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.","COVID-19; Epidemic Sampling; Population modeling","","","Center for Individualized Medicine, Department of Surgery, Mayo Clinic, Rochester, MN, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Bertocchi G,Dimico A","","Covid-19, Race, and Redlining","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-01","2020-12-08","","","","https://papers.ssrn.com/abstract=3650128;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3650128;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3650128;https://www.medrxiv.org/content/medrxiv/early/2020/07/20/2020.07.11.20148486.full.pdf","","","","","","Discussion on the disproportionate impact of COVID-19 on African Americans has been at center stage since the outbreak of the epidemic in the United States. To present day, however, lack of race-disaggregated individual data has prevented a rigorous assessment of the extent of this phenomenon and the reasons why blacks may be particularly vulnerable to the disease. Using individual and georeferenced death data collected daily by the Cook County Medical Examiner, we provide first evidence that race does affect COVID-19 outcomes. The data confirm that in Cook County blacks are overrepresented in terms of COVID-19 related deaths since---as of June 16, 2020---they constitute 35 percent of the dead, so that they are dying at a rate 1.3 times higher than their population share. Furthermore, by combining the spatial distribution of mortality with the 1930s redlining maps for the Chicago area, we obtain a block group level panel dataset of weekly deaths over the period January 1, 2020-June 16, 2020, over which we establish that, after the outbreak of the epidemic, historically lower-graded neighborhoods display a sharper increase in mortality, driven by blacks, while no pre-treatment differences are detected. Thus, we uncover a persistence influence of the racial segregation induced by the discriminatory lending practices of the 1930s, by way of a diminished resilience of the black population to the shock represented by the COVID-19 outbreak. A heterogeneity analysis reveals that the main channels of transmission are socioeconomic status and household composition, whose influence is magnified in combination with a higher black share.","blacks, Chicago, Cook County, COVID-19, deaths, redlining, Vulnerability","","","","","","","","","","","",""
"Journal Article","Reilly SE,Zane KL,McCuddy WT,Soulliard ZA,Scarisbrick DM,Miller LE,Mahoney Iii JJ","","Mental Health Practitioners' Immediate Practical Response During the COVID-19 Pandemic: Observational Questionnaire Study","JMIR Ment Health","JMIR mental health","2020","7","9","e21237","COVID Tracking Project","","","","mental.jmir.org","","","","","2020-10-01","","","2368-7959","","http://dx.doi.org/10.2196/21237;https://www.ncbi.nlm.nih.gov/pubmed/32931440;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546864;https://mental.jmir.org/2020/9/e21237/;https://mental.jmir.org/2020/10/e21237?utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_1","10.2196/21237","32931440","","","PMC7546864","BACKGROUND: The COVID-19 pandemic has been associated with increased psychological distress, signaling the need for increased mental health services in the context of stay-at-home policies. OBJECTIVE: This study aims to characterize how mental health practitioners have changed their practices during the pandemic. The authors hypothesize that mental health practitioners would increase tele-mental health services and that certain provider types would be better able to adapt to tele-mental health than others. METHODS: The study surveyed 903 practitioners, primarily psychologists/doctoral-level (Psych/DL) providers, social workers/master's-level (SW/ML) providers, and neuropsychologists employed in academic medical centers or private practices. Differences among providers were examined using Bonferroni-adjusted chi-square tests and one-way Bonferroni-adjusted analyses of covariance. RESULTS: The majority of the 903 mental health practitioners surveyed rapidly adjusted their practices, predominantly by shifting to tele-mental health appointments (n=729, 80.82%). Whereas 80.44% (n=625) were not using tele-mental health in December 2019, only 22.07% (n=188) were not by late March or early April 2020. Only 2.11% (n=19) reported no COVID-19-related practice adjustments. Two-thirds (596/888, 67.10%) reported providing additional therapeutic services specifically to treat COVID-19-related concerns. Neuropsychologists were less likely and Psych/DL providers and SW/ML providers were more likely than expected to transition to tele-mental health (P<.001). Trainees saw fewer patients (P=.01) and worked remotely more than licensed practitioners (P=.03). Despite lower rates of information technology service access (P<.001), private practice providers reported less difficulty implementing tele-mental health than providers in other settings (P<.001). Overall, the majority (530/889, 59.62%) were interested in continuing to provide tele-mental health services in the future. CONCLUSIONS: The vast majority of mental health providers in this study made practice adjustments in response to COVID-19, predominantly by rapidly transitioning to tele-mental health services. Although the majority reported providing additional therapeutic services specifically to treat COVID-19-related concerns, only a small subset endorsed offering such services to medical providers. This has implications for future practical directions, as frontline workers may begin to seek mental health treatment related to the pandemic. Despite differences in tele-mental health uptake based on provider characteristics, the majority were interested in continuing to provide such services in the future. This may help to expand clinical services to those in need via tele-mental health beyond the COVID-19 pandemic.","COVID-19; clinical practice; mental health; survey; tele–mental health","","","Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, United States. Department of Neuroscience, West Virginia University, Morgantown, WV, United States. Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.","en","Research Article","","","","","","",""
"Journal Article","Sen BP,Padalabalanarayanan S,Hanumanthu VS","","Stay-at-Home Orders, African American Population, Poverty and State-level Covid-19 Infections: Are there associations?","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.17.20133355v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/19/2020.06.17.20133355.full.pdf","","","","","","… incidence of COVID-19 using state-level observational data. Data on COVID-19 positive cases (hereafter 'cases') and COVID-19 tests (hereafter 'tests') are obtained from 'The COVID Tracking Project ' (TCTP) 11 . Initiated by …","","","","","","","","","","","","",""
"Journal Article","Cafer A,Rosenthal M","","COVID-19 in the Rural South: A Perfect Storm of Disease, Health Access, and Co-Morbidity","","","2020","","","","COVID Tracking Project","","","","egrove.olemiss.edu","","","","","2020","2020-12-08","","","","https://egrove.olemiss.edu/apcrl_policybriefs/2/;https://egrove.olemiss.edu/cgi/viewcontent.cgi?article=1001&context=apcrl_policybriefs","","","","","","By Anne Cafer and Meagen Rosenthal, Published on 04/13/20","","","","","","","APCRL Policy Briefs","","","","","",""
"Journal Article","Fowler L,Kettler JJ,Witt SL","","Pandemics and Partisanship: Following Old Paths into Uncharted Territory","American Politics Research","American Politics Research","2021","49","1","3-16","COVID Tracking Project","","","","SAGE Publications Inc","","","","","2021-01-01","","","1532-673X","","https://doi.org/10.1177/1532673X20961024;http://dx.doi.org/10.1177/1532673X20961024;https://journals.sagepub.com/doi/abs/10.1177/1532673X20961024?casa_token=cCIBDWK0IdQAAAAA:uVZKcmaha82hF1jAUYt9Lz7KiaYq94H4PJSYyxYTGZYg5G0cEEQez6dFQwDeSq67kM4roXnn8x4D;https://journals.sagepub.com/doi/pdf/10.1177/1532673X20961024?casa_token=JTkQ_zYobtwAAAAA:8FxkAPJaUm5eq2Z1sOo9z73jnSjKcRXoZ0lqzoGsDRuD4kOh3ltie_KMp49GH5hcfbi3Q10INUXY","10.1177/1532673X20961024","","","","","Although partisan politics tend be set aside during crisis, the timing of gubernatorial actions in response to COVID-19 is telling about how partisanship is shaping the way elected officials are reacting to this pandemic. Using an event history analysis, the authors find that Democratic governors responded to the White House?s attempts to downplay the severity of the pandemic by declaring emergencies in order to draw citizen attention to and to prepare for a public health crisis. On the other hand, Republican governors resisted doing so until Trump declared a national emergency on March 13; however, Republican reactions were conditional on the president?s job approval in their states. While the COVID-19 pandemic has pushed governments into uncharted territory, state governors appear to be following patterns of vertical partisan competition that mirror those of more conventional policy areas in recent years.","","","","","","","","","","","","",""
"Journal Article","White ER,Hébert-Dufresne LR","","State-level variation of initial COVID-19 dynamics in the United States: The role of local government interventions","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.14.20065318v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/17/2020.04.14.20065318.full.pdf","","","","","","… volunteer rate (2015 Corporation for National and Community Service data https://www. nationalservice.gov/vcla/state-rankings-volunteer-rate) • tightness scores Harrington & Gelfand (2014) • testing rates by state ( COVID Tracking Project https://covidtracking.com/) References …","","","","","","","","","","","","",""
"Journal Article","Sears J,Villas-Boas JM,Villas-Boas V,et al.","","Are we# stayinghome to Flatten the Curve?","of Agricultural and …","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3579691;https://www.medrxiv.org/content/medrxiv/early/2020/05/26/2020.05.23.20111211.full.pdf","","","","","","… 155 We obtain information on coronavirus test outcomes along with hospitalizations and 156 deaths from COVID-19 by state from the COVID Tracking Project [20]. Published 157 values are obtained directly from the respective public health authorities, supplemented 158 …","","","","","","","","","","","","",""
"Journal Article","Pence M,Pence DVP","","Time to Act","","","2020","","","","COVID Tracking Project","","","","idsociety.org","","","","","2020","","","","","https://www.idsociety.org/globalassets/idsa/public-health/covid-19/dpa-testing-ppe.pdf","","","","","","… Part 1: The Future of the COVID-19 Pandemic: Lessons Learned from Pandemic Influenza. April 30, 2020. https://www.cidrap.umn.edu/sites/default/files/public/downloads/cidrap-covid19- viewpoint-part1_0.pdf vii COVID Tracking Project . US Cumulative Daily Totals …","","","","","","","","","","","","",""
"Journal Article","Weitz JS,Park SW,Eksin C,Dushoff J","","Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations","Proc. Natl. Acad. Sci. U. S. A.","Proceedings of the National Academy of Sciences of the United States of America","2020","","","","COVID Tracking Project","","","","National Acad Sciences","","","","","2020-12-01","","","0027-8424","1091-6490","http://dx.doi.org/10.1073/pnas.2009911117;https://www.ncbi.nlm.nih.gov/pubmed/33262277;http://www.pnas.org/cgi/pmidlookup?view=long&pmid=33262277;https://www.pnas.org/content/early/2020/11/30/2009911117.short;https://www.pnas.org/content/pnas/early/2020/11/30/2009911117.full.pdf","10.1073/pnas.2009911117","33262277","","","","The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with plateau- or shoulder-like phenomena-a qualitative observation reinforced by a symmetry analysis of US state-level fatality data. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, fast increases to the peak are often followed by plateaus, shoulders, and lag-driven oscillations. The asymmetric shape of model-predicted incidence and fatality curves is consistent with observations from many jurisdictions. Yet, in contrast to model predictions, we find that population-level mobility metrics usually increased from low levels before fatalities reached an initial peak. We show that incorporating fatigue and long-term behavior change can reconcile the apparent premature relaxation of mobility reductions and help understand when post-peak dynamics are likely to lead to a resurgence of cases.","control; epidemics; epidemiology; nonlinear dynamics; public health","","","School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-0230; jsweitz@gatech.edu. School of Physics, Georgia Institute of Technology, Atlanta, GA 30332-0230. Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA 30332-0230. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843. Department of Biology, McMaster University, Hamilton, ON L8S 4L8, Canada. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4L8, Canada.","en","Research Article","","","","","","",""
"Journal Article","Gigliotti P,Martin EG","","Predictors of State-Level Stay-at-Home Orders in the United States and Their Association With Mobility of Residents","J. Public Health Manag. Pract.","Journal of public health management and practice: JPHMP","2020","26","6","622-631","COVID Tracking Project","","","","ingentaconnect.com","","","","","2020","","","1078-4659","1550-5022","http://dx.doi.org/10.1097/PHH.0000000000001236;https://www.ncbi.nlm.nih.gov/pubmed/32969952;https://doi.org/10.1097/PHH.0000000000001236;https://www.ingentaconnect.com/content/wk/phh/2020/00000026/00000006/art00023","10.1097/PHH.0000000000001236","32969952","","","","OBJECTIVE: To evaluate predictors of stay-at-home order adoption among US states, as well as associations between order enactment and residents' mobility. DESIGN: We assess associations between state characteristics and adoption timing. We also assess associations between enactment and aggregate state-level measures of residents' mobility (Google COVID-19 Community Mobility Reports). SETTING: The United States. PARTICIPANTS: Adoption population: 50 US states and District of Columbia. Mobility population: state residents using devices with GPS tracking accessible by Google. INTERVENTION AND EXPOSURES: State characteristics: COVID-19 diagnoses per capita, 2016 Trump vote share, Republican governor, Medicaid expansion status, hospital beds per capita, public health funding per capita, state and local tax revenue per capita, median household income, population, percent residents 65 years or older, and percent urban residents. Mobility exposure: indicator of order enactment by March 29, 2020 (date of mobility data collection). MAIN OUTCOME MEASURES: Order adoption timing: days since adoption of first order. Mobility: changes in mobility to 6 locations from February 6 to March 29, 2020. RESULTS: In bivariate models, order adoption was associated with COVID-19 diagnoses (hazard ratio [HR] = 1.01; 95% confidence interval [CI], 1.00 to 1.01), Republican governor (HR = 0.24; 95% CI, 0.13 to 0.44), Medicaid expansion (HR = 2.50; 95% CI, 1.40 to 4.48), and hospital capacity (HR = 0.43; 95% CI, 0.26 to 0.70), consistent with findings in the multivariate models. Order enactment was positively associated with time at home (beta (B) = 1.31; 95% CI, 0.35 to 2.28) and negatively associated with time at retail and recreation (B = -7.17; 95% CI, -10.89 to -3.46) and grocery and pharmacy (B = -8.28; 95% CI, -11.97 to -4.59) locations. Trump vote share was associated with increased mobility for 4 of 6 mobility measures. CONCLUSIONS AND RELEVANCE: While politics influenced order adoption, public health considerations were equally influential. While orders were associated with decreased mobility, political ideology was associated with increased mobility under social distancing policies.","","","","Department of Public Administration and Policy, Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York (SUNY), Albany, New York.","en","Research Article","","","","","","",""
"Preprint Manuscript","Stoye J","","Bounding Disease Prevalence by Bounding Selectivity and Accuracy of Tests: The Case of COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-14","","","","","http://arxiv.org/abs/2008.06178","","","2008.06178","","","I propose novel partial identification bounds on disease prevalence from information on test rate and test yield. The approach broadly follows recent work by \cite{MM20} on COVID-19, but starts from user-specified bounds on (i) test accuracy, in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful elsewhere. Evaluated on data from the pandemic's early stage, even the weakest of the novel bounds are reasonably informative. For example, they place the infection fatality rate for Italy well above the one of influenza by mid-April.","","","","","","","","arXiv","2008.06178","econ.EM","","","arXiv [econ.EM]"
"Journal Article","Miikkulainen R,Francon O,Meyerson E,Qiu X,et al.","","From Prediction to Prescription: AI-Based Optimization of Non-Pharmaceutical Interventions for the COVID-19 Pandemic","arXiv preprint arXiv","","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020","","","","","https://arxiv.org/abs/2005.13766;https://arxiv.org/pdf/2005.13766","","","","","","… For instance in the US, such datasets started to come out only in April [31, 35, 63]. Based on data from the Covid Tracking Project [63], University of Washington's Institute for Health Metrics and Evaluation (IHME) developed a dashboard that shows the NPI timeline [26] …","","","","","","","","","","","","",""
"Journal Article","Aragón‐Caqueo D,et al.","","Optimization of group size in pool testing strategy for SARS‐CoV‐2: A simple mathematical model","Journal of Medical","","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","","","https://onlinelibrary.wiley.com/doi/abs/10.1002/jmv.25929;https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.25929","","","","","","… 2020;(March):6 Available from: https://apps.who.int/iris/bitstream/handle/10665/331509/WHO- COVID-19- lab_testing-2020.1-eng.pdf 15. The COVID Tracking Project . US Historical Data. Available from: https://www.covidtracking.com/data/us-daily [Accessed April 19th 2020]. 16 …","","","","","","","","","","","","",""
"Journal Article","Menifield CE,Clark C","","Pandemic Planning in the US: An Examination of COVID‐19 Data","Public Adm. Rev.","Public administration review","","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","","","","0033-3352","","https://onlinelibrary.wiley.com/doi/abs/10.1111/puar.13326","","","","","","Page 1. Pandemic Planning in the US: An Examination of COVID-19 Data Charles E. Menifield Charles.menifield@rutgers.edu Rutgers University Newark 111 Washington St. Newark, NJ 07102 901-644-2448 Cal Clark clarkcm@auburn.edu 2507 Waterford Rd …","","","","","","","","","","","","",""
"Journal Article","Pozo-Martin F,Cristea F,El Bcheraoui C","","Rapid Review der Wirksamkeit nicht-pharmazeutischer Interventionen bei der Kontrolle der COVID-19-Pandemie","rki.de","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Projekte_RKI/Rapid-Review-NPIs.pdf?__blob=publicationFile","","","","","","… Weekly growth rate in infections Econometric structural outcomes model. Data on cases and mortality are from the New York Times, Johns Hopkins University and the Covid Tracking Project Data on policies is from the COVID-19 US policy database …","","","","","","","","","","","","",""
"Journal Article","Strauss AT,Boyarsky BJ,et al.","","Liver transplantation in the United States during the COVID‐19 pandemic: national and center‐level responses","American Journal of","","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","","","https://onlinelibrary.wiley.com/doi/abs/10.1111/ajt.16373?casa_token=UZaw6hiPQzsAAAAA:GDfV1L8xur2vNOZlBnt9qWVCdaWzKhJyF6QhPbbJkDh7i8bl-aPk553w2_Pf-QwuTVPimRBlah4l8XA;https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajt.16373?casa_token=MdZaUV6ebE4AAAAA:NAd0qxQn702oISUPa9DTQ5YfbJitiwNTXmXuDty03L9ko83zidNHOdQPEl8VPyro4ZdxmM11nJkOIag","","","","","","Page 1. This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record …","","","","","","","","","","","","",""
"Journal Article","Nayak A,Islam SJ,Mehta A,Ko YA,Patel SA,Goyal A,Sullivan S,Lewis TT,Vaccarino V,Morris AA,Quyyumi AA","","Impact of Social Vulnerability on COVID-19 Incidence and Outcomes in the United States","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-04-14","","","","","http://dx.doi.org/10.1101/2020.04.10.20060962;https://www.ncbi.nlm.nih.gov/pubmed/32511437;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217093;https://doi.org/10.1101/2020.04.10.20060962;https://www.medrxiv.org/content/10.1101/2020.04.10.20060962v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/17/2020.04.10.20060962.full.pdf","10.1101/2020.04.10.20060962","32511437","","","PMC7217093","Importance Prior pandemics have disparately affected socially vulnerable communities. Whether regional variations in social vulnerability to disasters influence COVID-19 outcomes and incidence in the U.S. is unknown. Objective To examine the association of Social Vulnerability Index (SVI), a percentile-based measure of county-level social vulnerability to disasters, and its sub-components (socioeconomic status, household composition, minority status, and housing type/transportation accessibility) with the case fatality rate (CFR) and incidence of COVID-19. Design Ecological study of counties with at least 50 confirmed COVID-19 cases as of April 4th, 2020. Generalized linear mixed-effects models with state-level clustering were applied to estimate county-level associations of overall SVI and its sub-component scores with COVID-19 CFR (deaths/100 cases) and incidence (cases/1000 population), adjusting for population percentage aged >65 years, and for comorbidities using the average Hierarchical Condition Category (HCC) score. Counties with high SVI (≥median) and high CFR (≥median) were identified. Setting Population-based study of U.S. county-level data. Participants U.S. counties with at least 50 confirmed COVID-19 cases. Main outcomes and measures COVID-19 CFR and incidence. Results Data from 433 counties including 283,256 cases and 6,644 deaths were analyzed. Median SVI was 0.46 [Range: 0.01-1.00], and median CFR and incidence were 1.9% [Range: 0-13.3] and 1.2 per 1000 people [Range: 0.6-38.8], respectively. Higher SVI, indicative of greater social vulnerability, was associated with higher CFR (RR: 1.19 [1.05, 1.34], p=0.005, per-1 unit increase), an association that strengthened after adjustment for age>65 years and comorbidities (RR: 1.63 [1.38, 1.91], p<0.001), and was further confirmed in a sensitivity analysis limited to six states with the highest testing levels. Although the association between overall SVI and COVID-19 incidence was not significant, the SVI sub-components of socioeconomic status and minority status were both predictors of higher incidence and CFR. A combination of high SVI (≥0.46) and high adjusted CFR (≥2.3%) was observed in 28.9% of counties. Conclusions and Relevance Social vulnerability is associated with higher COVID-19 case fatality. High social vulnerability and CFR coexist in more than 1 in 4 U.S. counties. These counties should be targeted by public policy interventions to help alleviate the pandemic burden on the most vulnerable population.","","","","","en","Research Article","","","","","","",""
"Journal Article","Vardavas R,Strong A,Bouey J,Welburn JW,et al.","","The Health and Economic Impacts of Nonpharmaceutical Interventions to Address COVID-19","","","2020","","","","COVID Tracking Project","","","","rand.org","","","","","2020","","","","","https://www.rand.org/content/dam/rand/pubs/tools/TLA100/TLA173-1/RAND_TLA173-1.pdf","","","","","","… Harvard Global Health Institute, 2020 State testing, deaths, and hospitalization time series These data show the daily flows and stocks of positive tests, negative tests, hospitalizations, and deaths for each state. COVID Tracking Project , undated …","","","","","","","","","","","","",""
"Preprint Manuscript","Ho P,Lubik T,Matthes C","","How to Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-01","2020-12-08","","","","https://papers.ssrn.com/abstract=3701996;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3701996;http://dx.doi.org/10.21144/wp20-10;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3701996;https://fraser.stlouisfed.org/files/docs/historical/frbrich/covid-19/frbrich_covid19_paper_20200821_wp20-10.pdf","10.21144/wp20-10","","","","","This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.","Bayesian Estimation, Panel, Time-Varying Parameters","","","","","","","","","","","",""
"Journal Article","Elton ES,Duoss EB,Tooker AC,Spadaccini CM","","Domestic Supply Chain of Medical Consumables Needed During COVID-19 Pandemic","","","2020","","","","COVID Tracking Project","","","","osti.gov","","","","","2020","","","","","https://www.osti.gov/servlets/purl/1634295","","","","","","… Mar. 2020. https://www.statnews.com/2020/03/14/thermo-fisher-to-produce-millions- of-coronavirus- diagnostic-tests/ [19] “US Historical Data.” The COVID Tracking Project , https://covidtracking.com/data/us-daily. Accessed 18 …","","","","","","","","","","","","",""
"Journal Article","Brennan E","","Coronavirus and protest: How Covid-19 has changed the face of American activism","","","2020","","","","COVID Tracking Project","","","","united-states-studies-centre.s3 …","","","","","2020","","","","","http://united-states-studies-centre.s3.amazonaws.com/uploads/29e/a82/a36/29ea82a364b1b39344aaa313e2d67b7c5653c099/Coronavirus-and-protest-How-COVID-19-has-changed-the-face-of-American-activism.pdf","","","","","","Page 1. CORONAVIRUS AND PROTEST: HOW COVID-19 HAS CHANGED THE FACE OF AMERICAN ACTIVISM ELLIOTT BRENNAN | MAY 2020 Page 2. The United States Studies Centre at the University of Sydney is a university …","","","","","","","","","","","","",""
"Preprint Manuscript","Trueblood JS,Sussman AB,O'Leary D","","The Role of General Risk Preferences in Messaging About COVID-19 Vaccine Take-Up","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-12","2020-12-08","","","","https://papers.ssrn.com/abstract=3649654;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3649654;http://dx.doi.org/10.2139/ssrn.3649654;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3649654","10.2139/ssrn.3649654","","","","","Development of an effective vaccine that protects against COVID-19 is widely considered one of the best paths to ending the current health crisis. While the ability to create and distribute such a vaccine in the short-term remains uncertain, the availability of a vaccine alone will not be sufficient to stop disease spread. Instead, policymakers will need to overcome the additional hurdle of rapid widespread adoption. In a large-scale nationally representative survey, the current work identifies general risk preferences as a correlate of take-up of an anticipated COVID-19 vaccine. A complementary experiment leverages this insight to create effective messaging encouraging vaccine take-up. Individual differences in risk-preferences moderate responses to messaging that provides context on vaccine efficacy, while messaging that describes benefits of vaccination for herd immunity speeds vaccine take-up irrespective of risk-preferences. Findings suggest that marketers and policy-makers should consider general risk-preferences when targeting vaccine-related communications.","COVID-19, risk preferences, vaccine, individual differences, marketing communications, public policy","","","","","","","","","","","","Available at SSRN"
"Journal Article","Gupta S,Simon KI,Wing C","","Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review","NBER Working Paper","","2020","","","","COVID Tracking Project","","","","nber.org","","","","","2020","","","","","https://www.nber.org/system/files/working_papers/w28139/w28139.pdf","","","","","","Page 1. NBER WORKING PAPER SERIES MANDATED AND VOLUNTARY SOCIAL DISTANCING DURING THE COVID-19 EPIDEMIC: A REVIEW Sumedha Gupta Kosali I. Simon Coady Wing Working Paper 28139 http://www.nber.org/papers/w28139 …","","","","","","","","","","","","",""
"Journal Article","Xiao Wu MS,Nethery RC,Sabath MB,Braun D,et al.","","Supplementary Materials for Exposure to air pollution and COVID-19 mortality in the United States Updated April 5 2020","projects.iq.harvard.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://projects.iq.harvard.edu/files/covid-pm/files/pm_and_covid_mortality_supp.pdf","","","","","","… available in 2019 from Homeland Infrastructure Foundation-Level Data (HIFLD) and state- level information on number of COVID-19 tests has been performed up to April 04, 2020 from the COVID tracking project (https://covidtracking.com/). We obtain meteorolog …","","","","","","","","","","","","",""
"Journal Article","Lover AA,McAndrew T","","Sentinel Event Surveillance to Estimate Total SARS-CoV-2 Infections, United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.03.17.20037648v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/03/20/2020.03.17.20037648.full.pdf","","","","","","… CDC announces additional COVID-19 infections. Library Catalog: www.cdc.gov. [3] Robinson Meyer, Erin Kissane, and Alexis Madrigal. The COVID Tracking Project . https://covidtracking.com/. [4] J. Edward Moreno. CPAC attendee tests positive for coronavirus, March 2020 …","","","","","","","","","","","","",""
"Journal Article","Rivieccio BA,Micheletti A,Maffeo M,Zignani M,et al.","","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 …","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.14.20212415v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/16/2020.10.14.20212415.full.pdf","","","","","","Page 1. 1 CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic 1 dynamics looking to emergency calls and Twitter trends in Italian Lombardy region 2 3 Bruno Alessandro Rivieccio 1*¶ , Alessandra …","","","","","","","","","","","","",""
"Journal Article","Tolk A,Glazner C,Ungerleider J","","Computational Decision Support for the COVID-19 Healthcare Coalition","Computing in Science Engineering","","2020","","","1-1","COVID Tracking Project","","","","ieeexplore.ieee.org","","","","","2020","","","1558-366X","","http://dx.doi.org/10.1109/MCSE.2020.3036586;https://ieeexplore.ieee.org/abstract/document/9250508/?casa_token=Hn3hnCAt7mEAAAAA:2pDE16eY6CKW-xK6DWBNQ_kW5TAyVsK-lcvFtuM0DCEY0-kE4NBrplLCx2ICA6BmsakefBMTmw;https://ieeexplore.ieee.org/iel7/5992/5232784/09250508.pdf?casa_token=QTC18yFljCoAAAAA:_GPgpSQ2alSaS_361z0oxL7H6SCCOj6SMUn8yINDCQf83CVV3BoNj6Y_6bD6aHItiP5snyb9NQ","10.1109/MCSE.2020.3036586","","","","","The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations by providing data-driven, real-time insights that improve outcomes. This required the coalition to obtain, align, and orchestrate many heterogeneous data sources and present this data on dashboards in a format that was understandable and useful to decision makers. To do this, the coalition employed an ensemble approach to analysis, combining machine learning algorithms together with theory-based simulations, allowing prognosis to provide computational decision support rooted in science and engineering. Introduction. In the early months of 2020, the SARS-CoV-2 Coronavirus took the world by surprise, resulting in the COVID-19 pandemic causing not only significant loss of lives, but challenging the sustainability of our health care systems as well. In mid-March it became obvious that government and communities had to react immediately. Under the lead of the Mayo Clinic and The MITRE Corporation, the COVID-19 Healthcare Coalition (C19HCC) was established as a coordinated public-interest, private-sector response. The coalition brought healthcare organizations, technology firms, nonprofits, academia, and startups to support supply chains, inform coordinated social policies, and provide data-driven insights to protect people and preserve the healthcare delivery system. The coalition quickly reached more than 1,000 member organizations, many of them working in computational fields. Although the efforts focused on the United States, we had several international partners who not only observed, but also contributed to the efforts. This article summarizes selected research results and lessons learned when highly diverse and heterogenous organizations bring their data and computational infrastructure together to provide computational decision support in a new problem","COVID-19;Predictive models;Sociology;Statistics;Standards;Mathematical model","","","","","","","","","","","",""
"Journal Article","Maloney MJ,Rhodes NJ,Yarnold PR","","Mask mandates can limit COVID spread: Quantitative assessment of month-over-month effectiveness of governmental policies in reducing the number of new COVID …","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.06.20208033v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/10/08/2020.10.06.20208033.full.pdf","","","","","","Page 1. Title: Mask mandates can limit COVID spread: Quantitative assessment of month-over-month 1 effectiveness of governmental policies in reducing the number of new COVID-19 cases in 37 US States 2 and the District of Columbia 3 4 …","","","","","","","","","","","","",""
"Review","Naljayan M,Yazdi F,Struthers S,Sharshir M,Williamson A,Simon EE","","COVID-19 in New Orleans: A Nephrology Clinical and Education Perspective and Lessons Learned","Kidney Med","Kidney medicine","2020","","","","COVID Tracking Project","","","","Elsevier","","","","","2020-12-02","","","2590-0595","","http://dx.doi.org/10.1016/j.xkme.2020.09.012;https://www.ncbi.nlm.nih.gov/pubmed/33283183;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708233;https://linkinghub.elsevier.com/retrieve/pii/S2590-0595(20)30255-7;https://www.sciencedirect.com/science/article/pii/S2590059520302557","10.1016/j.xkme.2020.09.012","33283183","","","PMC7708233","New Orleans' first case of COVID-19 was reported on March 9, 2020 with a subsequent rapid rise in the number of cases throughout the state of Louisiana. Traditional educational efforts were no longer viable with social distancing and stay-at-home orders, therefore virtual didactics were integrated into our curriculum. Due to an exponential increase in the number of patients with acute kidney injury requiring kidney replacement therapy, the nephrology sections at Louisiana State University School of Medicine and Tulane University School of Medicine adapted their clinical workflows to accommodate these increased clinical volumes by utilizing prolonged intermittent kidney replacement therapies and acute peritoneal dialysis as well as other strategies to mitigate nursing burnout and decrease scarce resource utilization. Telehealth was implemented in outpatient clinics and dialysis units to protect vulnerable patients with kidney disease while maintaining access to care. Lessons learned from this pandemic and subsequent response may be utilized for future responses in similar situations.","","","","Section of Nephrology and Hypertension, LSU School of Medicine, New Orleans, LA. Section of Nephrology and Hypertension, Tulane School of Medicine and Southeast Louisiana Veterans Healthcare System, New Orleans, LA. DaVita Kidney Care, Denver, CO.","en","Review","","","","","","",""
"Preprint Manuscript","Hasanzadeh S,Alishahi M","","COVID-19 Pounds: Quarantine and Weight Gain","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-31","2020-12-08","","","","https://papers.ssrn.com/abstract=3684120;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3684120;http://dx.doi.org/10.2139/ssrn.3684120;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3684120;https://mpra.ub.uni-muenchen.de/103074/1/MPRA_paper_103074.pdf","10.2139/ssrn.3684120","","","","","In response to the COVID-19 pandemic, many countries, including the U.S., set a mandatory stay-at-home order in attempts to avert the spread. Although the primary goal of such a policy is to protect societies and save lives, it might result in other potential physical and psychological health threats. This paper examines the impact of stay-at-home policies on people’s health behaviours towards weight gain and probable obesity attributable to imposing the order. Using Google Trends data, we investigate whether the lockdowns that were implemented in the U.S. led to changes in weight-gain-related online search behaviours. To probe the causal link between lockdown policies and changes in weight-gain-related topics, we employ the differences-in-differences method and regression discontinuity design and we find a significant increase in the search intensity for workout and weight loss, while the search intensity for fitness, nutrition, and fast food appears to have declined. Our results from using event study regression suggest that changes in health behaviours began weeks before lockdown orders were implemented contemporaneously with emergency declarations and other partial closures about COVID-19. The findings suggest that people’s health-related behaviours regarding weight gain were affected by the lockdowns.","COVID-19, lockdown, health behaviours, weight gain, obesity","","","","","","","","","","","","Available at SSRN 3684120"
"Preprint Manuscript","Pan T,Shen W,Hu G","","Spatial homogeneity learning for spatially correlated functional data with application to COVID-19 Growth rate curves","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-20","","","","","http://arxiv.org/abs/2008.09227","","","2008.09227","","","We study the spatial heterogeneity effect on regional COVID-19 pandemic timing and severity by analyzing the COVID-19 growth rate curves in the United States. We propose a geographically detailed functional data grouping method equipped with a functional conditional autoregressive (CAR) prior to fully capture the spatial correlation in the pandemic curves. The spatial homogeneity pattern can then be detected by a geographically weighted Chinese restaurant process prior which allows both locally spatially contiguous groups and globally discontiguous groups. We design an efficient Markov chain Monte Carlo (MCMC) algorithm to simultaneously infer the posterior distributions of the number of groups and the grouping configuration of spatial functional data. The superior numerical performance of the proposed method over competing methods is demonstrated using simulated studies and an application to COVID-19 state-level and county-level data study in the United States.","","","","","","","","arXiv","2008.09227","stat.AP","","","arXiv [stat.AP]"
"Preprint Manuscript","Renne JP,Roussellet G,Schwenkler G","","Preventing COVID-19 Fatalities: State versus Federal Policies","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-28","","","","","http://arxiv.org/abs/2010.15263","","","2010.15263","","","Are COVID-19 fatalities large when a federal government does not impose containment policies and instead allow states to implement their own policies? We answer this question by developing a stochastic extension of a SIRD epidemiological model for a country composed of multiple states. Our model allows for interstate mobility. We consider three policies: mask mandates, stay-at-home orders, and interstate travel bans. We fit our model to daily U.S. state-level COVID-19 death counts and exploit our estimates to produce various policy counterfactuals. While the restrictions imposed by some states inhibited a significant number of virus deaths, we find that more than two-thirds of U.S. COVID-19 deaths could have been prevented by late September 2020 had the federal government imposed federal mandates as early as some of the earliest states did. Our results highlight the need for early actions by a federal government for the successful containment of a pandemic.","","","","","","","","arXiv","2010.15263","econ.GN","","","arXiv [econ.GN]"
"Preprint Manuscript","Narayanan KR,Heidarzadeh A,Laxminarayan R","","On Accelerated Testing for COVID-19 Using Group Testing","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-09","","","","","http://arxiv.org/abs/2004.04785","","","2004.04785","","","COVID-19 has resulted in a global health crisis that may become even more acute over the upcoming months. One of the main reasons behind the current rapid growth of COVID-19 in the U.S. population is the limited availability of testing kits and the relatively-high cost of screening tests. In this draft, we demonstrate the effectiveness of group testing (pooling) ideas to accelerate testing for COVID-19. This draft is semi-tutorial in nature and is written for a broad audience with interest in mathematical formulations relevant to COVID-19 testing. Therefore, ideas are presented through illustrative examples rather than through purely theoretical formulations. The focus is also on pools of size less than 64 such as what is practical with current RT-PCR technology.","","","","","","","","arXiv","2004.04785","cs.IT","","","arXiv [cs.IT]"
"Journal Article","Price CC,Propp AM","","A Framework for Assessing Models of the COVID-19 Pandemic to Inform Policymaking in Virginia","rand.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.rand.org/content/dam/rand/pubs/research_reports/RRA300/RRA323-1/RAND_RRA323-1.pdf","","","","","","… The first interventions were put in place with only a few dozen confirmed cases having been reported, and Virginia had just over 1,000 confirmed cases when the stay-at- home order was implemented ( COVID Tracking Project , 2020). These restrictions were put in Page 12. 2 …","","","","","","","","","","","","",""
"Journal Article","Rader B,White LF,Burns MR,Chen J,Brilliant J,Cohen J,Shaman J,Brilliant L,Hawkins JB,Scarpino SV,Astley CM,Brownstein JS","","Mask Wearing and Control of SARS-CoV-2 Transmission in the United States","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-08-28","","","","","http://dx.doi.org/10.1101/2020.08.23.20078964;https://www.ncbi.nlm.nih.gov/pubmed/32869039;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457618;https://doi.org/10.1101/2020.08.23.20078964;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457618.2/","10.1101/2020.08.23.20078964","32869039","","","PMC7457618","INTRODUCTION: Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, research is limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. METHODS: Serial cross-sectional surveys were administered June 3 through July 27, 2020 via web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N=378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. RESULTS: We find an upward trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Segmented regression analysis of mask wearing found no statistical change following mandates, however the positive trend of increased mask wearing over time was preserved. CONCLUSION: Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.","","","","","en","Research Article","","","","","","",""
"Journal Article","Galea S","","esearch","maxwell.syr.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.maxwell.syr.edu/uploadedFiles/cpr/publications/cpr_policy_briefs/pb56.pdf","","","","","","… Figure 26 (Trading Economics, 2020) | US Department of Labor (The COVID Tracking Project , 2020) Page 29. 26 Figure 27 … That is the same social patterning with pre- COVID-19 manifesting in a time of COVID-19. That is the country (The COVID Tracking Project , 2020) Page 30 …","","","","","","","","","","","","",""
"Journal Article","Shah NR,Lai D,Wang CJ","","An Impact-Oriented Approach to Epidemiological Modeling","J. Gen. Intern. Med.","Journal of general internal medicine","2020","","","","COVID Tracking Project","","","","Springer","","","","","2020-09-21","","","0884-8734","1525-1497","http://dx.doi.org/10.1007/s11606-020-06230-1;https://www.ncbi.nlm.nih.gov/pubmed/32959348;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505215;https://dx.doi.org/10.1007/s11606-020-06230-1;https://link.springer.com/article/10.1007/s11606-020-06230-1","10.1007/s11606-020-06230-1","32959348","","","PMC7505215","… For example, the COVID Tracking Project is an open-source initiative of The Atlantic and provides one of the most complete data sets available about COVID-19 in the USA (available at https://covidtracking.com/). 7. Adaptability: Can the model be modified and adapted …","","","","Clinical Excellence Research Center, Stanford University, Stanford, CA, USA. nirav.shah@stanford.edu. Covid Act Now, Walnut, CA, USA. Center for Policy, Outcomes and Prevention, Stanford University, Stanford, CA, USA.","en","Research Article","","","","","","",""
"Journal Article","Fowler L,Kettler J,Witt S","","Democratic governors are quicker in responding to the coronavirus than Republicans","The Conversation","","2020","","","","COVID Tracking Project","","","","scholarworks.boisestate.edu","","","","","2020","","","","","https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=1117&context=pubadmin_facpubs","","","","","","… Some argue that states led by Republicans were hit by COVID-19 later or not as hard as states led by Democrats. Yet based on data from the COVID Tracking Project , there was little difference in the number of cases in each state when governors announced these orders …","","","","","","","","","","","","",""
"Preprint Manuscript","Malaty Rivera J,Gupta S,Ramjee D,El Hayek G,El Amiri N,Desai A,Majumder MS","","Evaluating Interest in Off-Label Use of Disinfectants for COVID-19 with Google Trends","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-29","2020-12-08","","","","https://papers.ssrn.com/abstract=3638653;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3638653;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3638653","","","","","","In the initial months of the Coronavirus Disease 2019 (COVID-19) pandemic, the U.S. Centers for Disease Control and Prevention (CDC) made recommendations to clean and disinfect frequently-handled objects, triggering panic-buying of disinfectant products like Clorox® and Lysol® nationwide. During a White House press briefing on April 23, 2020, President Trump publicly implied that the White House Coronavirus Task Force should investigate injections of disinfectant as potential treatment for COVID-19. Across the United States, we evaluated Google Trends data to determine search interest from January 1, 2020 to May 10, 2020 regarding (1) the purchase and consumption of disinfectants and (2) poison control centers, which were included to examine correlated behavioral outcomes. Our work demonstrates a contemporaneous correlation between the President’s remarks and online search trends, with an over 3,000% overnight uptick in search interest for off-label use of disinfectants as well as increases in poison control cases. Public officials have the responsibility during public health emergencies to communicate issues that are evidence-based, and misleading information may adversely affect public knowledge and behavior.","coronavirus, COVID-19, misinformation, public health, science communication","","","","","","","","","","","","Available at SSRN"
"Journal Article","Asteris PG,Douvika M,Karamani C,Skentou A,et al.","","A novel heuristic global algorithm to predict the COVID-19 pandemic trend","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/medrxiv/early/2020/04/22/2020.04.16.20068445.full.pdf","","","","","","… databases. Data for the sum of the countries were obtained from the database Worldometer [2], for USA were derived from the COVID Tracking project [3], while for the Italian cities from GitHub, Inc. [4]. All rights reserved. No reuse allowed without permission …","","","","","","","","","","","","",""
"Preprint Manuscript","Vinod HD,Theiss K","","Bias-corrected State-by-state Forecasts of COVID-19 Deaths","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-08","2020-12-08","","","","https://papers.ssrn.com/abstract=3646527;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3646527;http://dx.doi.org/10.2139/ssrn.3646527;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3646527;https://legacy.fordham.edu/economics/vinod/WeeklyCovid.pdf","10.2139/ssrn.3646527","","","","","Since testing for COVID-19 infections is not at all randomized over the general population, most epidemiological model forecasts of deaths are subject to `selection bias.' This paper updates and supplements Vinod and Theiss (2020), where the bias correction using generalized linear models (GLM) and inverse mills ratio (IMR) are described in detail. We include state-by-state forecasts using Poisson regression to predict one-week-ahead cumulative deaths from logarithms of current cumulative infections. We hope that the details provided here will help local governors and mayors in their opening up decisions.","virus infections, selection bias, generalized linear models, inverse Mills ratio, time series forecasting, bottstrap","","","","","","","","","","","","Available at SSRN 3646527"
"Journal Article","Chen C,Shi Q,Dong XP","","Analyses of the Duration times of COVID-19 Epidemic at Various Time-Points in 11 Severely Affected Countries","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-45425/latest.pdf","","","","","","… 2. COVID-19 coronavirus pandemic United States: Worldometers; 2020 [Available from: https://www.worldometers.info/coronavirus/country/us/. 3. The COVID Tracking Project United States: Data API; 2020 [Available from: https://covidtracking.com/api …","","","","","","","","","","","","",""
"Preprint Manuscript","Kamikubo Y,Takahashi A","","Epidemiological tools that predict partial herd immunity to SARS Coronavirus 2","","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.03.25.20043679v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/03/27/2020.03.25.20043679.full.pdf","","","","","","… Page 4. METHODS SOURCES OF DATA Epidemiological data were obtained from the Worldometer (https://www.worldometers.info/coronavirus/), the COVID Tracking Project (https://covidtracking.com/data/), and websites operated by Su Wei (https://covid …","","","","","","","","","","","","","medRxiv"
"Journal Article","Gv NK,Prajapati S","","Retrospective Analysis of total COVID-19 Cases and Comparison of Case Fatality and Recovery Rates Among States of India After First and Second Phases of …","Authorea Preprints","","2020","","","","COVID Tracking Project","","","","authorea.com","","","","","2020","","","","","https://www.authorea.com/doi/full/10.22541/au.159242126.68462730;https://www.authorea.com/doi/pdf/10.22541/au.159242126.68462730","","","","","","… 15. Data as on March 26, 2020 Sources: Korea Centers for Disease Control and Prevention; Ministry of Health, Italy; Department of Health and Social Care and Public Health England; The Covid Tracking Project Taiwan Centers for Disease Control; Indian Council of Medical …","","","","","","","","","","","","",""
"Journal Article","Guest JL,Sullivan PS,Valentine-Graves M,Valencia R,Adam E,Luisi N,Nakano M,Guarner J,Del Rio C,Sailey C,Goedecke Z,Siegler AJ,Sanchez TH","","Suitability and Sufficiency of Telehealth Clinician-Observed, Participant-Collected Samples for SARS-CoV-2 Testing: The iCollect Cohort Pilot Study","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","2","e19731","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-06-25","","","2369-2960","","http://dx.doi.org/10.2196/19731;https://www.ncbi.nlm.nih.gov/pubmed/32479412;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318863;https://publichealth.jmir.org/2020/2/e19731/","10.2196/19731","32479412","","","PMC7318863","BACKGROUND: The severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic calls for expanded opportunities for testing, including novel testing strategies such as home-collected specimens. OBJECTIVE: We aimed to understand whether oropharyngeal swab (OPS), saliva, and dried blood spot (DBS) specimens collected by participants at home and mailed to a laboratory were sufficient for use in diagnostic and serology tests of SARS-CoV-2. METHODS: Eligible participants consented online and were mailed a participant-collection kit to support collection of three specimens for SARS-CoV-2 testing: saliva, OPS, and DBS. Participants performed the specimen collection procedures during a telehealth video appointment while clinical observers watched and documented the suitability of the collection. The biological sufficiency of the specimens for detection of SARS-CoV-2 by reverse transcriptase-polymerase chain reaction and serology testing was assessed by laboratorians using visual inspection and quantification of the nucleic acid contents of the samples by ribonuclease P (RNase P) measurements. RESULTS: Of the enrolled participants,153/159 (96.2%) returned their kits, which were included in this analysis. All these participants attended their video appointments. Clinical observers assessed that of the samples collected, 147/153 (96.1%) of the saliva samples, 146/151 (96.7%) of the oropharyngeal samples, and 135/145 (93.1%) of the DBS samples were of sufficient quality for submission for laboratory testing; 100% of the OPS samples and 98% of the saliva samples had cycle threshold values for RNase P <30, indicating that the samples contained sufficient nucleic acid for RNA-PCR testing for SARS-CoV-2. CONCLUSIONS: These pilot data indicate that most participant-collected OPS, saliva, and DBS specimens are suitable and sufficient for testing for SARS-CoV-2 RNA and serology. Clinical observers rated the collection of specimens as suitable for testing, and visual and quantitative laboratory assessment indicated that the specimens were biologically sufficient. These data support the utility of participant-collected and mailed-in specimens for SARS-CoV-2 testing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19054.","COVID-19; diagnosis; diagnostic; home testing; pilot study; telehealth; testing","","","Rollins School of Public Health, Emory University, Atlanta, GA, United States. Molecular Testing Labs, Vancouver, WA, United States. School of Medicine, Emory University, Atlanta, GA, United States.","en","Research Article","","","","","","",""
"Preprint Manuscript","Masterman C","","Stay-at-Home Orders and COVID-19 Fatalities","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-14","2020-12-08","","","","https://papers.ssrn.com/abstract=3600905;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3600905;http://dx.doi.org/10.2139/ssrn.3600905;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3600905","10.2139/ssrn.3600905","","","","","The COVID-19 pandemic has prompted most state governments to order residents to stay at home. The goal of such orders is to mitigate infection rates to prevent health care system overload, thereby dramatically reducing the death toll of the pandemic. This article investigates the effectiveness of stay-at-home orders in decreasing COVID-19 infections and fatalities. Using a differences-in-differences approach, I estimate that stay-at-home orders between mid-March and May 9 prevented 1.7 million COVID-19 cases and 55,000 deaths in the United States. Orders that state governments issued were more effective than local government orders, suggesting that consistent policy approaches across geographic areas is key. The effects were concentrated in urban and higher wage counties. Based on the day of the week that infections are prevented, I also find some evidence that the cases stay-at-home orders prevent are largely those that would have occurred at work rather than from recreation.","COVID-19, pandemic, coronavirus, stay at home, shelter in place, health policy, law and economics","","","","","","","","","","","","Available at SSRN 3600905"
"Journal Article","Mesch D,Osili U,Skidmore T,Bergdoll J,Ackerman J,Sager J","","COVID-19, Generosity, and Gender: How Giving Changed During the Early Months of a Global Pandemic","","","2020","","","","COVID Tracking Project","","","","scholarworks.iupui.edu","","","","","2020-09-01","2020-12-08","","","","https://scholarworks.iupui.edu/handle/1805/23750;https://scholarworks.iupui.edu/bitstream/handle/1805/23750/covid-report1.pdf","","","","","","The spring of 2020 was marked by disruptions to society on a level many Americans had never experienced. The novel coronavirus (COVID-19) had a devastating human toll, infecting more than 1.7 million individuals and resulting in more than 100,000 deaths in the U.S. through May 2020. Beyond examining whether and how much households contributed, the report explores the types of philanthropy in which they participated and how their charitable giving changed. The study also pinpoints the effect of specific elements of the crisis on their giving. Finally, to provide a more nuanced picture of philanthropic responses to the pandemic, the report highlights differences across household types, with a particular focus on gender and marital status.","generosity; gender; giving; pandemic; COVID-19; Working Paper","","","","","","","","","","","",""
"Journal Article","Talk P,Board PTL","","Cultural and Minority Affairs","aptaoregon.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.aptaoregon.org/cultural-and-minority-affairs","","","","","","… Hawaiian/Pacific Islanders/Native Americans make up 2.3% of the general population and 4% of confirmed cases, while African Americans comprise approximately 2% of the population and 3% of confirmed COVID-19 cases (Census, 2019; Covid Tracking Project , 2020, OHA …","","","","","","","","","","","","",""
"Journal Article","Ohnishi A,Namekawa Y,Fukui T","","Universality in COVID-19 spread in view of the Gompertz function","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.18.20135210v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/05/2020.06.18.20135210.full.pdf","","","","","","Page 1. Prog. Theor. Exp. Phys. 2015, 00000 (21 pages) DOI: 10.1093/ptep/ 0000000000 Universality in COVID-19 spread in view of the Gompertz function † Akira Ohnishi1, Yusuke Namekawa1, and Tokuro Fukui1 1Yukawa …","","","","","","","","","","","","",""
"Journal Article","Shelton T","","A post-truth pandemic?","Big Data & Society","Big Data & Society","2020","7","2","2053951720965612","COVID Tracking Project","","","","SAGE Publications Ltd","","","","","2020-07-01","","","2053-9517","","https://doi.org/10.1177/2053951720965612;http://dx.doi.org/10.1177/2053951720965612;https://journals.sagepub.com/doi/abs/10.1177/2053951720965612;https://journals.sagepub.com/doi/pdf/10.1177/2053951720965612","10.1177/2053951720965612","","","","","As the coronavirus pandemic continues apace in the United States, the dizzying amount of data being generated, analyzed and consumed about the virus has led to calls to proclaim this the first ?data-driven pandemic?. But at the same time, it seems that this plethora of data has not meant a better grasp on the reality of the pandemic and its effects. Even as we have the potential to digitally track and trace nearly every single individual who has contracted the virus, we have no idea exactly how many people have had the virus, been hospitalized, or died because of it, largely due to a confluence of factors, particularly active obfuscation and mismanagement by public authorities and misinformation spread through social media and right-wing media channels. But beyond these dynamics, there also lies the less nefarious ways that the everyday, subjective practices of data collection, analysis and visualization have the potential to themselves (re)produce these very same dynamics where data is at once valorized and ignored, preeminent and completely useless. That is, the pandemic has revealed only the general inadequacy of our data infrastructures and assemblages to solving pressing social issues, but also the more general shift towards a ?post-truth? disposition in contemporary social life. But, as this paper argues, it would be a mistake to see the centrality of data as being somehow the opposite from the larger post-truth apparatus, as the two are instead fundamentally intertwined and co-produced.","","","","","","","","","","","","",""
"Journal Article","Green D,Loualiche E","","Local Government Finances and Balanced Budgets in the COVID-19 Crisis","Available at SSRN 3651605","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3651605","","","","","","… of States. This figure represents the timeline of the impact of the Coronavirus across US States. Panel A and B represent the number of new deaths and new cases across the US from the Covid tracking project . Panel C. represents …","","","","","","","","","","","","",""
"Journal Article","Cohen DE,Marlowe G,Contreras G,Sosa MA,et al.","","Assessment of a Laboratory-Based SARS-CoV-2 Antibody Test Among Hemodialysis Patients: A Quality Improvement Initiative","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.03.20163642v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/04/2020.08.03.20163642.full.pdf","","","","","","… initial findings. Miami-Dade County News Release. April 24, 2020 10. The COVID Tracking Project 2020. https://covidtracking.com/ Accessed June 17, 2020 All rights reserved. No reuse allowed without permission. (which was …","","","","","","","","","","","","",""
"Journal Article","Hoffman BU","","Significant relaxation of SARS-CoV-2-targeted non-pharmaceutical interventions may result in profound mortality: A New York state modelling study","PLoS One","PloS one","2020","15","9","e0239647","COVID Tracking Project","","","","journals.plos.org","","","","","2020-09-24","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0239647;https://www.ncbi.nlm.nih.gov/pubmed/32970745;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514073;https://dx.plos.org/10.1371/journal.pone.0239647;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239647","10.1371/journal.pone.0239647","32970745","","","PMC7514073","Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the most significant global health crisis of the 21st century. The aim of this study was to develop a model to simulate the effect of undocumented infections, seasonal infectivity, immunity, and non-pharmaceutical interventions (NPIs) on the transmission, morbidity, and mortality of SARS-CoV-2 in New York State (NYS) based on data collected between March 4 and April 28, 2020. Simulations predict that undocumented infections significantly contribute to infectivity, NPIs are effective in reducing morbidity and mortality, and relaxation >50% of NPIs from initial lock-down levels may result in tens-of-thousands more deaths. Endemic infection is likely to occur in the absence of sustained immunity. As a result, until an effective vaccine or other effective pharmaceutical intervention is developed, the risks of significantly reducing NPIs should be carefully considered. This study employs modelling to simulate fundamental characteristics of SARS-CoV-2 transmission, which can help policymakers navigate combating this virus in the coming years.","","","","Vagelos College of Physicians and Surgeons, Columbia University, New York City, New York, United States of America. Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Ganz SC","","Hypothesis Testing Sustained Declines in COVID-19 Intensity","AEI Paper & Studies","","2020","","","","COVID Tracking Project","","","","questia.com","","","","","2020","","","","","https://www.questia.com/library/journal/1G1-630406485/hypothesis-testing-sustained-declines-in-covid-19","","","","","","… M.sup.0]. 4 Test Applied to US Data. I next apply the test to positive COVID-19 test rates from 23 states, selected based on the quality of the public data made available through the COVID Tracking Project . I include only those …","","","","","","","","","","","","",""
"Preprint Manuscript","Sacks DW,Menachemi N,Embi P,Wing C","","What can we learn about SARS-CoV-2 prevalence from testing and hospital data?","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-01","","","","","http://arxiv.org/abs/2008.00298","","","2008.00298","","","Measuring the prevalence of active SARS-CoV-2 infections is difficult because tests are conducted on a small and non-random segment of the population. But people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population. By mid-June, we estimate that prevalence was between 0.01 and 4.1 percent in the general population and between 0.6 to 2.6 percent in the non-COVID hospital population. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states.","","","","","","","","arXiv","2008.00298","econ.EM","","","arXiv [econ.EM]"
"Journal Article","Haeder SF,Gollust SE","","From Poor to Worse: Health Policy and Politics Scholars’ Assessment of the U.S. COVID‐19 Response and Its Implications","World Medical & Health Policy","World Medical & Health Policy","2020","3","","e2023020‐e","COVID Tracking Project","","","","Wiley Online Library","","","","","2020-11-03","","","1948-4682","1948-4682","https://onlinelibrary.wiley.com/doi/10.1002/wmh3.371;https://onlinelibrary.wiley.com/doi/pdf/10.1002/wmh3.371;https://onlinelibrary.wiley.com/doi/full-xml/10.1002/wmh3.371;http://dx.doi.org/10.1002/wmh3.371;https://onlinelibrary.wiley.com/doi/abs/10.1002/wmh3.371","10.1002/wmh3.371","","","","","By any standard, the U.S. response to the coronavirus pandemic has been abysmal, with countless unnecessary deaths and suffering. Although the human impact is most important, the pandemic has also had enormous consequences on the U.S. political system. Health policy and politics scholars, particularly from political science orientations, are ideally equipped to evaluate the pandemic response from a political perspective. In this study, we report on the results of a two-wave survey of academic health policy researchers in April/May (N?=?239) and September (N?=?158) 2020. Respondents noted an outsized influence of public health, medicine, and economics, while noting limited public engagement of social scientists like sociologists and political scientists. The perceived expert influence declined over the two waves, while assessment of electoral consequences to favor Democrats grew. Respondents also offered a sober perspective on federal and state responses to the pandemic, forecasting lasting implications for health policy and political dynamics for years to come. Given their expertise, health policy and politics scholars appear uniquely qualified to enter the public and policy discourse going forward.","","","","","","","","","","","","",""
"Journal Article","Renken C,Manalo J,Zhu C,McColl C,Harb M,et al.","","COVID-19 Vaccines Diligence Report","enventure.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.enventure.org/blog/covid-19-vaccines-diligence-report","","","","","","… The New York Times. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html. Accessed September 2020. The COVID Tracking Project . https://covidtracking.com/. Accessed September 2020. HHS.gov. https://www.hhs.gov/. Accessed September 2020 …","","","","","","","","","","","","",""
"Journal Article","Ray R,Fabio R","","COVID-19 and the Future of Society","Contexts. American Sociological Association","","2020","","","","COVID Tracking Project","","","","contexts.org","","","","","2020","","","","","https://contexts.org/blog/covid-19-and-the-future-of-society/","","","","","","… Using data from the COVID Tracking Project and 2018 American Community Survey 1-year estimates for US states, we find that the percent population without health insurance coverage is negatively related to the state-level COVID-19 testing rate (R = -.44, p < .01), which is …","","","","","","","","","","","","",""
"Journal Article","Wu SL,Mertens A,Crider YS,Nguyen A,et al.","","Substantial underestimation of SARS-CoV-2 infection in the United States due to incomplete testing and imperfect test accuracy","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.12.20091744v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/18/2020.05.12.20091744.full.pdf","","","","","","… from February 28 to April 18, 2020. Data was collected by the COVID Tracking Project ,23 which assembles data on a regular basis primarily from state, district, and territory public health departments … Page 6. 6 on April 18 ( COVID Tracking Project : 721,245; CDC: 720,630) …","","","","","","","","","","","","",""
"Journal Article","Cradic K,Lockhart M,Ozbolt P,Fatica L,Landon L,Lieber M,Yang D,Swickard J,Wongchaowart N,Fuhrman S,Antonara S","","Clinical Evaluation and Utilization of Multiple Molecular In Vitro Diagnostic Assays for the Detection of SARS-CoV-2","Am. J. Clin. Pathol.","American journal of clinical pathology","2020","154","2","201-207","COVID Tracking Project","","","","academic.oup.com","","","","","2020-07-07","","","0002-9173","1943-7722","http://dx.doi.org/10.1093/ajcp/aqaa097;https://www.ncbi.nlm.nih.gov/pubmed/32462195;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314271;https://academic.oup.com/ajcp/article-lookup/doi/10.1093/ajcp/aqaa097;https://academic.oup.com/ajcp/advance-article-abstract/doi/10.1093/ajcp/aqaa097/5848026;https://academic.oup.com/ajcp/advance-article/doi/10.1093/ajcp/aqaa097/5848026?casa_token=O5IHV1wrC_kAAAAA:YzwoSC5nK-DlON2-njKZWzZHQO5IQvQxaAdaIPtH7X7pxrz6Gl2SfufJkkqaQHwaiCvqHYTp0V-L","10.1093/ajcp/aqaa097","32462195","","","PMC7314271","OBJECTIVES: To evaluate the clinical performance of 3 molecular assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We used 184 nasopharyngeal swab specimens to compare Abbott ID NOW COVID-19 (Abbott ID NOW), DiaSorin Molecular Simplexa COVID-19 Direct (DiaSorin Simplexa), and Roche cobas 6800 SARS-CoV-2 (Roche cobas) assays. In a separate analysis, 3 specimens (nasopharyngeal, oropharyngeal, and nasal) were collected from 182 unique patients presenting to the emergency department with suspicion of coronavirus disease 2019 and were tested utilizing Abbott ID NOW. To further characterize each assay, relative limits of detection were evaluated utilizing positive nasopharyngeal patient samples. RESULTS: The positive percent agreement was 91% (95% confidence interval [CI], 0.76-0.97) for Abbott ID NOW and 100% (95% CI, 0.90-1.00) for DiaSorin Simplexa and Roche cobas. The negative percent agreement was 100% (95% CI, 0.98-1.00) for all 3 assays. All swab types tested with the Abbott assay produced concordant results. Polymerase chain reaction assays had approximately 10 to 100 times lower limits of detection than Abbott ID NOW. CONCLUSIONS: Based on these evaluations, a multiplatform testing approach is proposed, depending on patient population and assay sensitivity, to address testing needs during a public health emergency.","Abbott ID NOW; COVID-19; Coronavirus; Molecular diagnostics; SARS-CoV-2","","","OhioHealth Laboratory Services, Columbus. CORPath Pathology Services, Columbus, OH. OhioHealth Research Institute, Columbus.","en","Research Article","","","","","","",""
"Journal Article","Adler P,Florida R,Hartt M","","Mega Regions and Pandemics","Tijdschr. Econ. Soc. Geogr.","Tijdschrift voor economische en sociale geografie = Journal of economic and social geography = Revue de geographie economique et humaine = Zeitschrift fur okonomische und soziale Geographie = Revista de geografia economica y social","2020","111","3","465-481","COVID Tracking Project","","","","Wiley Online Library","","","","","2020-07","","","0040-747X","","http://dx.doi.org/10.1111/tesg.12449;https://www.ncbi.nlm.nih.gov/pubmed/32834149;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7361226;https://onlinelibrary.wiley.com/doi/abs/10.1111/tesg.12449?casa_token=Vv4Tp-1EBfIAAAAA:Cp-P3JLbr8SG6zZ5nRBrzUSzBQtANnjkS795kQz_-J4Zi0Kj46vdN-3zx72O6h_3HtK4BsWrDhl3j0w;https://onlinelibrary.wiley.com/doi/pdf/10.1111/tesg.12449?casa_token=1Ad4JcfBmiUAAAAA:1ecQBDZcxo_dZ8si_4iRBoX76ojzg6Tf4AR2HIaViGO1u-rFvJh3ozZGrazi_88fo2to2XMLCXMPSnw","10.1111/tesg.12449","32834149","","","PMC7361226","The ongoing COVID-19 crisis has put the relationship between spatial structure and disease exposure into relief. Here, we propose that mega regions - clusters of metropolitan regions like the Acela Corridor in the United States are more exposed to diseases earlier in pandemics. We review standard accounts for the benefits and costs of locating in such regions before arguing that pandemic risk is higher there on average. We test this mega region exposure theory with a study of the US urban system. Our results indicate that American mega regions have born the early brunt of the disease, and that three mega regions are hotspots. From this standpoint, the extent more than the intensity of New York's urbanization may be implicated in its COVID-19 experience. We conclude that early pandemic risk is a hitherto unrecognised diseconomy operating in mega regions.","COVID‐19; agglomeration; clustering; coronavirus; regional economic growth; satellite data","","","School of Cities Rotman School of Management University of Toronto 105 St. George Toronto Ontario M5S 3E6 Canada. Cardiff University Glamorgan Building, King Edward VII Avenue Cardiff Wales CF103WA United Kingdom.","en","Research Article","","","","","","",""
"Journal Article","Kakpo A,Ahmed Salim N","","Effects of Social Distancing on COVID-19 Infections and Mortality in the US","Effects of Social Distancing on COVID","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3613825","","","","","","… factors on mortality in the US. First, we use daily data of number of positive COVID-19 tests as well as number of deaths by state, from the COVID tracking project website 3, for the period 03/01/20 - 04/14/20. Second, we use the safegraph Social Distancing Metrics (SDM) data …","","","","","","","","","","","","",""
"Journal Article","Garman J,MacAvaney S,Yang E,Frieder O","","SIDIR: Extending SIR with Detected and Isolated Populations for Pandemic Modeling","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.20.20157834v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/26/2020.07.20.20157834.full.pdf","","","","","","… for Disease Prevention and Control (ECDC). Data are extended and corroborated with other sources, such as DXY,3 BNO News,4 and the COVID Tracking Project .5 4.1.2 Disease Timeline COVID-19 progresses through a typical …","","","","","","","","","","","","",""
"Journal Article","Li AY,Hannah TC,Durbin J,Dreher N,McAuley FM,et al.","","Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in US Counties: The Significant Effects of Black Race and Temperature","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.17.20069708v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/24/2020.04.17.20069708.full.pdf","","","","","","Page 1. Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in US Counties: The Significant Effects of Black Race and Temperature Adam Y. Li, BS,​1​* Theodore C Hannah, BA,​1​* John R Durbin, BS …","","","","","","","","","","","","",""
"Preprint Manuscript","Jones LD,Magdon-Ismail M,Mersini-Houghton L,Meshnick S","","A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-24","","","","","http://arxiv.org/abs/2008.10530","","","2008.10530","","","We present a new mathematical model to explicitly capture the effects that the three restriction measures: the lockdown date and duration, social distancing and masks, and, schools and border closing, have in controlling the spread of COVID-19 infections $i(r, t)$. Before restrictions were introduced, the random spread of infections as described by the SEIR model grew exponentially. The addition of control measures introduces a mixing of order and disorder in the system's evolution which fall under a different mathematical class of models that can eventually lead to critical phenomena. A generic analytical solution is hard to obtain. We use machine learning to solve the new equations for $i(r,t)$, the infections $i$ in any region $r$ at time $t$ and derive predictions for the spread of infections over time as a function of the strength of the specific measure taken and their duration. The machine is trained in all of the COVID-19 published data for each region, county, state, and country in the world. It utilizes optimization to learn the best-fit values of the model's parameters from past data in each region in the world, and it updates the predicted infections curves for any future restrictions that may be added or relaxed anywhere. We hope this interdisciplinary effort, a new mathematical model that predicts the impact of each measure in slowing down infection spread combined with the solving power of machine learning, is a useful tool in the fight against the current pandemic and potentially future ones.","","","","","","","","arXiv","2008.10530","q-bio.PE","","","arXiv [q-bio.PE]"
"Preprint Manuscript","Paletta FC,Ohmaye H","","Por Causa do Novo Coronavírus: Contribuições da Ciência da Informação","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09","","","","","http://dx.doi.org/10.22478/ufpb.1981-0695.2020v15n3.55279;https://osf.io/preprints/b6pkh/;https://osf.io/b6pkh/download?format=pdf","10.22478/ufpb.1981-0695.2020v15n3.55279","","","","","Owing to the new coronavirus and also to the COVID-19, new words and concepts are being incorporated into our daily lives, and into linguistics as well. In an environment flooded with terms such as new normal, flatten the curve, asymptomatic, comorbidity, community spread, herd immunity, lockdown, quarantine, social distancing, live (as in live video or live streaming), and work from home, a new glossary has been made ordinary and assimilated by society. Times of crisis are usually harbingers of jumps in intellectual and technological innovation, as illustrated by the two world wars and also by the coronavirus pandemics. A bibliographical research of publishers' databases in the area of infographics, especially in the context of the production of descriptive documents reveals an almost complete inexistence of materials. In the aftermath of the closing down of cultural institutions such as museums and libraries, and of the emergence of news reports focussed on the topics of the pandemics and social distancing, our research was redirected to the output of infographics by organs and entities involved in the divulging of information. We consider that this exploratory research, carried out in a virtual and remote format, presents results that justify the such technical and theoretical treatment related to the effect caused by the coronavirus and/or COVID-19.","Coronavirus; COVID-19; INFOGRAPHIC; Librarianship","","","","","","","","","","","",""
"Journal Article","Patel D,Kher V,Desai B,Lei X,Cen S,Nanda N,et al.","","Machine Learning Based Predictors for COVID-19 Disease Severity","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-108301/latest.pdf","","","","","","Page 1. 1 Pattern-mixture model in network meta-analysis of binary missing outcome 1 data: one-stage or two-stage approach? 2 3 Loukia M. Spineli1* Spineli.Loukia@mh-hannover. de 4 Katerina Papadimitropoulou2,3 a.papadimitropoulou@lumc.nl 5 …","","","","","","","","","","","","",""
"Review","Sultan S,Siddique SM,Altayar O,Caliendo AM,Davitkov P,Feuerstein JD,Francis D,Inadomi JM,Lim JK,Falck-Ytter Y,Mustafa RA,American Gastroenterological Association. Electronic address: ewilson@gastro.org","","AGA Institute Rapid Review and Recommendations on the Role of Pre-Procedure SARS-CoV-2 Testing and Endoscopy","Gastroenterology","Gastroenterology","2020","159","5","1935-1948.e5","COVID Tracking Project","","","","Elsevier","","","","","2020-11","","","0016-5085","1528-0012","http://dx.doi.org/10.1053/j.gastro.2020.07.043;https://www.ncbi.nlm.nih.gov/pubmed/32735862;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386603;https://linkinghub.elsevier.com/retrieve/pii/S0016-5085(20)35006-X;https://www.sciencedirect.com/science/article/pii/S001650852035006X/pdf?md5=ad5a0f71a4d0edd69a26fd9bab511acd&pid=1-s2.0-S001650852035006X-main.pdf;https://www.gastrojournal.org/article/S0016-5085(20)35006-X/fulltext","10.1053/j.gastro.2020.07.043","32735862","","","PMC7386603","Page 1. Journal Pre-proof AGA Institute Rapid Review and Recommendations on the Role of Pre-Procedure SARS-CoV2 Testing and Endoscopy Shahnaz Sultan, Shazia M. Siddique, Osama Altayar, Angela M. Caliendo, Perica …","COVID-19; Diagnostic test; SARS-CoV-2","","","Division of Gastroenterology, Hepatology, and Nutrition, University of Minnesota, Minneapolis Veterans Affairs Healthcare System, Minneapolis, Minnesota. Division of Gastroenterology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. Division of Gastroenterology, Washington University School of Medicine, St Louis, Missouri. Department of Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island. Division of Gastroenterology, Northeast Ohio Veterans Affairs Healthcare System, Case Western Reserve University School of Medicine, Cleveland, Ohio. Division of Gastroenterology and Center for Inflammatory Bowel Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts. Mayo Clinic College of Medicine, Division of Gastroenterology and Hepatology, Jacksonville, Florida. Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah. Yale Liver Center and Section of Digestive Diseases, Yale University School of Medicine, New Haven, Connecticut. Division of Nephrology and Hypertension, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas.","en","Review","","","","","","",""
"Report","Freed M","","Advisory Memorandum To: US Commission on Civil Rights From: Connecticut State Advisory Committee Date: September 10, 2020 Subject: Advisory …","","","2020","","","","COVID Tracking Project","","","","usccr.gov","","","","","2020","","","","","https://www.usccr.gov/files/2020-09-29-Connecticut-Nursing-Homes-and-Covid-19-Advisory-Memorandum.pdf","","","","","","… have COVID-19 cases as homes with the highest percentage of white residents.13 See Figure 1. The finding of racial disparities among COVID-19 infections and deaths is consistent with national infection and mortality data published by The COVID Tracking Project which has …","","","","","","Government Report","","","","","","",""
"Journal Article","Mowles E,Nguyen C,Nguyen L,Hollows JE,et al.","","Lessons from the USMA Faculty Development Workshop in teaching STEM courses during COVID-19","","","2020","","","","COVID Tracking Project","","","","… .s3.amazonaws.com","","","","","2020","","","","","http://itempdf74155353254prod.s3.amazonaws.com/12869651/Lessons_from_the_USMA_Faculty_Development_Workshop_in_Teaching_STEM_Courses_During_COVID-19_v1.pdf","","","","","","… 45. COVID-19 Map. Johns Hopkins Coronavirus Resource Center https://coronavirus.jhu. edu/map.html (accessed 2020-08-20). 46. The COVID Tracking Project . https://covidtracking. com/ (accessed 2020-08-20). Page 20. 19 Figures and legends …","","","","","","","","","","","","",""
"Preprint Manuscript","McGovern S","","Spectral Processing of COVID-19 Time-Series Data","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-13","","","","","http://arxiv.org/abs/2008.08039","","","2008.08039","","","The presence of oscillations in aggregated COVID-19 data not only raises questions about the data's accuracy, it hinders understanding of the pandemic. Spectral analysis is used to reveal additional properties of the data, and the oscillations are replicated using sinusoidal resynthesis. The precise behavior of the seven-day moving average is also discussed, specifically, the cause of its jaggedness and the phase error it introduces. In comparison, other filtering techniques and Fourier processing produce superior smoothing and have zero phase error. Both of these are presented, and they are extended to isolate several frequency ranges. This extracts some of the same short-term variability that is resynthesized, and it shows that fluctuations with periods between 8 and 21 days are present in U.S. mortality data. These methods have applications that include modeling epidemiological time-series data as well as identifying less obvious properties of the data.","","","","","","","","arXiv","2008.08039","eess.SP","","","arXiv [eess.SP]"
"Book","Krishnadas D","","Confronting Covid-19: A Strategic Playbook for Leaders and Decision Makers","","","2020","","","","COVID Tracking Project","","","","Marshall Cavendish International Asia Pte Ltd","","","","","2020-10-30","","9789814928489","","","https://play.google.com/store/books/details?id=S_MEEAAAQBAJ;https://books.google.com/books?hl=en&lr=&id=S_MEEAAAQBAJ&oi=fnd&pg=PT13&dq=%22COVID+Tracking+project%22&ots=GL90yfHoKc&sig=CaYDDgKIndRYgD0u7an2XGwfsHE","","","","","","COVID-19 is the most challenging crisis the world has faced for almost a century. As a truly global pandemic, there is not a single country on earth – or even a single person – immune to the economic, political and social impact of the devastating virus.This book analyses the coronavirus crisis in unparalleled depth.The author begins in section one by framing the COVID-19 pandemic by categorically identifying variables and factors central to understanding how COVID-19 has panned out. This is followed in section two with an examination of the pandemic in the realms of politics, public health and economics.Section three comprises in-depth country case studies, complete with scenario mapping and a formulation of recommendations, before section four looks beyond the immediate imperatives of the ongoing pandemic and pictures our shared future in a 'post-Covid' world.Confronting COVID-19's combination of rigorous, evidence-based analyses, projections and actionable recommendations makes this a must-read book for all leaders and decision makers in public, private and community sectors. Most of all, the ideas presented within these pages command the urgent attention of those within the international policy-making community.","","","","","en","","","","","","250","",""
"Preprint Manuscript","Tweedy AE","","The Validity of Tribal Checkpoints in South Dakota to Curb the Spread of COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-09","2020-12-08","","","","https://papers.ssrn.com/abstract=3622836;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3622836;http://dx.doi.org/10.2139/ssrn.3622836;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3622836","10.2139/ssrn.3622836","","","","","This essay examines the question of whether, during a public health emergency, tribes located in a state that has adopted minimal protections to curb the pandemic may enact stronger protections for their own citizens and territories. May they do so, even when enforcement of these protections causes inconvenience to those simply passing through the reservations and when the regulations affect non-member residents of the reservations? Based on Supreme Court case law, the answer is yes—tribes are within their rights in adopting and enforcing regulations designed to protect their citizens and other reservation residents from a public health emergency.","COVID-19, Tribes, Coronavirus, tribal checkpoints, South Dakota, tribal civil jurisdiction, tribal civil regulatory jurisdiction, public health, Cheyenne River Sioux, Oglala Sioux, Montana v. United States","","","","","","","","","","","","Available at SSRN 3622836"
"Journal Article","Gerbino C","","Emergenza COVID-19: dati, standard ed interoperabilità o solo dashboard?","GEOmedia","","2020","","","","COVID Tracking Project","","","","ojs.mediageo.it","","","","","2020","","","","","http://ojs.mediageo.it/index.php/GEOmedia/article/download/1719/1560","","","","","","… measurements Stan- dard (40;41) ▪ è stato creato per raccogliere e scambiare dati sull›infezione SARS- CoV-2 ma è suffi- cientemente generale per applicarsi anche ad altre tipologie di infezione o Per lo scenario statuni- tense è stato realizzato The COVID Tracking Project (42 …","","","","","","","","","","","","",""
"Journal Article","Friedson A,McNichols D,Sabia J,Dave D","","Did California's Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Benefits","NBER Working Paper","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580550","","","","","","… entering the state. COVID-19 tests are measured as the natural log of total coronavirus tests reported by each state. These data are available from COVID Tracking Project (see: https://covidtracking.com). Page 13. 11 Our primary …","","","","","","","","","","","","",""
"Journal Article","Yong E","","How the pandemic defeated America","Atlantic","Atlantic ","2020","","","","COVID Tracking Project","","","","mfprac.com","","","","","2020","","","0276-9077","","https://www.mfprac.com/web2020/07literature/literature/Infectious_Dis/Covid19-DefeatedNation_Yong.pdf","","","","","","… The official data were so clearly wrong that The Atlantic developed its own volunteer-led initiative—the COVID Tracking Project —to count cases. Diagnostic tests are easy to make, so the US failing to create one seemed inconceivable …","","","","","","","","","","","","",""
"Journal Article","Lorch L,Trouleau W,Tsirtsis S,Szanto A,et al.","","A spatiotemporal epidemic model to quantify the effects of contact tracing, testing, and containment","arXiv preprint arXiv","","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020","","","","","https://arxiv.org/abs/2004.07641;https://arxiv.org/pdf/2004.07641","","","","","","Page 1. A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment Lars Lorch1, William Trouleau2, Stratis Tsirtsis3, Aron Szanto4, Bernhard Schölkopf5, Manuel Gomez-Rodriguez3 …","","","","","","","","","","","","",""
"Preprint Manuscript","McKay T,Metzl J,Piemonte J","","Effects of Statewide Coronavirus Public Health Measures and State Gun Laws on American Gun Violence","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-24","2020-12-08","","","","https://papers.ssrn.com/abstract=3680050;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3680050;http://dx.doi.org/10.2139/ssrn.3680050;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3680050","10.2139/ssrn.3680050","","","","","The coronavirus (COVID-19) pandemic dramatically shifted American public life, and with it patterns of gun violence. In this paper, we show that states’ efforts to contain COVID-19 infections through statewide emergency declarations, Stay at Home orders, and phased reopening have significantly altered prevailing patterns of firearm injuries and deaths. We provide a systematic analysis of how state policy responses to COVID-19 affected overall levels of gun violence and specific kinds of shootings, including multiple victim and mass shootings. While emergency declarations and Stay at Home orders had a dampening effect on many forms of gun violence, we find that the number of people injured or killed by a firearm per day increased more than 15% following state reopening, on average. Over the first 30 days of reopening, we estimate that the average state had an additional 5 mass shootings than would be predicted absent the epidemic in the first 30 days of reopening. Additionally, we examine how COVID-related public health measures affect the composition of gun violence. We find that gun violence has followed workers and children home; even though workplaces and schools have closed, gun violence has likely reappeared in Americans’ lives as domestic violence related shootings and child involved shootings during Stay at Home and school closure periods. Finally, we show that state gun laws worked together with COVID-related emergency declarations and Stay at Home orders to further decrease gun violence in some states. Conversely, in states with decreased criminal liability for firearm use, as in states with Stand Your Ground laws, we observe an exacerbating effect on firearm injuries and deaths during the emergency declaration and Stay at Home order periods. Only one policy, waiting periods for handgun purchases, significantly dampened reopening surges in gun violence. These findings suggest that state policy environments can substantially reduce the impacts of exogenous shocks like COVID-19 on American gun violence and provides guidance on which policies can be helpful and which can be harmful.","coronavirus, COVID-19, gun violence, firearm injury, firearm death, state policy, gun laws, waiting period, Stand Your Ground","","","","","","","","","","","","Available at SSRN 3680050"
"Journal Article","Kurland TP,Burns D,Younger SP,Webb PB,Tyler LLP","","Emerging Insurance Disputes","pbwt.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.pbwt.com/content/uploads/2020/07/EID071620cm11.pdf","","","","","","… pandemic. 2. As of the time of this article, New York had over 397,000 COVID-19 cases and had suffered almost 25,000 deaths. See Our Data, The Covid Tracking Project , https://covidtracking.com/data (last accessed July 7, 2020). 3 …","","","","","","","","","","","","",""
"Journal Article","Rahmandad H,Lim TY,Sterman J","","Behavioral dynamics of COVID-19: estimating under-reporting, multiple waves, and adherence fatigue across 91 nations","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.24.20139451v3.full-text","","","","","","medRxiv - The Preprint Server for Health Sciences.","","","","","","","","","","","","",""
"Journal Article","Lorenzo-Redondo R,Nam HH,Roberts SC,Simons LM,Jennings LJ,Qi C,Achenbach CJ,Hauser AR,Ison MG,Hultquist JF,Ozer EA","","A clade of SARS-CoV-2 viruses associated with lower viral loads in patient upper airways","EBioMedicine","EBioMedicine","2020","62","","103112","COVID Tracking Project","","","","Elsevier","","","","","2020-11-10","","","2352-3964","","http://dx.doi.org/10.1016/j.ebiom.2020.103112;https://www.ncbi.nlm.nih.gov/pubmed/33186810;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655495;https://linkinghub.elsevier.com/retrieve/pii/S2352-3964(20)30488-6;https://www.sciencedirect.com/science/article/pii/S2352396420304886","10.1016/j.ebiom.2020.103112","33186810","","","PMC7655495","BACKGROUND: The rapid spread of SARS-CoV-2, the causative agent of Coronavirus disease 2019 (COVID-19), has been accompanied by the emergence of distinct viral clades, though their clinical significance remains unclear. Here, we aimed to investigate the phylogenetic characteristics of SARS-CoV-2 infections in Chicago, Illinois, and assess their relationship to clinical parameters. METHODS: We performed whole-genome sequencing of SARS-CoV-2 isolates collected from COVID-19 patients in Chicago in mid-March, 2020. Using these and other publicly available sequences, we performed phylogenetic, phylogeographic, and phylodynamic analyses. Patient data was assessed for correlations between demographic or clinical characteristics and virologic features. FINDINGS: The 88 SARS-CoV-2 genome sequences in our study separated into three distinct phylogenetic clades. Clades 1 and 3 were most closely related to viral sequences from New York and Washington state, respectively, with relatively broad distributions across the US. Clade 2 was primarily found in the Chicago area with limited distribution elsewhere. At the time of diagnosis, patients infected with Clade 1 viruses had significantly higher average viral loads in their upper airways relative to patients infected with Clade 2 viruses, independent of disease severity. INTERPRETATION: These results show that multiple variants of SARS-CoV-2 were circulating in the Chicago area in mid-March 2020 that differed in their relative viral loads in patient upper airways. These data suggest that differences in virus genotype can impact viral load and may influence viral spread. FUNDING: Dixon Family Translational Research Award, Northwestern University Clinical and Translational Sciences Institute (NUCATS), National Institute of Allergy and Infectious Diseases (NIAID), Lurie Comprehensive Cancer Center, Northwestern University Emerging and Re-emerging Pathogens Program.","COVID-19; Phylogenetics; SARS-CoV-2; Viral genotype; Viral load; Whole genome sequencing","","","Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA; Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. Electronic address: judd.hultquist@northwestern.edu. Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. Electronic address: e-ozer@northwestern.edu.","en","Research Article","","","","","","",""
"Journal Article","Kurland TP,Burns D,Younger SP,Webb PB,Tyler LLP","","Catastrophic Loss","pbwt.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.pbwt.com/content/uploads/2020/08/Mealys-Litigation-Report-Article-August-2020.pdf","","","","","","… pandemic. 2. As of the time of this article, New York had over 397,000 COVID-19 cases and had suffered almost 25,000 deaths. See Our Data, The Covid Tracking Project , https://covidtracking.com/data (last accessed July 7, 2020). 3 …","","","","","","","","","","","","",""
"Journal Article","D’Eramo D,Cruz CN","","COVID-19 y teorías del cambio en las políticas públicas","GIGAPP Estudios Working Papers","GIGAPP Estudios Working Papers","2020","7","182-189","569-592","COVID Tracking Project","","","","gigapp.org","","","","","2020-11-17","2020-12-08","","2174-9515","","http://www.gigapp.org/ewp/index.php/GIGAPP-EWP/article/view/226;http://www.gigapp.org/ewp/index.php/GIGAPP-EWP/article/download/226/235","","","","","","… Steven Bernard. FT ECDC Tracking COVID Tracking Project . (01-10-2020) … 578 Según datos de Finantial Times (ECDC Tracking COVID Tracking Project ), el conjunto de América La- tina concentra un 37% de las muertes por coronavirus, con Brasil y México a la cabeza …","Nueva normalidad; Equilibrio puntuado; Factor exógeno; Espacio de políticas; cambio de las políticas públicas; Argentina; imágenes de políticas","","","","","","","","","","","",""
"Journal Article","De Rosa R,Reda V","","e-politics. Il COVID-19 e la sua Politics","Comunicazione politica","","2020","","","","COVID Tracking Project","","","","rivisteweb.it","","","","","2020","","","","","https://www.rivisteweb.it/doi/10.3270/97911","","","","","","… Infine, The COVID Tracking Project at The Atlantic è uno sforzo open source, che doppia di fatto il ruolo del Centers for Disease Control10 di cui evidenzia i limiti, a lungo unica fonte di dati per il giornalismo e le istituzioni scientifiche …","","","","","","","","","","","","",""
"Journal Article","Boncz I,Endrei D","","Assessment of Countries' Preparedness and Lockdown Effectiveness in Fighting COVID-19","researchgate.net","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.researchgate.net/profile/Faten_Amer2/publication/342423292_Assessment_of_Countries'_Preparedness_and_Lockdown_Effectiveness_in_Confronting_COVID-19/links/5f9b3003a6fdccfd7b8a689c/Assessment-of-Countries-Preparedness-and-Lockdown-Effectiveness-in-Confronting-COVID-19.pdf","","","","","","Page 1. Disaster Medicine and Public Health Preparedness 1 Assessment of Countries' Preparedness and Lockdown Effectiveness in Fighting COVID-19 Faten Amer, MSc; BSc ; Sahar Hammoud, MSc; BSc; Bashar Farran, MA, BA; Imre Boncz, PhD, MD; D´ora Endrei, PhD, MD …","","","","","","","","","","","","",""
"Journal Article","Roa Jr DE","","Pandemics: How the Stock Market and Other Financial Instruments Compare to Past Pandemics","","","2020","","","","COVID Tracking Project","","","","search.proquest.com","","","","","2020","","","","","http://search.proquest.com/openview/955ca44bc4de0b0cf7e5a91f46e81cee/1?pq-origsite=gscholar&cbl=18750&diss=y","","","","","","… virus. NPR reporter Lynsey Jeffery reported that on Saturday March 21st, 2020 the, “US had conducted more than 182,000 tests in total, yielding over 23,000 confirmed positive results, according to the COVID Tracking Project ” (Jeffery, 2020). The COVID Tracking Project is a …","","","","","","","","","","","","",""
"Journal Article","Patel V,McCarthy C,Taylor RA,Moir R,Kelly LA,et al.","","An improved methodology for estimating the prevalence of SARS-CoV-2","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.08.04.20168187v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/08/06/2020.08.04.20168187.full.pdf","","","","","","Page 1. An improved methodology for estimating the prevalence of SARS- CoV-2 Virag Patel 1 , Catherine McCarthy 1 , Rachel A Taylor 1 , Ruth Moir 2 , Louise A Kelly 1,3 , Emma L Snary 1 1 Department of Epidemiological …","","","","","","","","","","","","",""
"Preprint Manuscript","Watney C,Stapp A","","Masks for All: Using Purchase Guarantees and Targeted Deregulation to Boost Production of Essential Medical Equipment","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-08","2020-12-08","","","","https://papers.ssrn.com/abstract=3590717;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3590717;http://dx.doi.org/10.2139/ssrn.3590717;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3590717;https://www.mercatus.org/system/files/watney_and_stapp_-_policy_brief_-_covid_series_-_masks_for_all_-_v1.pdf","10.2139/ssrn.3590717","","","","","Demand has rapidly outstripped supply as the urgent need for personal protective equipment (PPE) such as surgical masks, respirators, gloves, and gowns, as well as for ventilators, continues to grow apace with the COVID-19 global pandemic.On April 3, the White House and the Centers for Disease Control and Prevention (CDC) updated their guidance to recommend that the public wear cloth face masks as a stopgap measure until the production of more effective medical masks (such as surgical masks and N95 respirators) can be scaled up. Therefore, government officials need to seriously evaluate the fastest strategy for substantially increasing supply.According to estimates from the Department of Health and Human Services, the Strategic National Stockpile has about 35 million masks, amounting to only 1 percent of the number the United States will need for medical professionals alone, let alone for the general public, in the event of a full-blown pandemic in the United States. Heavy-handed government mandates may increase supply on the margin, but a more effective approach would be to unleash American industrial capacity through massive government purchase guarantees and the removal of liability risk and regulatory barriers.","face masks, hospital supplies, medical equipment, healthcare, coronavirus, coronavirus pandemic, COVID-19, public health, economics, quarantine, economy, economic crisis","","","","","","","","","","","","Mercatus Special Edition Policy Brief"
"Journal Article","홍성훈","","코로나 19 를 통해 본 미국의 인종별 격차 문제","국제노동브리프","","2020","","","","COVID Tracking Project","","","","dbpia.co.kr","","","","","2020","","","","","https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE09412563","","","","","","… _75 [그림 2] 코로나19 확진자의 인종별 분포(2020년 7월 1일까지의 통계) (단위 : %) 자료: The COVID Tracking Project . 39.7 20.4 25.2 … 다인종 기타 [그림 3] 코로나19 사망자의 인종별 분포(2020년 7월 1일까지의 통계) (단위 : %) 자료: The COVID Tracking Project . 52.7 23.3 15.1 …","","","","","","","","","","","","",""
"Preprint Manuscript","Gabrys R,Pattabiraman S,Rana V,Ribeiro J,Cheraghchi M,Guruswami V,Milenkovic O","","AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-10","","","","","http://arxiv.org/abs/2011.05223","","","2011.05223","","","The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols. The second part of the paper is concerned with examining various off-the-shelf group testing methods for Covid-19 and identifying their strengths and weaknesses for the application at hand. The third part of the paper contains a collection of new analytical results for adaptive semiquantitative group testing with probabilistic and combinatorial priors, including performance bounds, algorithmic solutions, and noisy testing protocols. The probabilistic setting is of special importance as it is designed to be simple to implement by nonexperts and handle heavy hitters. The worst-case paradigm extends and improves upon prior work on semiquantitative group testing with and without specialized PCR noise models.","","","","","","","","arXiv","2011.05223","q-bio.QM","","","arXiv [q-bio.QM]"
"Website","Brief S","","[No title]","","","","","","","COVID Tracking Project","","","","","","","","","","2020-12-08","","","","https://eric.ed.gov/?id=ED607128;https://files.eric.ed.gov/fulltext/ED607128.pdf","","","","","","… A&M, May 29, 2020. 19 “Report of the Higher Education Subcommittee,” Reopen Connecticut, May 8, 2020. 20 “US Historical Data,” The COVID Tracking Project , accessed on June 22, 2020. 21 “Number of International Students …","","","","","","","","","","","","",""
"Journal Article","Florant A,Noel N,Stewart S,Wright J","","COVID-19: Investing in black lives and livelihoods","","","2020","","","","COVID Tracking Project","","","","dataspace.princeton.edu","","","","","2020","","","","","https://dataspace.princeton.edu/handle/88435/dsp01xg94hs51q","","","","","","Page 1. April 2020 COVID-19: Investing in black lives and livelihoods An unfolding public-health and economic disaster, the COVID-19 pandemic will disproportionately impact black Americans—unless stakeholders respond immediately. Page 2 …","","","","","","","","","","","","",""
"Journal Article","Wulkow H,Conrad T,Conrad ND,Mueller SA,Nagel K,et al.","","Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.12.01.20241885v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/12/03/2020.12.01.20241885.full.pdf","","","","","","Page 1. Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts Hanna Wulkow1, Tim Conrad1,2, Nataša Djurdjevac Conrad1, Sebastian A. Mueller3, Kai Nagel3, and Christof Schuette1,2 …","","","","","","","","","","","","",""
"Journal Article","Sen BP,Padalabalanarayanan S","","Examining COVID19 Positivity-Ratio Trends in US States from April-July: Are Rising Caseloads Attributable to and Do State Political-Affiliations Play a …","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.07.20.20158485v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/07/22/2020.07.20.20158485.full.pdf","","","","","","Page 1. Examining COVID19 Positivity-Ratio Trends in US States from April-July: Are Rising Caseloads Attributable to 'More Testing' and Do State Political-Affiliations Play a Role? Running Head: COVID19 Positivity-Ratio Trends …","","","","","","","","","","","","",""
"Journal Article","Kamikubo Y,Takahashi A","","Paradoxical dynamics of SARS-CoV-2 by herd immunity and antibody-dependent enhancement","","","2020","","","","COVID Tracking Project","","","","cambridge.org","","","","","2020-05-03","2020-12-08","","","","https://www.cambridge.org/engage/coe/article-details/5ead2b518d7bf7001951c5a5?fbclid=IwAR0W0YQoWW8EVXM5vb2u2LIm7vl9DQpLGs1qY3ZdK9SAN_-1z3CO7QVMqUQ;http://dx.doi.org/10.33774/coe-2020-fsnb3;https://researchmap.jp/read0146204/published_papers/27145840/attachment_file.pdf","10.33774/coe-2020-fsnb3","","","","","The outbreak of SARS-CoV-2 in Wuhan, China caused a pandemic of COVID-19. However, it remains enigmatic why the mortality rate is variable among countries. Here we show that at least three types of SARS-CoV-2 virus, type S, K, and G. have spread globally and formed complex infectious trends in terms of transmissibility and virulence. Type K establishes herd immunity and protects against the most virulent type G. Immunity to type S is involved in aggravating type G infections through antibody-dependent enhancement (ADE). Epidemiological tools based on influenza and SARS-CoV-2 epidemic curves explain why COVID-19 mortality varies among Japan prefectures and European countries, and warns of high fatality in the United States. An equation was developed to quantify the severity of COVID-19. Our tools and equations also detect new infectious disease explosions and bioterrorism early, and guide containment of the virus with therapeutic approaches and local policies efficiently inducing herd immunity.","COVID-19;SARS-CoV-2;herd immunity;antibody-dependent enhancement","","","","en","","","","","","","",""
"Journal Article","Yarsky P","","A simple COVID-19 model applied to American states to simulate mitigation and containment strategies","Journal of Global Health Reports","","2020","","","","COVID Tracking Project","","","","joghr.org","","","","","2020","","","","","https://www.joghr.org/article/13515-a-simple-covid-19-model-applied-to-american-states-to-simulate-mitigation-and-containment-strategies/attachment/37289.docx","","","","","","… References. The COVID Tracking Project . Available: www.covidtracking.com. Accessed: 7 May 2020. Tang B, Bragazzi N, Li Q, Tang S, Xiao Y, Wu J. An Updated Estimation of the Risk of Transmission of the Novel Coronavirus (2019-nCov) …","","","","","","","","","","","","",""
"Journal Article","Platt DE,Parida LE,Zalloua P","","Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.20.20071928v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/21/2020.04.20.20071928.full.pdf","","","","","","Page 1. Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails Daniel E. Platt1, Laxmi Parida1, Pierre Zalloua2,3 1 Computational Genomics, IBM TJ Watson Research Center …","","","","","","","","","","","","",""
"Journal Article","Arnon A,Ricco J,Smetters K","","Epidemiological and Economic Effects of Lockdown","forthcoming, Brookings Papers on","","2020","","","","COVID Tracking Project","","","","brookings.edu","","","","","2020","","","","","https://www.brookings.edu/wp-content/uploads/2020/09/Arnon-et-al-conference-draft.pdf","","","","","","… sources: Johns Hopkins's Center for Systems Science and Engineering, the New York Times, the COVID Tracking Project , and USAFacts. These sources employ different data collection … deaths. We obtain counts of the number of COVID-19 tests from the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","de Sousa TCM,de Paula Moreira N,Krieger J,et al.","","Socioeconomic Vulnerabilities and the Intensity of RT-PCR SARS-CoV-2 Testing Efforts in the Public Health System in São Paulo State","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.10.29.20221960v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/03/2020.10.29.20221960.full.pdf","","","","","","… 18. The Atlantic. The COVID Tracking Project : Counting COVID-19 Tests: How States Do It, How We Do It, and What's Changing [Internet]. 2020 [cited October 26, 2020]. Available: https://covidtracking.com/blog/counting-covid-19-tests 19. Johns Hopkins University …","","","","","","","","","","","","",""
"Journal Article","Hellweg R,Cano O,Hellweg C","","Deep Analysis of the 37 COVID-19 Pandemic: A Complex Interaction of Scientific, Political, Economic and Psychological Facts 38 and Fakes","OSF Preprints. July","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Stefano_Hellweg/publication/343948597_Analyse_approfondie_de_la_pandemie_de_COVID-19_une_complexe_interaction_de_faits_et_d'intox_scientifiques_politiques_economiques_et_psychologiques/links/5f8b98d4458515b7cf8817c7/Analyse-approfondie-de-la-pandemie-de-COVID-19-une-complexe-interaction-de-faits-et-dintox-scientifiques-politiques-economiques-et-psychologiques.pdf","","","","","","Page 1. Analyse de la pandémie de COVID-19 : une complexe 1 interaction de faits et d'intox scientifiques, politiques, 2 économiques et psychologiques 3 4 Raffaele Hellweg1, Orietta Cano2 et Christian Hellweg2 5 6 1 Université …","","","","","","","","","","","","",""
"Journal Article","Sousa TCM,Moreira NP,Rosa ISC,Zamudio MM,et al.","","Using RT-PCR Testing to Assess the Effectiveness of Outbreak Control Efforts in São Paulo State, the Pandemics Epicenter in Brazil, according to …","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.10.29.20221960v3.full.pdf","","","","","","… targets/. Acessed October 26, 2020 19. The Atlantic. The COVID Tracking Project : Counting COVID-19 Tests: How States Do It, How We Do It, and What's Changing website. https://covidtracking.com/blog/counting-covid-19-tests. Acessed October 26, 2020. 20 …","","","","","","","","","","","","",""
"Journal Article","Mossavar-Rahmani S,Nelson B,Ardagna S,et al.","","A Light at the End of the Tunnel","","","2020","","","","COVID Tracking Project","","","","benchmarkglobalhospitality.com","","","","","2020","","","","","https://www.benchmarkglobalhospitality.com/i/downloads/GOLDMAN_SACHS.pdf","","","","","","… Data as of March 22, 2020. Source: Investment Strategy Group, Our World in Data, The COVID Tracking Project . Page 4. 4 Data as of March 22, 2020. Source: Investment Strategy Group, Our World in Data, The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Uribe-Tirado A,Del Río Riande G,Raiher S,et al.","","La Ciencia Abierta desde el COVID-19: Acceso Abierto+ Datos Abiertos","","","2020","","","","COVID Tracking Project","","","","repositorio.cedes.org","","","","","2020","","","","","http://repositorio.cedes.org/handle/123456789/4538;http://repositorio.cedes.org/bitstream/123456789/4538/1/La%20Ciencia%20Abierta%20desde%20el%20COVID-19.%20Acceso%20Abierto%2BDatos%20Abiertos%20%28Recopilacio%CC%81n%20actualizada.%20Versio%CC%81n%20II%29.pdf","","","","","","Page 1. LA CIENCIA ABIERTA DESDE EL COVID-19: ACCESO ABIERTO + DATOS ABIERTOS RECOPILACIÓN ACTUALIZADA. VERSIÓN II La Ciencia Abierta desde el COVID-19: Acceso Abierto + Datos Abiertos Alejandro Uribe-Tirado – Universidad de Antioquia. CoLaV …","","","","","","","","","","","","",""
"Journal Article","Miller AC,Foti NJ,Lewnard JA,Jewell NP,Guestrin C,et al.","","Mobility trends provide a leading indicator of changes in SARS-CoV-2 transmission","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.07.20094441v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/11/2020.05.07.20094441.full.pdf","","","","","","… www.nytimes.com/article/coronavirus-county-data-us.html (2020). Accessed: 2020-04-28. 22. The COVID tracking project , https://covidtracking.com (2020). Accessed: 2020-04-28. 23. MD Hoffman, A. Gelman, Journal of Machine Learning Research 15, 1593 (2014). 11 …","","","","","","","","","","","","",""
"Preprint Manuscript","Uribe-Tirado A,Del Río Riande G,Raiher S,Ochoa-Gutiérrez J","","La Ciencia Abierta desde el COVID-19: Acceso Abierto + Datos Abiertos. (Recopilación actualizada. Versión II)","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-03","2020-12-08","","","","http://eprints.rclis.org/40026;http://eprints.rclis.org/40026/1/La%20Ciencia%20Abierta%20desde%20el%20COVID-19.%20Acceso%20Abierto%2BDatos%20Abiertos%20%28Recopilaci%C3%B3n%20actualizada.%20Versi%C3%B3n%20II%29.pdf","","","","","","The coronavirus crisis has created different initiatives that promote access to open publications and open data, solve collaboratively and from different places, being an example of the benefits of open science. From the initial version of the Compilation on Open Science from COVID-19: Open Access + Open Data (Version I: April 3, 2020) published by Alejandro Uribe-Tirado (http://eprints.rclis.org/39864/), it seemed to us, a good practice to update this first input openly and collaboratively, using the platform: https://etherpad.wikimedia.org/p/covid19. This new version (Version II: June 3, 2020), is the result of this joint work.","ciencia abierta, acceso abierto, datos abiertos; open science, open access, open data; coronavirus, COVID-19, SARS, CoV-2","","","","es","","","","","","","",""
"Journal Article","Hoffman BU","","Significant Relaxation of SARS-CoV-2-Targeted Non-Pharmaceutical Interventions Will Result in Profound Mortality: A New York State Modelling Study","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-05-12","","","","","http://dx.doi.org/10.1101/2020.05.08.20095505;https://www.ncbi.nlm.nih.gov/pubmed/32511495;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273263;https://doi.org/10.1101/2020.05.08.20095505;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273263/","10.1101/2020.05.08.20095505","32511495","","","PMC7273263","Severe acute respiratory syndrome-coronavirus 2 (SARS CoV 2) is the most significant global health crisis of the 21st century. The aim of this study was to develop a model to estimate the effect of undocumented infections, seasonal infectivity, immunity, and non-pharmaceutical interventions (NPIs), such as social distancing, on the transmission, morbidity, and mortality of SARS-CoV-2 in New York State (NYS). Simulations revealed dramatic infectivity driven by undocumented infections, and a peak basic reproductive number in NYS of 5.7. NPIs have been effective, and relaxation >50% will result in tens-of-thousands more deaths. Endemic infection is likely to occur in the absence of profound sustained immunity. As a result, until an effective vaccine or other effective pharmaceutical intervention is developed, it will be critical to not reduce NPIs >50% below current levels. This study establishes fundamental characteristics of SARS CoV 2 transmission, which can help policymakers navigate combating this virus in the coming years.","","","","Vagelos College of Physicians and Surgeons, Columbia University, New York, NY.","en","Research Article","","","","","","",""
"Journal Article","Reimer JR,Ahmed SM,Brintz B,Shah RU,Keegan LT,Ferrari MJ,Leung DT","","Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-07-08","","","","","http://dx.doi.org/10.1101/2020.07.07.20148510;https://www.ncbi.nlm.nih.gov/pubmed/32676615;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7359540;https://doi.org/10.1101/2020.07.07.20148510;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7359540/","10.1101/2020.07.07.20148510","32676615","","","PMC7359540","Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.","","","","","en","Research Article","","","","","","",""
"Journal Article","Kaplan G,Moll B,Violante GL","","The Pandemic Possibility Frontier: Distributional Effects of Policy Responses to COVID-19","","","2020","","","","COVID Tracking Project","","","","pdfs.semanticscholar.org","","","","","2020","","","","","https://pdfs.semanticscholar.org/bb2f/184f381792930e085bd65990032b22681a36.pdf","","","","","","Page 1. The Pandemic Possibility Frontier: Distributional Effects of Policy Responses to COVID-19 ∗ Greg Kaplan Benjamin Moll Giovanni L. Violante August 26, 2020 Abstract We provide a quantitative analysis of the trade-offs …","","","","","","","","","","","","",""
"Journal Article","Blaselbauer VM,Josko JMB","","JSONGlue: A hybrid matcher for JSON schema matching","sbbd.org.br","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://sbbd.org.br/2020/wp-content/uploads/sites/13/2020/09/207982_1-JSONGlue-A-hybrid-matcher-for-JSON-schema-matching.pdf","","","","","","Page 1. JSONGlue: A hybrid matcher for JSON schema matching Vitor Marini Blaselbauer1, Jo˜ao Marcelo Borovina Josko1 1Center of Mathematics, Computing and Cognition – Federal University of ABC (UFABC) Av. dos Estados, 5001 – Santo Andre – SP – Brazil …","","","","","","","","","","","","",""
"Journal Article","Chen X,Chong WF,Feng R,Zhang L","","Pandemic risk management: resources contingency planning and allocation","","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Wing_Fung_Chong/publication/341480083_Pandemic_risk_management_resources_contingency_planning_and_allocation/links/5ec38646299bf1c09ac90fee/Pandemic-risk-management-resources-contingency-planning-and-allocation.pdf","","","","","","Page 1. Pandemic risk management: resources contingency planning and allocation ∗ Xiaowei Chen†1, Wing Fung Chong‡2,3, Runhuan Feng§2, and Linfeng Zhang¶2 1School of Finance, Nankai University 2Department …","","","","","","","","","","","","",""
"Journal Article","del Rio Riande G,Raiher S,Ochoa-Gutiérrez J,et al.","","La Ciencia Abierta desde el COVID-19: Acceso Abierto+ Datos Abiertos","","","2020","","","","COVID Tracking Project","","","","repositorio.hospitalelcruce.org","","","","","2020","","","","","https://repositorio.hospitalelcruce.org/xmlui/handle/123456789/975;https://repositorio.hospitalelcruce.org/xmlui/bitstream/handle/123456789/975/Revista%20HEC2020_26_46-62.pdf?sequence=3","","","","","","Page 1. 2020 Revista del Hospital El Cruce 2020(26):46-62. ISSN: 2524-9932 Licencia Creative Commons 4.0 Internacional Disponible en https://repositorio.hospitalelcruce.org/ 46 La Ciencia Abierta desde el COVID-19: Acceso Abierto + Datos Abiertos …","","","","","","","","","","","","",""
"Journal Article","Wortham JM,Lee JT,Althomsons S,et al.","","Case-Based Surveillance","The Covid-19 Reader","","2020","","","","COVID Tracking Project","","","","books.google.com","","","","","2020","","","","","https://books.google.com/books?hl=en&lr=&id=6KoLEAAAQBAJ&oi=fnd&pg=PT143&dq=%22COVID+Tracking+project%22&ots=qoCa4Ch5sJ&sig=GLV6x58ZZeXMiDm4B7C8qx6GYN0","","","","","","Page 144. 13 Characteristics of Persons Who Died With COVID-19—United States, February 12–May 18, 2020 Jonathan M. Wortham, James T. Lee, Sandy Althomsons, Julia Latash et al. On July 10, 2020, this report was posted …","","","","","","","","","","","","",""
"Journal Article","Xiao Wu MS,Sabath MB","","Updated April 5, 2020","indiaenvironmentportal.org.in","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://www.indiaenvironmentportal.org.in/files/file/COVID-19-air-pollution.pdf","","","","","","… datas ets/hospitals) County level number of hospital beds in 2019 The COVID tracking project (https://covidtracking.com/) State level number of COVID-19 tests performed up to and including April 4, 2020 Gridmet via google …","","","","","","","","","","","","",""
"Journal Article","Aiya U","","Related Podcast","infectiousdiseaseadvisor.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.infectiousdiseaseadvisor.com/home/topics/covid19/the-united-states-test-against-covid-19/","","","","","","… Diagnostic testing for severe acute respiratory syndrome–related coronavirus-2: a narrative review [published online April 13, 2020]. Ann Intern Med. doi:10.7326/M20-1301; The COVID Tracking Project . US historical data. Updated May 1, 2020. Accessed May 1, 2020 …","","","","","","","","","","","","",""
"Journal Article","Harbert R,Cunningham SW,Tessler M","","Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States","PeerJ","PeerJ","2020","8","","e10140","COVID Tracking Project","","","","peerj.com","","","","","2020-10-26","","","2167-8359","","http://dx.doi.org/10.7717/peerj.10140;https://www.ncbi.nlm.nih.gov/pubmed/33173618;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594635;https://doi.org/10.7717/peerj.10140;https://peerj.com/articles/10140/","10.7717/peerj.10140","33173618","","","PMC7594635","The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.","COVID-19; Climate; Coronavirus; SARS-CoV-2; Species distribution modeling; US","","","Biology, Stonehill College, Easton, MA, USA. Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA. Department of Biological Sciences, Fordham University, Bronx, NY, USA. Department of Biology, St. Francis College, Brooklyn, NY, USA.","en","Research Article","","","","","","",""
"Review","Adiga A,Chen J,Marathe M,Mortveit H,Venkatramanan S,Vullikanti A","","Data-Driven Modeling for Different Stages of Pandemic Response","J. Indian Inst. Sci.","Journal of the Indian Institute of Science","2020","","","1-15","COVID Tracking Project","","","","Springer","","","","","2020-11-16","","","0019-4964","0970-4140","http://dx.doi.org/10.1007/s41745-020-00206-0;https://www.ncbi.nlm.nih.gov/pubmed/33223629;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667282;https://link.springer.com/article/10.1007/s41745-020-00206-0","10.1007/s41745-020-00206-0","33223629","","","PMC7667282","Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.","","","","Biocomplexity Institute and Initiative, Charlottesville, USA. Department of Computer Science, University of Virginia, Charlottesville, USA. Department of Systems Engineering and Environment, University of Virginia, Charlottesville, USA.","en","Review","","","","","","",""
"Journal Article","Bolaños MN","","Fighting the​ Greater Recession​: All Time High Unemployment Levels and Sudden Record-Breaking State and Local Revenue Shortfalls","","","2020","","","","COVID Tracking Project","","","","nemw.org","","","","","2020","","","","","https://www.nemw.org/wp-content/uploads/2020/07/Macroeconomic-Issue-Brief-Final-Version-1-p.m..docx.pdf","","","","","","… Opportunity Insights Economic Tracker https://tracktherecovery.org/ The COVID Tracking Project https://covidtracking.com/ Urban Institute State Economic Monitor https://apps.urban.org/features/state-economic-monitor/ REFERENCES …","","","","","","","","","","","","",""
"Journal Article","Morey BN,Tulua A,Tanjasiri SP,Subica AM,et al.","","Structural Racism and Its Effects on Na-tive Hawaiians and Pacific Islanders in the United States: Issues of Health Eq-uity, Census Undercounting, and Voter …","","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Joseph_Kaholokula/publication/345821132_Structural_Racism_and_Its_Effects_on_Native_Hawaiians_and_Pacific_Islanders_in_the_United_States_Issues_of_Health_Equity_Census_Undercounting_and_Voter_Disenfranchisement/links/5faf243492851cf24cce03c4/Structural-Racism-and-Its-Effects-on-Native-Hawaiians-and-Pacific-Islanders-in-the-United-States-Issues-of-Health-Equity-Census-Undercounting-and-Voter-Disenfranchisement.pdf","","","","","","… Table 3 shows that as of June 14, 2020, only nineteen states were reporting any case data and only thirteen states were reporting any death data for NHPIs (The COVID Tracking Project , 2020). The number of states with Page 11 …","","","","","","","","","","","","",""
"Preprint Manuscript","Moy N,Antonini M,Kyhlstedt M,Paolucci F","","Categorising Policy & Technology Interventions for a Pandemic: A Comparative and Conceptual Framework","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-10","2020-12-08","","","","https://papers.ssrn.com/abstract=3622966;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3622966;http://dx.doi.org/10.2139/ssrn.3622966;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3622966","10.2139/ssrn.3622966","","","","","A number of government measures and interventions are implemented in response to viral outbreaks or declared global pandemics. To examine the impact government and non-government interventions and technological responses have on individual behaviour, epidemiology, and economic outcomes, we propose a conceptual framework that categorises government policy directives. This framework assigns a gradient indicating the severity or impact of the measure. In doing so we provide a measure that examines the effect of dominant policy initiatives on the outcomes. We demonstrate the value of the categorisation process using the interventions for the SARS-CoV-2 pandemic in Italy, New Zealand, the United Kingdom and the United States of America.","public choice, applied economics and public policy, international comparison, health policy and technology, stringency trade-offs, COVID-19 pandemic","","","","","","","","","","","","Available at SSRN"
"Journal Article","Larson T,Culbreath K,Chavez D,Larson R,Crossey M,Grenache DG","","Modeling SARS-CoV-2 Positivity Using Laboratory Data: Timing Is Everything","Clin. Chem.","Clinical chemistry","2020","66","7","981-983","COVID Tracking Project","","","","academic.oup.com","","","","","2020-07-01","","","0009-9147","1530-8561","http://dx.doi.org/10.1093/clinchem/hvaa108;https://www.ncbi.nlm.nih.gov/pubmed/32353116;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197603;https://academic.oup.com/clinchem/article-lookup/doi/10.1093/clinchem/hvaa108;https://academic.oup.com/clinchem/advance-article-abstract/doi/10.1093/clinchem/hvaa108/5827459;https://academic.oup.com/clinchem/advance-article-pdf/doi/10.1093/clinchem/hvaa108/33327729/hvaa108.pdf?casa_token=a9aALvdWw_wAAAAA:JelgdpRYtbCrzG5UNi7IOXJyoOVnR99XUlhKRAsr4Zuh6EGAtaDE7tNbgnmf4aifXW3VIANgaAk_","10.1093/clinchem/hvaa108","32353116","","","PMC7197603","… Letter to the Editor 2 Clinical Chemistry 0:0 (2020) Page 3. 4. The COVID Tracking Project . Home page. https://covid tracking.com (Accessed April 2020). Thor Larson,a Karissa Culbreath,bDennis Chavez,c Richard Larson,d Michael Crossey,b and David G. Grenacheb …","","","","Harvard College, Cambridge, MA. TriCore Reference Laboratories, Albuquerque, NM. Rhodes Group, Albuquerque, NM. University of New Mexico Health Sciences Center, Albuquerque, NM.","en","Research Article","","","","","","",""
"Journal Article","Mehl-Madrona LE,Bricaire F,Cuyugan A,Barac J,et al.","","Understanding SARSCOV-2 propagation, impacting factors to derive possible scenarios and simulations","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.07.20190066v3.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/19/2020.09.07.20190066.full.pdf","","","","","","… world- factbook/rankorder/2228rank.html. Accessed: July 2020 The Atlantic Monthly Group “The COVID Tracking Project : https://covidtracking.com/. Accessed: July 2020 Data Preparation and Analysis Data was transformed to …","","","","","","","","","","","","",""
"Journal Article","St Hilaire BG,Durand NC,Mitra N,Pulido SG,et al.","","A rapid, low cost, and highly sensitive SARS-CoV-2 diagnostic based on whole genome sequencing","bioRxiv","","2020","","","","COVID Tracking Project","","","","biorxiv.org","","","","","2020","","","","","https://www.biorxiv.org/content/10.1101/2020.04.25.061499v3.abstract;https://www.biorxiv.org/content/biorxiv/early/2020/05/11/2020.04.25.061499.full.pdf","","","","","","Page 1. A rapid, low cost, and highly sensitive SARS-CoV-2 diagnostic based on whole genome sequencing Authors: Brian Glenn St Hilaire1,2,3, Neva C. Durand1,2,3, Namita Mitra1,2,3, Saul Godinez Pulido1,2,3, Ragini Mahajan1 …","","","","","","","","","","","","",""
"Journal Article","Mauck A","","State of the Industry: Trends in Healthcare 2020","education.healthtrustpg.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://education.healthtrustpg.com/wp-content/uploads/2020/07/ForPosting_State-of-the-Industry_Final2_8.4.2020.pdf","","","","","","… 6 Source: “US Historical Data,” The COVID Tracking Project . As cases and hospitalizations rise, resource shortages threaten anew A fearsome foe, risen from the mat Advisory Board interviews and analysis. Daily Covid-19 deaths and positive tests From March 26 to July 5 0 …","","","","","","","","","","","","",""
"Journal Article","Proudman V,Sveinsdottir T,Davidson J","","An Analysis of Open Science Policies in Europe, v6","","","2020","","","46","COVID Tracking Project","","","","SPARC Europe","","","","","2020-08","2020-12-08","","","","https://eprints.gla.ac.uk/222719/;http://dx.doi.org/10.5281/zenodo.4005612;https://eprints.gla.ac.uk/222719/1/222719.pdf","10.5281/zenodo.4005612","","","","","This report is the sixth in a series of SPARC Europe and DCC analyses of national Open Science policies in Europe and covers the period between March 2020 and August 2020. This issue provides an update on activity across European Member States and relevant countries from the European Research Area. This issue includes a section on policy change related to Covid-19 and an overview of European Open Science Cloud (EOSC) policy-related activities among the European Commission supported INFRAEOSC 5b projects. To access previous versions of the analysis of Open Data and Open Science policies in Europe and other SPARC Europe reports related to Open Data please see https://sparceurope.org/what-we-do/open-data/sparc-europe-open-data-resources/.","","","","","en","","","","","","","",""
"Journal Article","Ray R,Rojas F","","Healthcare and Critical Infrastructure","contexts.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://contexts.org/blog/healthcare-and-critical-infrastructure/","","","","","","… The Centers for Disease Control and Prevention (CDC) have stopped publishing testing data. Some of the most comprehensive data are, however, available from The COVID Tracking Project … Note: Testing data from The COVID Tracking Project …","","","","","","","","","","","","",""
"Preprint Manuscript","Vanni F,Lambert D,Palatella L","","Epidemic response to physical distancing policies and their impact on the outbreak risk","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-29","","","","","http://arxiv.org/abs/2007.14620","","","2007.14620","","","We introduce a theoretical framework that highlights the impact of physical distancing variables such as human mobility and physical proximity on the evolution of epidemics and, crucially, on the reproduction number. In particular, in response to the coronavirus disease (CoViD-19) pandemic, countries have introduced various levels of 'lockdown' to reduce the number of new infections. Specifically we use a collisional approach to an infection-age structured model described by a renewal equation for the time homogeneous evolution of epidemics. As a result, we show how various contributions of the lockdown policies, namely physical proximity and human mobility, reduce the impact of SARS-CoV-2 and mitigate the risk of disease resurgence. We check our theoretical framework using real-world data on physical distancing with two different data repositories, obtaining consistent results. Finally, we propose an equation for the effective reproduction number which takes into account types of interactions among people, which may help policy makers to improve remote-working organizational structure.","","","","","","","","arXiv","2007.14620","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Kaplan G,Moll B,Violante G","","The great lockdown and the big stimulus: Tracing the pandemic possibility frontier for the US","NBER Working Paper","","2020","","","","COVID Tracking Project","","","","nber.org","","","","","2020","","","","","https://www.nber.org/system/files/working_papers/w27794/w27794.pdf","","","","","","Page 1. NBER WORKING PAPER SERIES THE GREAT LOCKDOWN AND THE BIG STIMULUS: TRACING THE PANDEMIC POSSIBILITY FRONTIER FOR THE US Greg Kaplan Benjamin Moll Giovanni L. Violante Working Paper 27794 http://www.nber.org/papers/w27794 …","","","","","","","","","","","","",""
"Journal Article","Allcott H,Boxell L,Conway JC,Ferguson BA,et al.","","What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?","NBER Working","","2020","","","","COVID Tracking Project","","","","nber.org","","","","","2020","","","","","https://www.nber.org/system/files/working_papers/w27965/w27965.pdf","","","","","","… For all dates up to the first available data, we assume no cases nor deaths. We collect state-level testing and hospitalization data from the Covid Tracking Project . 2.5 Demographic Data We supplement the policy and outcome data with data on CSA characteristics …","","","","","","","","","","","","",""
"Journal Article","Redfield RR","","Robert R. Redfield","wikizero.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.wikizero.com/www/Robert_R._Redfield","","","","","","","","","","","","","","","","","","",""
"Journal Article","Choi YW,Tuel A,Eltahir EAB","","An environmental determinant of viral respiratory disease","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.05.20123349v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/07/2020.06.05.20123349.full.pdf","","","","","","… https://data.humdata.org/. The most up-to-date COVID data for each US state at a daily temporal 140 resolution is taken from the COVID Tracking Project (https://covidtracking.com/data/). 141 Population data for world countries and US states was downloaded from 142 …","","","","","","","","","","","","",""
"Journal Article","Lin YT,Neumann J,Miller EF,Posner RG,Mallela A,Stafa C,Ray J,Thakur G,Chinthavali S,Hlavacek WS","","Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","arxiv.org","","","","","2020-07-26","","","","","http://dx.doi.org/10.1101/2020.07.20.20151506;https://www.ncbi.nlm.nih.gov/pubmed/32743595;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386519;https://doi.org/10.1101/2020.07.20.20151506;https://arxiv.org/abs/2007.12523;https://arxiv.org/pdf/2007.12523","10.1101/2020.07.20.20151506","32743595","","","PMC7386519","To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. One is a fitting function that can be calibrated to reproduce an epidemic curve with two timescales (e.g., fast growth and slow decay). The other is a compartmental model that accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating our models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting. We infer new significant upward trends for five of the metropolitan areas starting between 19-April-2020 and 12-June-2020.","","","","","en","Research Article","","","","","","",""
"Journal Article","Jung J,Manley J,Shrestha V","","Coronavirus Infections and Deaths by Poverty Status: Time Trends and Patterns","","","2020","","","","COVID Tracking Project","","","","webapps.towson.edu","","","","","2020","","","","","http://webapps.towson.edu/cbe/economics/workingpapers/2020-03.pdf","","","","","","… Testing data. We gather daily cumulative number of tests administered at the state level from The COVID Tracking Project .13 The number of coronavirus tests conducted is extracted from the local or state public health authorities. The cumulative number of testing is aggregated …","","","","","","","","","","","","",""
"Book","Sternfeld J","","Unprepared: America in the Time of Coronavirus","","","2020","","","","COVID Tracking Project","","","","Bloomsbury Publishing USA","","","","","2020-09-22","","9781635577211","","","https://play.google.com/store/books/details?id=Wh_3DwAAQBAJ;https://books.google.com/books?hl=en&lr=&id=Wh_3DwAAQBAJ&oi=fnd&pg=PT7&dq=%22COVID+Tracking+project%22&ots=NYtnkniqpw&sig=0mGop6Y4jTPip_P5jJutOw8TBbQ","","","","","","\"An essential volume.\" -E. J. Dionne, Jr. * \"A damning portrait\" -Publishers Weekly With an introduction by Pulitzer Prize-winner Timothy Egan, the riveting, eye-opening first-draft history of the Covid-19 pandemic.Unprepared is the sweeping history of the Covid-19 pandemic-a raw, primary-source accounting of the epoch-defining event: a virus that first appeared in China in late 2019 and spread rapidly across the globe, killing hundreds of thousands, devastating economies, and changing the modern world forever. A day-by-day chronicle of the response to Covid-19 as it attacked, Unprepared gathers a range of public statements from President Trump and his administration, elected officials such as New York Governor Andrew Cuomo to Atlanta Mayor Keisha Lance Bottoms, leading journalists and scientists, and organizations from National Nurses United to the United Food and Commercial Workers union. A haunting portrait of the world scrambling for answers while the number of cases rose alongside the death toll, the book reveals not only our strengths as a people, but also the fault lines and dysfunction that plague our nation in the new millennium. Unprepared is an illuminating artifact for today and for future generations, an astonishing document of history being made, and a multifaceted narrative that drops the reader directly into the real-time experience of confusion, drama, and fear that defines the outbreak of Covid-19.","","","","","en","","","","","","384","",""
"Preprint Manuscript","Barclay RA,Akhrymuk I,Patnaik A,Callahan V,Lehman C,Andersen P,Barbero R,Barksdale S,Dunlap R,Goldfarb D,Jones-Roe T,Kelly R,Kim B,Miao S,Munns A,Munns D,Patel S,Porter E,Ramsey R,Sahoo S,Swahn O,Warsh J,Kehn-Hall K,Lepene B","","Nanotrap® particles improve detection of SARS-CoV-2 for pooled sample methods, extraction-free saliva methods, and extraction-free transport medium methods","","","2020","","","2020.06.25.172510","COVID Tracking Project","","","","","","","","","2020-06-29","2020-12-08","","","","https://www.biorxiv.org/content/10.1101/2020.06.25.172510v2.abstract;http://dx.doi.org/10.1101/2020.06.25.172510;https://www.biorxiv.org/content/biorxiv/early/2020/06/29/2020.06.25.172510.full.pdf","10.1101/2020.06.25.172510","","","","","Here we present a rapid and versatile method for capturing and concentrating SARS-CoV-2 from transport medium and saliva using affinity-capture magnetic hydrogel particles. We demonstrate that the method concentrates virus prior to RNA extraction, thus significantly improving detection of the virus using a real-time RT-PCR assay across a range of viral titers, from 100 to 1,000,000 viral copies/mL; in particular, detection of virus in low viral load samples is enhanced when using the method coupled with the IDT 2019-nCoV CDC EUA Kit. This method is compatible with commercially available nucleic acid extraction kits, as well with a simple heat and detergent method. Using transport medium diagnostic remnant samples that previously had been tested for SARS-CoV-2 using either the Abbott RealTime SARS-CoV-2 EUA Test (n=14) or the Cepheid Xpert Xpress SARS-CoV-2 EUA Test (n=35), we demonstrate that our method not only correctly identifies all positive samples (n = 17) but also significantly improves detection of the virus in low viral load samples. The average improvement in cycle threshold (Ct) value as measured with the IDT 2019-nCoV CDC EUA Kit was 3.1; n = 10. Finally, to demonstrate that the method could potentially be used to enable pooled testing, we spiked infectious virus or a confirmed positive diagnostic remnant sample into 5 mL and 10 mL of negative transport medium and observed significant improvement in the detection of the virus from those larger sample volumes. ### Competing Interest Statement RAB, AP, PA, RB, SB, RD, DG, TJ, RK, SM, AM, DM, SP, EP, RR, SS, OS, JW, and BL are employees of Ceres Nanosciences Inc. KKH is a member of Ceres' Scientific Advisory Board.","","","","","en","","","","","","","","Cold Spring Harbor Laboratory"
"Journal Article","Cimoli AC","","Musei, territori, comunità interpretative: le nuove sfide della partecipazione/Museums, territories, interpretative communities: the new challenges of participation","IL CAPITALE CULTURALE. Studies on the Value of Cultural Heritage","IL CAPITALE CULTURALE. Studies on the Value of Cultural Heritage","2020","0","11","249-266","COVID Tracking Project","","","","riviste.unimc.it","","","","","2020-11-03","2020-12-08","","2039-2362","2039-2362","http://riviste.unimc.it/index.php/cap-cult/article/view/2528;http://dx.doi.org/10.13138/2039-2362/2528;https://riviste.unimc.it/index.php/cap-cult/article/download/2528/1739","10.13138/2039-2362/2528","","","","","The following article focuses on the challenges of cultural participation within the context defined by the Covid-19 pandemic. While a background noise characterized by a sort of forced, compulsive digital interaction has been accompanying the months of lockdown, making many express a renewed optimism towards the massive interest in heritage as a form of resistance, the impoverishment of the sector is casting new light on the possibilities of its agency and social impact in the next future. Still, in confused times it is paramount to isolate a few sustainable and innovative practices and to observe them throughout time, much as in a scientific lab. The article concentrates on two issues pertaining to the umbrella-concept of “participation”, and articulates each of them through a selection of recent case-studies from within the museum field: the role and agency of museums as mirrors in times of crisis and their capability of a “rapid response”, and heritage interpretation communities as a means for strengthening the social and cultural tissue through an intergenerational approach. While the focus is on the Italian context, a few international experiences are also described as potential sources of inspiration in terms of strategy and methodology.","","","","","it","","","","","","","",""
"Journal Article","Boxell L,Conway J,Ferguson B,Gentzkow M,et al.","","What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?","","","2020","","","","COVID Tracking Project","","","","stanford.edu","","","","","2020","","","","","https://web.stanford.edu/~gentzkow/research/VirusPolicy.pdf","","","","","","… For all dates up to the first available data, we assume no cases nor deaths. We collect state-level testing and hospitalization data from the Covid Tracking Project . 2.5 Demographic Data We supplement the policy and outcome data with data on CSA characteristics …","","","","","","","","","","","","",""
"Journal Article","Fuss FK,Weizman Y,Tan AM","","COVID19 pandemic: how effective are interventive control measures and is a complete lockdown justified? A comparison of countries and states","researchgate.net","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.researchgate.net/profile/Franz_Fuss/publication/344661549_COVID19_pandemic_how_effective_are_interventive_control_measures_and_is_a_complete_lockdown_justified_A_comparison_of_countries_and_states/links/5f87a1faa6fdccfd7b625f09/COVID19-pandemic-how-effective-are-interventive-control-measures-and-is-a-complete-lockdown-justified-A-comparison-of-countries-and-states.pdf","","","","","","Page 1. COVID19 pandemic: how effective are interventive control measures and is a complete lockdown justified? A comparison of countries and states Franz Konstantin Fuss, Yehuda Weizman, Adin Ming Tan Faculty of Health …","","","","","","","","","","","","",""
"Journal Article","People A","","UNITED STATES DISTRICT COURT FOR THE DISTRICT OF COLUMBIA","","","2020","","","","COVID Tracking Project","","","","savethepostoffice.com","","","","","2020","","","","","https://www.savethepostoffice.com/wp-content/uploads/2020/10/NAACP-PI-Opinion.pdf","","","","","","Page 1. 1 UNITED STATES DISTRICT COURT FOR THE DISTRICT OF COLUMBIA NATIONAL ASSOCIATION FOR THE ADVANCEMENT OF COLORED PEOPLE, Plaintiff, v. UNITED STATES POSTAL SERVICE, et al., Defendants. No. 20-cv-2295(EGS) …","","","","","","","","","","","","",""
"Journal Article","de Souza Amorim D,Alves D,Moreno AS","","Condições para a reabertura da Rede Escolar no Município de Ribeirão Preto no contexto da pandemia do covid-19","jornal.usp.br","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://jornal.usp.br/wp-content/uploads/2020/10/Condicoes-para-reabertura-da-rede-escolar-RP_24Set2020.pdf","","","","","","… a 40%. Apenas a título de comparação, o Covid Tracking Project , cujos dados são utilizados pela Johns Hopkins University, apontavam na metade de julho um aumento de positividade, chegando a cerca de 8% [53]. Essa …","","","","","","","","","","","","",""
"Journal Article","Pawlik O","","TANZANIAN HEALERS IN RURAL AND URBAN AREAS. IMPLICATIONS FOR MIGRATION","Migrations in the contemporary world: A case of Africa","","","","","","COVID Tracking Project","","","","researchgate.net","","","","","","","","","","https://www.researchgate.net/profile/Helena_Myeya/publication/345979214_Migrations_in_the_contemporary_world_A_case_of_Africa/links/5fb3b7b745851518fdacfe0c/Migrations-in-the-contemporary-world-A-case-of-Africa.pdf#page=148","","","","","","… 3), pp. 305–315; A. Nyika (2009), The Ethics of Improving African Traditional Medical Practice: Scientific or African Traditional Research?,“Acta Tropica” 112, pp. S32–S36. 18 COVID Tracking Project 2020. Page 163. 162 Olga …","","","","","","","","","","","","",""
"Journal Article","Levison ME","","MEDICAL TOPICS","Update","Update ","2020","","","","COVID Tracking Project","","","","merckmanuals.com","","","","","2020","","","","","https://www.merckmanuals.com/en-pr/professional/resourcespages/covid-19-what-we-know-about-coronaviruses","","","","","","Merck Manual. Please confirm that you are a health care professional. Yes No. Logo. Merck Manual. Professional Version. The trusted provider of medical information since 1899. ENGLISH ESPAÑOL (SPANISH) Other Languages. Search Search AZ …","","","","","","","","","","","","",""
"Book","Allen JR,West DM","","Reopening America and the World: Saving Lives and Livelihoods","","","2020","","","","COVID Tracking Project","","","","Brookings Institution Press","","","","","2020-07-07","","9780815738749","","","https://play.google.com/store/books/details?id=GFnuDwAAQBAJ;https://books.google.com/books?hl=en&lr=&id=GFnuDwAAQBAJ&oi=fnd&pg=PT6&dq=%22COVID+Tracking+project%22&ots=vPR2eVwLFO&sig=VyZPzvdjkomTk7vcK7drI9pPLMc","","","","","","The coronavirus has imposed a heavy toll on people’s lives, livelihoods, and connections with one another. As America and the world reopen from this devastating pandemic, we need to examine how the process is taking place, its impact on individual lives and livelihoods, and learn from the experiences of other nations.In this book, we look at the experiences of the United States and other countries to see what we can derive about the reopening and its economic, social, and policy impacts. We present the insights and observations of Brookings scholars who offer their thoughts and recommendations for future action. Our goals are to inform the public conversation about Covid-19, help business, government, and civic leaders take their next steps, and think about the immediate and longer-term consequences of the virus.","","","","","en","","","","","","214","",""
"Preprint Manuscript","Serra M,al-Mosleh S,Ganga Prasath S,Raju V,Mantena S,Chandra J,Iams S,Mahadevan L","","Optimal policies for mitigating pandemic costs","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-22","","","","","http://arxiv.org/abs/2007.11178","","","2007.11178","","","Several non-pharmaceutical interventions have been proposed to control the spread of the COVID-19 pandemic. On the large scale, these empirical solutions, often associated with extended and complete lockdowns, attempt to minimize the costs associated with mortality, economic losses and social factors, while being subject to constraints such as finite hospital capacity. Here we pose the question of how to mitigate pandemic costs subject to constraints by adopting the language of optimal control theory. This allows us to determine top-down policies for the nature and dynamics of social contact rates given an age-structured model for the dynamics of the disease. Depending on the relative weights allocated to life and socioeconomic losses, we see that the optimal strategies range from long-term social-distancing only for the most vulnerable, to partial lockdown to ensure not over-running hospitals, to alternating-shifts with significant reduction in life and/or socioeconomic losses. Crucially, commonly used strategies that involve long periods of broad lockdown are almost never optimal, as they are highly unstable to reopening and entail high socioeconomic costs. Using parameter estimates from data available for Germany and the USA, we quantify these policies and use sensitivity analysis in the relevant model parameters and initial conditions to determine the range of robustness of our policies. Finally we also discuss how bottom-up behavioral changes can also change the dynamics of the pandemic and show how this in tandem with top-down control policies can mitigate pandemic costs even more effectively.","","","","","","","","arXiv","2007.11178","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Redfield RR","","Career [edit]","","","","","","","COVID Tracking Project","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Journal Article","Must-See TV","","The 26th Annual Mariachi Vargas Extravaganza Goes Virtual!","beachwoodreporter.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://www.beachwoodreporter.com/music/2020/11/","","","","","","","","","","","","","","","","","","",""
"Journal Article","Ige GD","","during a phased reopening of the economy.• Engage in more frequent, effective communication with the community","","","2020","","","","COVID Tracking Project","","","","oneoahu.org","","","","","2020","","","","","https://www.oneoahu.org/s/Letter-to-Governor-with-attachments.pdf","","","","","","Page 1. May 4, 2020 Governor David Ige State Capitol, Fifth Floor Executive Chambers Honolulu, HI 86813 Aloha e Governor Ige: Hawai`i has responded well to the pandemic. We have not seen exponential growth in community …","","","","","","","","","","","","",""
"Journal Article","Rafkin C,Shreekumar A,Vautrey PL","","When Guidance Changes: Early Government Stances and Downstream Credibility","","","2020","","","","COVID Tracking Project","","","","charlierafkin.com","","","","","2020","","","","","http://charlierafkin.com/papers/rsv_covid_changing.pdf","","","","","","… imposed emergency policies by this time. According to the COVID Tracking Project (2020), there were about 9,000 total deaths from COVID in the United States by April 4. We ran our experiments on Lucid.io, a nationally representative …","","","","","","","","","","","","",""
"Journal Article","Silverstein HR","","Clyde W. Yancy, MD, Vice Dean for Diversity and Inclusion Chief of Cardiology in the Department of Medicine, Feinberg School of Medicine at Northwestern …","thepmc.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.thepmc.org/new/wp-content/uploads/2020/06/Corona-virus-6-01-20.pdf","","","","","","Page 1. By H. Robert Silverstein, MD, FACC for the Preventive Medicine Center Clyde W. Yancy, MD, Vice Dean for Diversity and Inclusion Chief of Cardiology in the Department of Medicine, Feinberg School of Medicine at Northwestern University in Chicago …","","","","","","","","","","","","",""
"Journal Article","Silverstein HR,Yancy CW","","Good Health For All","thepmc.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://www.thepmc.org/lib/corona-virus-6-01-20/","","","","","","by H. Robert Silverstein, MD, FACC -- media, politics, pandemic modelling, symptoms, lockdown, economics, joblessness, testing, ventilators, medications ...","","","","","","","","","","","","",""
"Journal Article","Sorensen CD","","Randomized Quasi-Monte Carlo and Its Applications","","","2020","","","","COVID Tracking Project","","","","search.proquest.com","","","","","2020","","","","","http://search.proquest.com/openview/0b43938100c8ad6f55689e9fc3987860/1?pq-origsite=gscholar&cbl=51922&diss=y&casa_token=AxSmYiwlatAAAAAA:SegacgQtv0cQtjE9yhT7hjFkT4gyrZMYxFN9yqOKaEr5L0v1wPEc1DC5RFSdvtqCOfKyBACmkQ","","","","","","Page 1. RANDOMIZED QUASI-MONTE CARLO AND ITS APPLICATIONS by CURTIS DANE SORENSEN, M.Sc. DISSERTATION Presented to the Graduate Faculty of The University of Texas at San Antonio In Partial Fulfillment Of the Requirements For the Degree of …","","","","","","","","","","","","",""
"Journal Article","Rafkin C,Shreekumar A,Vautrey PL","","When Guidance Changes: Government Stances and Public Beliefs","","","2020","","","","COVID Tracking Project","","","","adviksh.com","","","","","2020","","","","","https://www.adviksh.com/files/rsv_covid_changing.pdf","","","","","","… imposed emergency policies by this time. According to the COVID Tracking Project (2020), there were about 9,000 total deaths from COVID in the United States by April 4. We ran our experiments on Lucid.io, a nationally representative …","","","","","","","","","","","","",""
"Journal Article","Power CA","","Category: Uncategorized","The architect","","2020","","","","COVID Tracking Project","","","","akeratos.wordpress.com","","","","","2020","","","","","https://akeratos.wordpress.com/category/uncategorized/page/20/","","","","","","Posts about Uncategorized written by nikos athanasiou.","","","","","","","","","","","","",""
"Journal Article","Гирфанова МИ","","Новостной дискурс как способ моделирования события (на материале английского языка)","","","2020","","","","COVID Tracking Project","","","","elib.sfu-kras.ru","","","","","2020","","","","","http://elib.sfu-kras.ru/bitstream/handle/2311/135622/girfanova_magisterskaya_dissertaciya.pdf?sequence=1","","","","","","Page 1. Федеральное государственное автономное образовательное учреждение высшего образования «СИБИРСКИЙ ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ» Институт филологии и языковой коммуникации Кафедра …","","","","","","","","","","","","",""
"Journal Article","Belloir A,Blanquart F","","Estimating the global reduction in transmission and rise in detection capacity of the novel coronavirus SARS-CoV-2 in early 2020","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.10.20192120v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/09/23/2020.09.10.20192120.full.pdf","","","","","","Page 1. 1 Estimating the global reduction in transmission and rise in detection capacity of the 1 novel coronavirus SARS-CoV-2 in early 2020 2 Antoine Belloir1 3 François Blanquart2,3 4 1. Ecole Polytechnique, Route de Saclay, 91120 Palaiseau, France. 5 …","","","","","","","","","","","","",""
"Journal Article","seleccionados para el Estudiante T,Fritschy BA","","La GEOGRAFíA","fhuc.unl.edu.ar","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.fhuc.unl.edu.ar/olimpiadageo/images/pdf/2020/M%C3%B3dulo%205-Merged_BF.pdf","","","","","","Page 1. Textos seleccionados para el Estudiante y el Docente - 2020 Blanca Argentina Fritschy (Editora y compiladora) ... . La GEOGRAFíA tiene algo que decir sobre la pandemia del Covid-19 Auspicia y financia Ministeriode Educaciónde la Nación …","","","","","","","","","","","","",""
"Journal Article","Manal J,Aboelata R,Rivas LQ","","Building Bridges: The Strategic Imperative for Advancing Health Equity and Racial Justice","","","2020","","","","COVID Tracking Project","","","","preventioninstitute.org","","","","","2020","","","","","https://www.preventioninstitute.org/sites/default/files/publications/PI_Racial_Justice_Paper_063020_C.pdf","","","","","","… According to the COVID Tracking Project , African-American deaths from COVID-19 are nearly two times greater than would be expected based on their share of the population. In four states, the rate is three or more times greater …","","","","","","","","","","","","",""
"Preprint Manuscript","Goodwin BD,Jaskolski C,Zhong C,Asmani H","","Intra-model Variability in COVID-19 Classification Using Chest X-ray Images","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-04-30","","","","","http://arxiv.org/abs/2005.02167","","","2005.02167","","","X-ray and computed tomography (CT) scanning technologies for COVID-19 screening have gained significant traction in AI research since the start of the coronavirus pandemic. Despite these continuous advancements for COVID-19 screening, many concerns remain about model reliability when used in a clinical setting. Much has been published, but with limited transparency in expected model performance. We set out to address this limitation through a set of experiments to quantify baseline performance metrics and variability for COVID-19 detection in chest x-ray for 12 common deep learning architectures. Specifically, we adopted an experimental paradigm controlling for train-validation-test split and model architecture where the source of prediction variability originates from model weight initialization, random data augmentation transformations, and batch shuffling. Each model architecture was trained 5 separate times on identical train-validation-test splits of a publicly available x-ray image dataset provided by Cohen et al. (2020). Results indicate that even within model architectures, model behavior varies in a meaningful way between trained models. Best performing models achieve a false negative rate of 3 out of 20 for detecting COVID-19 in a hold-out set. While these results show promise in using AI for COVID-19 screening, they further support the urgent need for diverse medical imaging datasets for model training in a way that yields consistent prediction outcomes. It is our hope that these modeling results accelerate work in building a more robust dataset and a viable screening tool for COVID-19.","","","","","","","","arXiv","2005.02167","eess.IV","","","arXiv [eess.IV]"
"Journal Article","Ross PT,Lypson ML,Byington CL,Sánchez JP,Wong BM,Kumagai AK","","Learning From the Past and Working in the Present to Create an Antiracist Future for Academic Medicine","Acad. Med.","Academic medicine: journal of the Association of American Medical Colleges","2020","95","12","1781-1786","COVID Tracking Project","","","","journals.lww.com","","","","","2020-12","","","1040-2446","1938-808X","http://dx.doi.org/10.1097/ACM.0000000000003756;https://www.ncbi.nlm.nih.gov/pubmed/33031120;https://doi.org/10.1097/ACM.0000000000003756;https://journals.lww.com/academicmedicine/Fulltext/2020/12000/Learning_From_the_Past_and_Working_in_the_Present.1.aspx?casa_token=ulAuS1PYBxUAAAAA:HzuJYFCZy8sk8dwlBcAus_9LFj9Gy40RHr-OSdW_sPGrxKa0zxbPtIX1gh8dII-j1Hc0M2EMXOoMGEo5DxOPYnI","10.1097/ACM.0000000000003756","33031120","","","","You may be trying to access this site from a secured browser on the server. Please enable scripts and reload this page. Log in Your account has been temporarily locked Your account has been temporarily locked due to incorrect …","","","","Administrative director, Research. Innovation. Scholarship. Education. (RISE)-Michigan Medicine, University of Michigan, Ann Arbor, Michigan. Vice chair and director, Division of General Internal Medicine, and professor, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC. Executive vice president, University of California Health, Oakland, California. Professor, Emergency Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico. Associate professor of medicine, Sunnybrook Health Sciences Centre, Department of Medicine, University of Toronto, and director, Centre for Quality Improvement and Patient Safety, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Vice chair for education, Department of Medicine, and F.M. Hill Chair in Humanism Education, Women's College Hospital and University of Toronto, Toronto, Ontario, Canada.","en","Research Article","","","","","","",""
"Journal Article","Ganz SC","","Does your state have 14-days of declining COVID-19 cases? Finding straightforward answers to a surprisingly tricky question","","","2020","","","","COVID Tracking Project","","","","JSTOR","","","","","2020","","","","","https://www.jstor.org/stable/pdf/resrep24601.pdf?acceptTC=true&coverpage=false","","","","","","… about uncertainty in daily case reporting in each state. I collect data from the COVID Tracking Project , which aggregates publicly reported data on testing and patient outcomes.2 I analyze eight states: California, Florida, Massachusetts, North …","","","","","","","","","","","","",""
"Journal Article","Beland LP,Wright T","","COVID-19, Stay-at-Home Orders and Employment: Evidence from CPS Data Previous title: The Short-Term Economic Consequences of COVID-19: Exposure to …","","","2020","","","","COVID Tracking Project","","","","ir.library.carleton.ca","","","","","2020","","","","","https://ir.library.carleton.ca/pub/27084/cewp20-04.pdf","","","","","","… or week-level for each state. For this project, we rely on data from the COVID Tracking Project (https://covidtracking.com/). The database is the product of important data collection efforts relying on information from state public health …","","","","","","","","","","","","",""
"Preprint Manuscript","Rodriguez A,Muralidhar N,Adhikari B,Tabassum A,Ramakrishnan N,Aditya Prakash B","","Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-23","","","","","http://arxiv.org/abs/2009.11407","","","2009.11407","","","Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts is affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. We term the ILI values observed when it is potentially affected as COVID-ILI. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-NET, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it is should emphasize learning from COVID-related signals and when from the historical model. In such way, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.","","","","","","","","arXiv","2009.11407","cs.LG","","","arXiv [cs.LG]"
"Journal Article","Federgruen A,Naha SR","","Variation in Covid-19 Cases Across New York City","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.05.25.20112797v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/04/2020.05.25.20112797.full.pdf","","","","","","… et al. (2020) and Magoy and Wood (2020) , the latter reporting on demographic data collected by the COVID Racial Tracker, a joint project of the Antiracist Research & Policy Center and the COVID Tracking Project . It is easier …","","","","","","","","","","","","",""
"Journal Article","Knudsen R","","Testing for tracing or testing just for treating? A comparative analysis between strategies to face COVID-19 pandemic","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.01.20119123v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/11/2020.06.01.20119123.full.pdf","","","","","","… For Europe and South Korea, information is from the websites Worldometer (Total Deaths, Total Cases and Population) (4) and Our World in Data (Total Tests) (5). For the USA states, all the data is from the website “The COVID Tracking Project ” (6) …","","","","","","","","","","","","",""
"Journal Article","Green D,Loualiche E","","State and Local Government Employment in the COVID-19 Crisis","J. Public Econ.","Journal of public economics","2020","","","","COVID Tracking Project","","","","hbs.edu","","","","","2020","","","0047-2727","","https://www.hbs.edu/faculty/Publication%20Files/21-023_8619d07d-8fc8-4e17-a99f-f155af9c2c22.pdf","","","","","","… Finally, we use data from the United States Treasury on the size of the Coronavirus Relief Fund aid allocated to each state. Data on severity of the pandemic comes from the Covid Tracking Project and from Raifman et al. (2020) …","","","","","","","","","","","","",""
"Journal Article","Kakkar M,Agarwal E,Arora S","","Epidemiological Covid-19 Outbreak Prediction and Analysis using Machine Learning","Aquat. Microb. Ecol.","Aquatic microbial ecology: international journal","2020","","","","COVID Tracking Project","","","","ijari.org","","","","","2020","","","0948-3055","","https://ijari.org/assets/papers/8/2/IJARI-CS-20-06-105.pdf","","","","","","… April 2020. Fig. 2: Active Covid-19 cases across the world through World Map visualization Fig. 3: Source: FT analysis of the European Centre for Disease Prevention and Control; COVID Tracking Project ; FT research.] Figure 4 …","","","","","","","","","","","","",""
"Journal Article","Bushana P,Seignemartin B,Waraich RK,Wood WW","","COVID-19 Exposes Urgent Inequities: A Call to Action for Healthcare Reform","sciencepolicyjournal.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.sciencepolicyjournal.org/uploads/5/4/3/4/5434385/bushana_seignemartin_waraich_etal_jspg_v17.1.pdf","","","","","","… Desilver 2020; Dunn et al. 2020; Patel and McGinnis 2020). Data collected by the COVID Tracking Project shows all but one state reports some type of demographic information for their COVID-19 cases. While race or ethnicity has …","","","","","","","","","","","","",""
"Journal Article","Brown G,Ghysels E,Yi L","","Estimating Undetected COVID-19 Infections—The Case of North Carolina","","","2020","","","","COVID Tracking Project","","","","kenaninstitute.unc.edu","","","","","2020","","","","","https://kenaninstitute.unc.edu/wp-content/uploads/2020/08/Unobserved_COVID_Infection.pdf","","","","","","… propagation data from North Carolina over the period of 133 days between March 4 to August 7, 2020. We use the daily data reported by The Covid Tracking Project . The frequency of ID(t) is measured by the rolling 2-week sum of the new positive …","","","","","","","","","","","","",""
"Journal Article","Rivera R,Rosenbaum JE,Quispe W,Rico M","","Excess Mortality in the United States During the First Peak of the COVID-19 Pandemic","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.05.04.20090324v4.full.pdf","","","","","","… https://www.census.gov/data/tables/time-series/demo/popest/2010snational-detail.html. [13] Alexis Madrigal, Jeffrey Hammerbacher, Erin Kissane, and COVID Tracker team. COVID Tracking Project . https://covidtracking.com/data. [14] New York Times. Coronavirus in the US …","","","","","","","","","","","","",""
"Journal Article","Singh AS,Takhellambam MC","","COVID-19 pandemic wave: A global struggle and ways to control","Arch Community Med","","2020","","","","COVID Tracking Project","","","","peertechzpublications.com","","","","","2020","","","","","https://www.peertechzpublications.com/articles/ACMPH-6-202.pdf","","","","","","… Associated Press. Link: https://bit.ly/3iZLll8 38. US Historical Data (2020) The COVID Tracking Project . Link: https://bit.ly/3gb9pQf 39. Gearan A, DeBonis M, Dennis, Brady (2020) Trump plays down coron- avirus testing as US falls far short of level scientists say is needed …","","","","","","","","","","","","",""
"Journal Article","Nakano T,Ikeda Y","","Novel Indicator to Ascertain the Status and Trend of COVID-19 Spread: Modeling Study","J. Med. Internet Res.","Journal of medical Internet research","2020","22","11","e20144","COVID Tracking Project","","","","jmir.org","","","","","2020-11-30","","","1439-4456","1438-8871","http://dx.doi.org/10.2196/20144;https://www.ncbi.nlm.nih.gov/pubmed/33180742;https://www.jmir.org/2020/11/e20144/;https://www.jmir.org/2020/11/e20144","10.2196/20144","33180742","","","","BACKGROUND: In the fight against the pandemic of COVID-19, it is important to ascertain the status and trend of the infection spread quickly and accurately. OBJECTIVE: The purpose of our study is to formulate a new and simple indicator that represents the COVID-19 spread rate by using publicly available data. METHODS: The new indicator K is a backward difference approximation of the logarithmic derivative of the cumulative number of cases with a time interval of 7 days. It is calculated as a ratio of the number of newly confirmed cases in a week to the total number of cases. RESULTS: The analysis of the current status of COVID-19 spreading over countries showed an approximate linear decrease in the time evolution of the K value. The slope of the linear decrease differed from country to country. In addition, it was steeper for East and Southeast Asian countries than for European countries. The regional difference in the slope seems to reflect both social and immunological circumstances for each country. CONCLUSIONS: The approximate linear decrease of the K value indicates that the COVID-19 spread does not grow exponentially but starts to attenuate from the early stage. The K trajectory in a wide range was successfully reproduced by a phenomenological model with the constant attenuation assumption, indicating that the total number of the infected people follows the Gompertz curve. Focusing on the change in the value of K will help to improve and refine epidemiological models of COVID-19.","COVID-19; SARS-CoV-2; communicable diseases; infectious disease; model; modeling; spread; virus","","","Research Center for Nuclear Physics, Osaka University, Osaka, Japan. Department of Physics, Faculty of Science, Kyushu University, Fukuoka, Japan.","en","Research Article","","","","","","",""
"Preprint Manuscript","Zhu S,Bukharin A,Xie L,Santillana M,Yang S,Xie Y","","High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-15","","","","","http://arxiv.org/abs/2009.07356","","","2009.07356","","","We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (a) temporal auto- and pairwise correlation of the two local time series (confirmed cases and death of the COVID-19), (b) dynamics between locations (propagation between counties), and (c) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model's high-dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top ten metropolitan areas in the nation, which we refer (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multi-variate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability.","","","","","","","","arXiv","2009.07356","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Kendi IX","","Stop blaming Black people for dying of the coronavirus","Atlantic","Atlantic ","2020","","","","COVID Tracking Project","","","","homepages.wmich.edu","","","","","2020","","","0276-9077","","http://homepages.wmich.edu/~jswanson/fall2020/3630/readings/Black%20People%20Are%20Not%20to%20Blame%20for%20Dying%20of%20COVID-19%20-%20The%20Atlantic.pdf","","","","","","… e tracker, a collaboration between e Atlantic's COVID Tracking Project and my colleagues at the Antiracist Research and Policy Center, is being developed to track, analyze, and regularly update racial data on the pandemic within the United States …","","","","","","","","","","","","",""
"Journal Article","Ernie Bowling OD","","Hope in the time of COVID-19","Optometry Times","","2020","","","","COVID Tracking Project","","","","search.proquest.com","","","","","2020","","","","","http://search.proquest.com/openview/ba3dbc58cea0a10e99955c30b5fcf2a7/1?pq-origsite=gscholar&cbl=2029739&casa_token=jn6WLBto5joAAAAA:IkI4N5tdFK3Im0FgLG-xTXgZ2AGauKHTwcaxA_yTLXTN7svLX9hcjSBEIXdSzNuDsPMDBUNi4Q","","","","","","… html?CDC_AA_refVal=https%3A%2F%2Fwww. cdc.gov%2Fcoronavirus%2F2019- ncov%2Fabout%2Ftransmission.html. Accessed 4/7/20. 9. The COVID Tracking Project . US Historical Data. Available at: https://covidtracking.com/us-daily/. Accessed 4/7/20. 10 …","","","","","","","","","","","","",""
"Journal Article","Yu J,Shen ZJM","","Fighting COVID-19 with Flexible Testing: Models and Insights","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.11.17.20233577v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/18/2020.11.17.20233577.full.pdf","","","","","","… optimal pool sizes, and the CPRs of different testing strategies for 50 US states and Washington, DC Based on testing data from the COVID Tracking Project https://covidtracking.com/, between October 28–November 2, 2020, five states reported a seven …","","","","","","","","","","","","",""
"Journal Article","Rahmandad H,Lim TY,Sterman J","","Estimating the global spread of COVID-19","MedRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/medrxiv/early/2020/06/26/2020.06.24.20139451.full.pdf","","","","","","Page 1. 1 Preprint, Ver. 6 (November 25, 2020) - the manuscript has not been peer-reviewed yet. Title: Behavioral dynamics of COVID-19: estimating under-reporting, multiple waves, and adherence fatigue across 91 nations …","","","","","","","","","","","","",""
"Journal Article","McCoy S","","Review 1:\" Data From the COVID-19 Epidemic in Florida Suggest That Younger Cohorts Have Been Transmitting Their Infections to Less Socially Mobile …","Rapid Reviews COVID-19","","2020","","","","COVID Tracking Project","","","","rapidreviewscovid19.mitpress.mit …","","","","","2020","","","","","https://rapidreviewscovid19.mitpress.mit.edu/pub/ordchzcp","","","","","","… This well-written manuscript uses publicly available data from the Florida Department of Public Health, the COVID Tracking Project , Google Community Mobility Reports, and OpenTable to display trends in various indicators (eg, case rates, hospitalization rates, positivity rates) …","","","","","","","","","","","","",""
"Journal Article","Varshney LR,Socher R","","COVID-19 Growth Rate Decreases with Social Capital","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.04.23.20077321v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/04/29/2020.04.23.20077321.full.pdf","","","","","","… These test counts are from the COVID Tracking Project (https://covidtracking.com/data) … Test count from the COVID Tracking Project ; population is July 2019 estimate from the US Census; social capital and community health indices are from [6] …","","","","","","","","","","","","",""
"Journal Article","Wetzler HP,Wetzler EA,Cobb HW","","COVID-19: How Many Years of Life Lost","MedRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.08.20050559v2.full.pdf","","","","","","… https://doi.org/10.1101/2020.06.08.20050559 doi: medRxiv preprint Page 10. 10 26. US Historical Data | The COVID Tracking Project . https://covidtracking.com/data/us-daily Accessed June 1, 2020. 27. United States Coronavirus - Worldometer …","","","","","","","","","","","","",""
"Journal Article","Etzioni R,Markowitz E,Douglas IS","","Benchmarking COVID-19 Mortality in the United States","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.09.30.20204586v1.full-text","","","","","","… https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html. Accessed September 24, 2020. 9. US Historical Data | The COVID Tracking Project . https://covidtracking. com/data/national. Accessed September 29, 2020. 10.↵ OECD …","","","","","","","","","","","","",""
"Journal Article","Mimkes J,Janssen R","","Test-adjusted results of mortality for Covid-19 in Germany, USA, UK","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.11.03.20225268v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/11/04/2020.11.03.20225268.full.pdf","","","","","","… Page 6. 6 Fig. 9. Daily test volume T k in USA (The Covid Tracking Project [7]) Fig … [7] The Covid Tracking Project , https://covidtracking.com/data/national [8] GOV.UK, Coronavirus (COVID-19) in the UK, https://coronavirus.data.gov.uk/testing …","","","","","","","","","","","","",""
"Journal Article","Amo-Boateng M","","Tracking and Classifying Global COVID-19 Cases by using 1D Deep Convolution Neural Network","medRxiv","","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020","","","","","https://www.medrxiv.org/content/10.1101/2020.06.09.20126565v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/06/12/2020.06.09.20126565.full.pdf","","","","","","… Data sources used in compiling the CSSE database include: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, the COVID Tracking Project (testing and hospitalizations), and city, county, state and national public health departments …","","","","","","","","","","","","",""
"Journal Article","Chiu WA,Fischer R,Ndeffo-Mbah ML","","State-level needs for social distancing and contact tracing to contain COVID-19 in the United States","Nat Hum Behav","Nature human behaviour","2020","4","10","1080-1090","COVID Tracking Project","","","","nature.com","","","","","2020-10","","","2397-3374","","http://dx.doi.org/10.1038/s41562-020-00969-7;https://www.ncbi.nlm.nih.gov/pubmed/33024280;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572893;https://doi.org/10.1038/s41562-020-00969-7;https://www.nature.com/articles/s41562-020-00969-7","10.1038/s41562-020-00969-7","33024280","","","PMC7572893","Starting in mid-May 2020, many US states began relaxing social-distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of 22 July 2020, we found that only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing and distancing. Increased testing and contact-tracing capacity is paramount for mitigating the recent large-scale increases in US cases and deaths.","","","","Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. wchiu@tamu.edu. Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, TX, USA. Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. m.ndeffo@tamu.edu. Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, TX, USA. m.ndeffo@tamu.edu.","en","Research Article","","","","","","",""
"Journal Article","Kiran E","","PROMINENT ISSUES ABOUT THE SOCIAL IMPACTS OF COVID 19","Gaziantep Üniversitesi Sosyal Bilimler Dergisi","","","19","COVID-19 Special","752-766","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","dergipark.org.tr","","","","","","","","","","https://dergipark.org.tr/en/pub/jss/article/787779;https://dergipark.org.tr/en/download/article-file/1263988","","","","","","Page 1. GAZİANTEP UNIVERSITY JOURNAL OF SOCIAL SCIENCES 2020 SPECIAL ISSUE 752-766 * Sorumlu yazar/Corresponding author. e-posta: ekiran@nku.edu.tr GAZİANTEP UNIVERSITY JOURNAL OF SOCIAL SCIENCES …","","","","","","","","","","","","",""
"Preprint Manuscript","Harris AP,Pamukcu A","","Fostering the Civil Rights of Health","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-07-31","2020-11-18","","","","https://papers.ssrn.com/abstract=3675087;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3675087;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3675087","","","","","","Pandemics, like climate disasters, thrive on inequality. COVID-19 is no exception, flourishing where inequality has weakened the social fabric. One of these weaknesses is long-standing racial discrimination, which has produced unjust, racialized disparities in COVID-19 transmission and mortality, and disproportionate economic harm to people of color. Efforts to address these racial disparities have been hindered by a series of governance and advocacy disconnects. Some of these disconnects are well-known and widely discussed, such as fractures in federal, state, and local leadership that have politicized basic public health measures such as wearing masks. Less-well understood is the society-wide failure to adequately address racial discrimination in all its forms. This has perpetuated the disconnection of public health and civil rights advocacy from one another, and the disconnection of public health and civil rights professionals from anti-discrimination social movements. One promising tool to bridge these disconnects is research on the social determinants of health. Highlighting the ways in which discrimination is a public health problem allows legal advocates to use civil rights law as a health intervention and public health advocates to squarely challenge discrimination. In keeping with the emergent health justice movement, civil rights and public health advocates can amplify their effectiveness by partnering with organizations that fight discrimination. We call this approach “the civil rights of health.” This agenda for action requires (1) integrating civil rights and public health initiatives and (2) fostering three-way partnerships among civil rights, public health, and justice movement leaders (Harris & Pamukcu, 2019).This paper was prepared as part of Assessing Legal Responses to COVID-19, a comprehensive report published by Public Health Law Watch in partnership with the de Beaumont Foundation and the American Public Health Association.","COVID-19, coronavirus, legal responses, civil rights, pandemic, public health, law, public health law","","","","","","","","","","","",". Assessing Legal Responses to COVID-19 …"
"Journal Article","Kiran E","","PROMINENT ISSUES ABOUT THE SOCIAL IMPACTS OF COVID 19","Gaziantep Üniversitesi Sosyal Bilimler Dergisi","","","19","COVID-19 Special","752-766","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","dergipark.org.tr","","","","","","","","","","https://dergipark.org.tr/en/pub/jss/article/787779;https://dergipark.org.tr/en/download/article-file/1263988","","","","","","Page 1. GAZİANTEP UNIVERSITY JOURNAL OF SOCIAL SCIENCES 2020 SPECIAL ISSUE 752-766 * Sorumlu yazar/Corresponding author. e-posta: ekiran@nku.edu.tr GAZİANTEP UNIVERSITY JOURNAL OF SOCIAL SCIENCES …","","","","","","","","","","","","",""
"Preprint Manuscript","Hammond A,Jurow Kleiman A,Scheffler G","","How the COVID-19 Pandemic Has and Should Reshape the American Safety Net","","","2020","","","","DON'T USE COVID Racial Data Tracker;COVID Racial Data Tracker;COVID Tracking Project","","","","","","","","","2020-06-12","2020-11-18","","","","https://papers.ssrn.com/abstract=3625965;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3625965;http://dx.doi.org/10.2139/ssrn.3625965;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3625965","10.2139/ssrn.3625965","","","","","The COVID-19 pandemic has delivered an unprecedented shock to the United States and the world. It is unclear precisely how long the twin crises, epidemiological and economic, will last, and it is difficult to gauge the extent and direction of the changes in American life these crises will cause. Nonetheless, it is beyond dispute that the COVID-19 pandemic is putting significant strain on both the ability of Americans to meet basic needs and our government’s capacity to assist them. Federal, state, and local governments have responded in various ways to deploy existing safety net programs like Medicaid, SNAP (food stamps), tax credits, and unemployment insurance to meet the surge in need. At this early stage of the crisis, it is worth a) identifying the ways in which the pandemic feeds on and exacerbates both racial and economic inequality in America, b) analyzing the government response in detail, c) considering which changes should outlast the current crisis, and d) addressing how government, in the future, should build social welfare programs that are better suited to meet the needs of all Americans in the coming years. This Essay tries to do these four things in a way that is cogent and useful to legal and lay audiences alike.","coronavirus, COVID-19, CARES Act, social provision, health insurance, direct payments, unemployment insurance, Medicaid, SNAP, food assistance, child allowance, tax, tax policy, recovery rebate, Families First","","","","","","","","","","","","San Diego Legal Studies"
"Preprint Manuscript","Laeven L","","Pandemics, Intermediate Goods, and Corporate Valuation","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-07-01","2020-12-08","","","","https://papers.ssrn.com/abstract=3650137;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3650137;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3650137;https://repec.cepr.org/repec/cpr/ceprdp/DP15022.pdf","","","","","","We evaluate the role of input-output linkages and social distancing in transmitting the COVID-19 shock to the valuation of U.S. corporates. Using a new dataset on sectoral dependence on the use and sale of intermediate goods, we find that firms that depend on the sale of intermediate goods to sectors affected by social distancing are more affected by the crisis. We estimate that the indirect effect of social distancing through input-output linkages is at least as important as its direct effect. Several tests are consistent with the view that larger firms and firms with cash buffers are better able to absorb the pandemic shock.","cash, Intermediate goods, liquidity, Pandemic, Valuation","","","","","","","","","","","",""
"Journal Article","Allcott H,Boxell L,Conway J,Ferguson B,et al.","","Economic and health impacts of social distancing policies during the coronavirus pandemic","Available at SSRN","","2020","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2020","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3610422;http://web.stanford.edu/~gentzkow/research/VirusPolicy.pdf","","","","","","… up to the first available data, we assume no cases nor deaths. We collect state-level testing and hospitalization data from the Covid Tracking Project . 2.5 County-Level Demographic Data We supplement the policy and outcome data with data on county characteristics …","","","","","","","","","","","","",""
"Journal Article","Cox RC,Olatunji BO","","Linking insomnia and OCD symptoms during the coronavirus pandemic: Examination of prospective associations","J. Anxiety Disord.","Journal of anxiety disorders","2020","77","","102341","COVID Tracking Project","","","","Elsevier","","","","","2020-11-26","","","0887-6185","1873-7897","http://dx.doi.org/10.1016/j.janxdis.2020.102341;https://www.ncbi.nlm.nih.gov/pubmed/33285369;https://linkinghub.elsevier.com/retrieve/pii/S0887-6185(20)30155-9;https://www.sciencedirect.com/science/article/pii/S0887618520301559;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689352/;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689352","10.1016/j.janxdis.2020.102341","33285369","","","PMC7689352","There is considerable concern in the mental health community about the psychological consequences of the coronavirus pandemic and who may be most vulnerable. Obsessive-compulsive disorder (OCD) symptoms may be particularly sensitive to the context of the pandemic. Previous research suggests insomnia symptoms may contribute to increased OCD symptoms over time, particularly during times of stress, such as the pandemic. The present study examined pre-coronavirus outbreak insomnia symptoms as a predictor of post-coronavirus outbreak OCD symptoms in a sample of community adults who completed a 2016 survey study and were re-contacted on April 1, 2020 (N = 369). Results revealed a small significant increase in OCD symptoms following the coronavirus outbreak and a small significant decrease in insomnia symptoms. Pre-coronavirus outbreak insomnia symptoms significantly predicted increases in post-coronavirus outbreak OCD symptoms. Similar results were found for specific OCD symptom facets with the exception of washing and hoarding symptoms, which were unrelated to pre-coronavirus insomnia symptoms. There was no evidence for a reverse effect of prior OCD symptoms on insomnia symptoms during the pandemic. These findings suggest those with insomnia symptoms prior to the coronavirus pandemic may be vulnerable to increases in some OCD symptoms during the pandemic. The implications for preventing adverse psychological responses during the coronavirus pandemic are discussed.","COVID-19; Coronavirus; Insomnia; OCD; Pandemic","","","Vanderbilt University, United States. Electronic address: rebecca.cox.1@vanderbilt.edu. Vanderbilt University, United States.","en","Research Article","","","","","","",""
"News","Testoni L","","Le Biblioteche Digitali. Spazi informativi sempre “aperti” (soprattutto durante il lockdown)","Vedianche","Vedianche","2020","30","1","61-63","COVID Tracking Project","","","","riviste.aib.it","","","","","2020-07-21","2020-12-08","","2281-0617","2281-0617","https://riviste.aib.it/index.php/vedianche/article/view/12214;https://riviste.aib.it/index.php/vedianche/article/download/12214/11602","","","","","","Obiettivo dell'articolo è focalizzare le criticità ma anche gli aspetti promettenti delle Biblioteche digitali in ambito accademico durante il lockdown portato dalla pandemia di Sars-Covid-2, che ha compoerato un aumento sensibile nell'utilizzo di queste risorse.","","","","","it","News","","","","","","",""
"Journal Article","Molnár TG,Singletary AW,Orosz G,Ames AD","","Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays","IEEE Control Systems Letters","","2020","","","1-1","COVID Tracking Project","","","","ieeexplore.ieee.org","","","","","2020","","","2475-1456","","http://dx.doi.org/10.1109/LCSYS.2020.3040948;https://ieeexplore.ieee.org/abstract/document/9272547/?casa_token=Aww9v7pY2ecAAAAA:oZ3l9ENi18Duhz5N4a4DGPX_5EhRyLTMnGGHtDxPVRi86_XGdyc6JMgIAcFkGs8oHnv0XXDBtA;https://ieeexplore.ieee.org/iel7/7782633/7912304/09272547.pdf?casa_token=DYd7lB-73ZEAAAAA:_GxkaowLdnwZ94awYn9NA51MkCv-oXWQ2Rmkb0mxuH2gyY3K9t2f_tM4KeW5cLoOmBZtab2S1g","10.1109/LCSYS.2020.3040948","","","","","We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA.","Safety;Statistics;Sociology;Biological system modeling;Delays;Data models;COVID-19;COVID-19;epidemiology;safety-critical control;time delay.","","","","","","","","","","","",""
"Journal Article","Lorenzo-Redondo R,Nam HH,Roberts SC,Simons LM,Jennings LJ,Qi C,Achenbach CJ,Hauser AR,Ison MG,Hultquist JF,Ozer EA","","A Unique Clade of SARS-CoV-2 Viruses is Associated with Lower Viral Loads in Patient Upper Airways","medRxiv","medRxiv : the preprint server for health sciences","2020","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2020-05-26","","","","","http://dx.doi.org/10.1101/2020.05.19.20107144;https://www.ncbi.nlm.nih.gov/pubmed/32511558;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274239;https://doi.org/10.1101/2020.05.19.20107144;https://www.medrxiv.org/content/10.1101/2020.05.19.20107144v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2020/05/26/2020.05.19.20107144.full.pdf","10.1101/2020.05.19.20107144","32511558","","","PMC7274239","The rapid spread of SARS-CoV-2, the causative agent of Coronavirus disease 2019 (COVID-19), has been accompanied by the emergence of distinct viral clades, though their clinical significance remains unclear. Here, we examined the genome sequences of 88 SARS-CoV-2 viruses from COVID-19 patients in Chicago, USA and identified three distinct phylogenetic clades. Clade 1 was most closely related to clades centered in New York, and showed evidence of rapid expansion across the USA, while Clade 3 was most closely related to those in Washington. Clade 2 was localized primarily to the Chicago area with limited evidence of expansion elsewhere. Average viral loads in the airways of patients infected with the rapidly spreading Clade 1 viruses were significantly higher than those of the poorly spreading Clade 2. These results show that multiple variants of SARS-CoV-2 are circulating in the USA that differ in their relative airway viral loads and potential for expansion.","","","","Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. Department of Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.","en","Research Article","","","","","","",""
"Journal Article","Rispoli T","","La politica dei conflitti negli Stati Uniti della pandemia","LA PANDEMIA","","","","","","COVID Tracking Project","","","","amsacta.unibo.it","","","","","","","","","","http://amsacta.unibo.it/6470/1/Pensare%20la%20Pandemia.pdf#page=44","","","","","","… 18 Taylor (2020) 19 The Covid Tracking Project (2020); Pes (2020). 20 I numeri sono aggiornati a fine maggio, cfr. Aratani (2020). Page 46 … Chicago: Haymarket Books. The Covid Tracking Project [Online] Consultabile su https://covidtracking. com [ultimo accesso 25/06/2020] …","","","","","","","","","","","","",""
"Report","Bisin A,Moro A","","Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses","","","2020","","","","COVID Tracking Project","","National Bureau of Economic Research","w27590","nber.org","","","","","2020-07-27","2020-12-08","","","","https://www.nber.org/papers/w27590;http://dx.doi.org/10.3386/w27590;https://www.nber.org/system/files/working_papers/w27590/w27590.pdf","10.3386/w27590","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Civantos AM,Byrnes Y,Chang C,Prasad A,et al.","","Mental health among otolaryngology resident and attending physicians during the COVID‐19 pandemic: National study","Head &","","2020","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2020","","","","","https://onlinelibrary.wiley.com/doi/abs/10.1002/hed.26292;https://onlinelibrary.wiley.com/doi/pdf/10.1002/hed.26292","","","","","","… the “Surge,” while states that had not reached that date were “Pre Surge,” and states that were already past that date were “Post Surge.” Numbers of positive COVID‐19 cases and numbers of COVID‐19 deaths per state were obtained from the COVID Tracking Project from date …","","","","","","","","","","","","",""
"Journal Article","Carroll R,Prentice C","","On stay at home orders: Using the power of data science for spatial and temporal modeling of COVID-19","uncw.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://uncw.edu/engagement/socialimpact/carroll_prentice_2020_preprint.pdf","","","","","","… The New York Times. 2020 [cited 2020 May 20]. Available from: See Which States Are Reopening and Which Are Still Shut Down 16. The Atlantic. The COVID Tracking Project . [Internet]. CC BY-NC-4.0 license. 2020. Available from: https://covidtracking.com/ 17 …","","","","","","","","","","","","",""
"Journal Article","Mossavar-Rahmani S,Nelson B,Bajraktari Y,Dibo M,et al.","","The First Wave Crests","","","2020","","","","COVID Tracking Project","","","","blog.eckelberry.com","","","","","2020","","","","","http://blog.eckelberry.com/wp-content/uploads/2020/05/The-First-Wave-Crests-002.pdf","","","","","","… Ohio May 1-14 X √ X X X X X X Page 11. 11 Source: Investment Strategy Group, COVID Tracking Project . Testing is needed to quickly identify who may be infected so that they can be isolated and their contacts traced and also tested and self-quarantined as needed …","","","","","","","","","","","","",""
"Journal Article","Aslan A,Shah G,Sittaramane V,Shankar P","","Sewage Monitoring in Rural Communities: A Powerful Strategy for COVID-19 Surveillance","J. Environ. Health","Journal of environmental health","2020","83","","8+","COVID Tracking Project","","","","","","","","","2020-12","","","0022-0892","","https://go.gale.com/ps/anonymous?id=GALE%7CA643541259&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00220892&p=AONE&sw=w","","","","","","","Coronaviruses; Public health; COVID-19; United States. Centers for Disease Control and Prevention; Johns Hopkins University; Georgia Southern University","","","","","","","","","","","",""
"Journal Article","Khan A,Bibi S,Lyu J,Latif A,Lorenzo A","","COVID-19 and sectoral employment trends: assessing resilience in the US leisure and hospitality industry","Curr. Issues Tourism","Current Issues in Tourism","2020","","","1-18","COVID Tracking Project","","","","Routledge","","","","","2020-12-07","","","1368-3500","","https://doi.org/10.1080/13683500.2020.1850653;http://dx.doi.org/10.1080/13683500.2020.1850653;https://www.tandfonline.com/doi/full/10.1080/13683500.2020.1850653","10.1080/13683500.2020.1850653","","","","","ABSTRACT This study explores the vulnerability and resilience of the US Leisure and Hospitality industry sector-wise by taking employment levels in seven different business segments. An autoregressive distributed lag (ARDL) model approach was applied to daily time series data of employment and COVID-19 to assess each sector's fragility and resilience. The findings reveal that museums and historical places, performing arts, and sports are the worst influenced sectors and exhibit low resilience. The accommodation sector initially shows high vulnerability; however, it bounces back by showing high resilience compared to some of the other sectors. The rest of the sector presents the same story negatively influenced by pandemic but eventually reveals a sign of recovery. A detailed discussion with the theoretical and practical implications is provided.","","","","","","","","","","","","",""
"Preprint Manuscript","Bertolotti P,Jadbabaie A","","Network Group Testing","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-04","","","","","http://arxiv.org/abs/2012.02847","","","2012.02847","","","We consider the problem of identifying infected individuals in a population of size $N$. Group testing provides an approach to test the entire population using significantly fewer than $N$ tests when infection prevalence is low. The original and most commonly utilized form of group testing, called Dorfman testing, treats each individual's infection probability as independent and homogenous. However, as communicable diseases spread from individual to individual through underlying social networks, an individual's network location affects their infection probability. In this work, we utilize network information to improve group testing. Specifically, we group individuals by community and demonstrate the performance gain over Dorfman testing. After introducing a network and epidemic model, we derive the number of tests used under network grouping. We prove the expected number of tests is upper bounded by Dorfman testing. In addition, we demonstrate network grouping successfully achieves the theoretical lower bound for two-stage testing procedures when networks have strong community structure. On the other hand, network grouping is equivalent to Dorfman testing when networks have no structure. We end by demonstrating network grouping outperforms Dorfman testing in the scenario of a university testing its population for COVID-19 cases.","","","","","","","","arXiv","2012.02847","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Lundy I,Maduro R,Beckton D,Strock H,Bundy J,Asher J","","Striving for Health Equity","vhha.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.vhha.com/research/wp-content/uploads/sites/18/2020/12/COVID_19-Community-Screenings_Lessons-Learned_Sentara_VHHA.pdf","","","","","","Abstract Summary of the Problem. Large racial disparities in the Unites States were observed early in the COVID-19 pandemic. In an effort to address those disparities, Sentara Healthcare decided to offer community COVID-19 testing in early March, 2020. This paper shares our lessons learned. Five Lessons. Lesson 1 was that fear and stigma were present in all communities we serve and were a major barrier to screening. Lesson 2 was that messaging is everything. Lesson 3 was around appropriateness of walk-up & drive-through …","","","","","","","","","","","","",""
"Journal Article","Sheinson DM,Wong WB,Solon CE,Cheng MM,Shah A,Elsea D,Meng Y","","Estimated Impact of Public and Private Sector COVID-19 Diagnostics and Treatments on US Healthcare Resource Utilization","Adv. Ther.","Advances in therapy","2020","","","","COVID Tracking Project","","","","Springer","","","","","2020-12-26","","","0741-238X","1865-8652","http://dx.doi.org/10.1007/s12325-020-01597-3;https://www.ncbi.nlm.nih.gov/pubmed/33367984;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765700;https://dx.doi.org/10.1007/s12325-020-01597-3;https://link.springer.com/article/10.1007/s12325-020-01597-3","10.1007/s12325-020-01597-3","33367984","","","PMC7765700","INTRODUCTION: Coronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints. METHODS: We modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use. RESULTS: We estimated that public sector contributions led to at least 30% reductions in COVID-19-related healthcare resource utilization. Private sector contributions to expanded diagnostic testing and treatments led to further reductions in mortality (- 44%), intensive care unit (ICU) and non-ICU hospital beds (- 30% and - 28%, respectively), and ventilator use (- 29%). The combination of lower diagnostic test sensitivity and proportions of patients self-isolating may exacerbate case numbers, and policies that encourage self-isolating should be considered. CONCLUSION: While mechanisms exist to facilitate research, development, and patient access to diagnostic testing, future policies should focus on ensuring equitable patient access to both diagnostic testing and treatments that, in turn, will alleviate COVID-19-related resource constraints.","COVID-19; Diagnostic test; Health policy; Health resources","","","Genentech, Medical Affairs, South San Francisco, CA, USA. sheinson.daniel@gene.com. Genentech, Medical Affairs, South San Francisco, CA, USA. Roche Molecular Systems, Inc, Global Access and Health Economics, Pleasanton, CA, USA. Bresmed, Health Economic Analysis, Las Vegas, NV, USA.","en","Research Article","","","","","","",""
"Journal Article","Fraser-Arnott M","","Academic Library COVID-19 Subject Guides","Ref. Libr.","The Reference Librarian","2020","","","1-20","COVID Tracking Project","","","","Routledge","","","","","2020-12-22","","","0276-3877","","https://doi.org/10.1080/02763877.2020.1862021;http://dx.doi.org/10.1080/02763877.2020.1862021;https://www.tandfonline.com/doi/abs/10.1080/02763877.2020.1862021?casa_token=I2HF8KKxSY8AAAAA:vMDpEQdpfR55EOiJAPO7IeNyEiPRqA45E_WzRZT_nHebmo7GhCXgWMTmD46tCKbiWxW8i4qZQ8xK;https://www.tandfonline.com/doi/pdf/10.1080/02763877.2020.1862021?casa_token=pr5oQXH0lVYAAAAA:1Qvdpxh8DRTQ5yLDJzHh6dwHvGWu9XxslsFNDX-3dNvA76Lb1w1ytQCUTdYcAilV1DKg8EN8Cw6J","10.1080/02763877.2020.1862021","","","","","ABSTRACT This study reviewed the coronavirus (COVID-19) resources available on the Times Higher Education top 50 research universities websites. Both general coronavirus resource pages and library subject guides dedicated to the pandemic were examined. The results were compiled into a list of 416 links which were then analyzed. The most common content creators (publishers/authors) were (1) government departments and agencies, (2) academic publishers, (3) international and nonprofit organizations, and (4) universities. The most common types of resources included (1) scholarly article collections (publisher databases or preprint collections), (2) consumer health information, (3) data sets including data maps, and (4) media resources. The examination of library subject guides and university COVID-19 resource pages revealed opportunities for collaboration with other university units in the creation of resource lists for different audiences and the creation of information literacy resources with an emphasis on data literacy.","","","","","","","","","","","","",""
"Journal Article","Curran K","","Health Equity-Building Trust for Vaccine Rollout Winter 2021","chausa.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.chausa.org/publications/health-progress/article/winter-2021/health-equity---building-trust-for-vaccine-rollout","","","","","","… website, Oct. 31, 2020, https://www.cdc.gov/coronavirus/2019-ncov/covid- data/pdf/covidview-11-06-2020.pdf. \"The COVID Racial Data Tracker,\" The COVID Tracking Project at The Atlantic, https://covidtracking.com/race. Vicki S …","","","","","","","","","","","","",""
"Journal Article","Badger D,Michel N","","Mask Mandates: Do They Work? Are There Better Ways to Control COVID-19 Outbreaks?","heritage.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.heritage.org/sites/default/files/2020-12/BG3578.pdf","","","","","","… Update of New Reported Cases of COVID-19 by Country Worldwide,” https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic- distribution-covid-19-cases-worldwide (accessed December 16, 2020); The COVID Tracking …","","","","","","","","","","","","",""
"Journal Article","Singer M,Rylko-Bauer B","","The Syndemics and Structural Violence of the COVID Pandemic: Anthropological Insights on a Crisis","Open Anthropological Research","","2020","1","1","7-32","COVID Tracking Project","","","","De Gruyter","Berlin, Boston","","","","2020","","","","","https://www.degruyter.com/view/journals/opan/1/1/article-p7.xml;http://dx.doi.org/10.1515/opan-2020-0100","10.1515/opan-2020-0100","","","","","This paper examines the COVID-19 pandemic in light of two key concepts in medical anthropology: syndemics and structural violence. Following a discussion of the nature of these two concepts, the paper addresses the direct and associated literatures on the syndemic and structural violence features of the COVID pandemic, with a specific focus on: 1) the importance of local socioenvironmental conditions/demographics and disease configurations in creating varying local syndemic expressions; 2) the ways that the pandemic has exposed the grave weaknesses in global health care investment; and 3) how the syndemic nature of the pandemic reveals the rising rate of noncommunicable diseases and their potential for interaction with current and future infectious disease. The paper concludes with a discussion on the role of anthropology in responding to COVID-19 from a syndemics perspective.","","","","","","","","","","","","",""
"Preprint Manuscript","Wilkinson R,Roper M","","Homogeneous Interpretable Approximations to Heterogeneous SIR Models","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-24","","","","","http://arxiv.org/abs/2012.13424","","","2012.13424","","","The SIR-compartment model is among the simplest models that describe the spread of a disease through a population. The model makes the unrealistic assumption that the population through which the disease is spreading is well-mixed. Although real populations have heterogeneities in contacts not represented in the SIR model, it nevertheless well fits real US state Covid-19 case data. Here we demonstrate mathematically how closely the simple continuous SIR model approximates a model which includes heterogeneous contacts, and provide insight onto how one can interpret parameters gleaned from regression in the context of heterogeneous dynamics.","","","","","","","","arXiv","2012.13424","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Dowdy SC,Dunlay SM,Habermann EB,Limper AH,Liu H,Franco PM,Noe KH,Poe JD,Sampathkumar11 MD,Storlie CB,Others","","Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic","els-jbs-prod-cdn.jbs.elsevierhealth","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://els-jbs-prod-cdn.jbs.elsevierhealth.com/pb/assets/raw/Health%20Advance/journals/jmcp/jmcp_ft95_12_6.pdf","","","","","","… 1. What Healthcare Leaders Need to Know: Preparing for COVID-19. Coronavirus Update Webinar. American Hospital Association. Feb 26 2020. . 2. The COVID tracking project . The Atlantic Monthly Group. 2020. https://covidtracking.com/data/us-daily. Accessed 7/28 …","","","","","","","","","","","","",""
"Journal Article","Wang X,Ren R,Kattan MW,Jehi L,Cheng Z,Fang K","","Public Health Interventions' Effect on Hospital Use in Patients With COVID-19: Comparative Study","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","4","e25174","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-12-23","","","2369-2960","","http://dx.doi.org/10.2196/25174;https://www.ncbi.nlm.nih.gov/pubmed/33315585;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759508;https://publichealth.jmir.org/2020/4/e25174/;https://publichealth.jmir.org/2020/4/e25174","10.2196/25174","33315585","","","PMC7759508","BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE: We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS: This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS: The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio's transmission rates declined before the state's \"stay at home\" order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS: The series of public health interventions in three areas were temporally associated with the burden of COVID-19-attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.","COVID-19; China; United States; comparative; hospital; implementation; intervention; observational; prediction; public health; use","","","Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States. Department of Statistics, Xiamen University, Xiamen, China. Neurological Institute, Cleveland Clinic, Cleveland, OH, United States. Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.","en","Research Article","","","","","","",""
"Journal Article","Fernández-Salinas J,Aragón-Caqueo D,Valdés G,Laroze D","","Modeling pool testing for SARS-CoV-2: Addressing Heterogeneity in Populations","Epidemiology & Infection","Epidemiology & Infection","","","","1-26","COVID Tracking Project","","","","Cambridge University Press","","","","","","2021-01-04","","0950-2688","1469-4409","https://www.cambridge.org/core/journals/epidemiology-and-infection/article/modeling-pool-testing-for-sarscov2-addressing-heterogeneity-in-populations/C2360E1DE186C5A4A4E9C13D53952F72;http://dx.doi.org/10.1017/S0950268820003052;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C2360E1DE186C5A4A4E9C13D53952F72/S0950268820003052a.pdf/modeling-pool-testing-for-sars-cov-2-addressing-heterogeneity-in-populations.pdf","10.1017/S0950268820003052","","","","","//static.cambridge.org/content/id/urn%3Acambridge.org%3Aid%3Aarticle%3AS0950268820003052/resource/name/firstPage-S0950268820003052a.jpg","","","","","","","","","","","","",""
"Journal Article","Vernice NA,Pereira NM,Wang A,Demetres M,Adams LV","","The adverse health effects of punitive immigrant policies in the United States: A systematic review","PLoS One","PloS one","2020","15","12","e0244054","COVID Tracking Project","","","","journals.plos.org","","","","","2020-12-16","","","1932-6203","","http://dx.doi.org/10.1371/journal.pone.0244054;https://www.ncbi.nlm.nih.gov/pubmed/33326463;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744052;https://dx.plos.org/10.1371/journal.pone.0244054;https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244054","10.1371/journal.pone.0244054","33326463","","","PMC7744052","BACKGROUND: Immigrants in the United States (US) today are facing a dynamic policy landscape. The Trump administration has threatened or curtailed access to basic services for 10.5 million undocumented immigrants currently in the US. We sought to examine the historical effects that punitive laws have had on health outcomes in US immigrant communities. METHODS: In this systematic review, we searched the following databases from inception-May 2020 for original research articles with no language restrictions: Ovid MEDLINE, Ovid EMBASE, Cochrane Library (Wiley), Web of Science Core Collection (Clarivate), CINAHL (EBSCO), and Social Work Abstracts (Ovid). This study is registered with PROSPERO, CRD42019138817. Articles with cohort sizes >10 that directly evaluated the health-related effects of a punitive immigrant law or policy within the US were included. FINDINGS: 6,357 studies were screened for eligibility. Of these, 32 studies were selected for inclusion and qualitatively synthesized based upon four themes that appeared throughout our analysis: (1) impact on healthcare utilization, (2) impact on women's and children's health, (3) impact on mental health services, and (4) impact on public health. The impact of each law, policy, mandate, and directive since 1990 is briefly discussed, as are the limitations and risk of bias of each study. INTERPRETATION: Many punitive immigrant policies have decreased immigrant access to and utilization of basic healthcare services, while instilling fear, confusion, and anxiety in these communities. The federal government should preserve and expand access for undocumented individuals without threat of deportation to improve health outcomes for US citizens and noncitizens.","","","","Center for Global Health Equity, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America. Department of Medicine, Weill Cornell Medicine, New York, New York, United States of America. Samuel J. Wood Library & C.V. Starr Biomedical Information Center, Weill Cornell Medicine, New York, New York, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Truong D,Truong MD","","Projecting daily travel behavior by distance during the pandemic and the spread of COVID-19 infections – Are we in a closed loop scenario?","Transportation Research Interdisciplinary Perspectives","Transportation Research Interdisciplinary Perspectives","2021","9","100283","100283","COVID Tracking Project","","","","Elsevier BV","","","","","2021-03-01","","","2590-1982","","http://www.sciencedirect.com/science/article/pii/S2590198220301949;https://www.sciencedirect.com/science/article/pii/S2590198220301949;https://linkinghub.elsevier.com/retrieve/pii/S2590198220301949;http://dx.doi.org/10.1016/j.trip.2020.100283","10.1016/j.trip.2020.100283","","","","","Understanding the future development of COVID-19 is the key to contain the spreading of the coronavirus. The purpose of this paper is to explore a potential relationship between United States residents’ daily trips by distance and the COVID-19 infections in the near future. The study used the daily travel data from the Bureau of Transportation Statistics (BTS) and the COVID-19 data from the Centers for Disease Control and Prevention (CDC) in the United States. Time-series forecast models using Autoregressive Moving Average (ARIMA) method were constructed to project future trends of United States residents’ daily trips by distance at the national level from November 30, 2020, to February 28, 2021. A comparative trend analysis was conducted to detect the patterns of daily trips and the spread of COVID-19 during that period. The results revealed a closed loop scenario, in which the residents’ travel behavior dynamically changes based on their risk perception of COVID-19 in an infinite loop. A detected lag in the travel behavior between short trips and long trips further worsens the situation and creates more difficulties in finding an effective solution to break the loop. The study shed new light on efforts to contain and control the spread of the coronavirus. The loop can only be broken with proper and prompt mitigation strategies to reduce the burden on hospitals and healthcare systems and save more lives.","COVID-19; Coronavirus; Daily travel; Travel behavior; Forecast; Risk perception","","http://creativecommons.org/licenses/by-nc-nd/4.0/","","en","","","","","","","",""
"Journal Article","Duvisac S,Brady M,Crowley N","","PRIVATE EQUITY IS COMING","","","2020","","","","COVID Tracking Project","","","","urbandemos.nyu.edu","","","","","2020","","","","","https://urbandemos.nyu.edu/wp-content/uploads/2020/12/Private-equity-whitepaper-December-14-2020.pdf","","","","","","… Journal of Urban Affairs, 37(2), 144–165. https://doi.org/10.1111/juaf.12098 36 The COVID Racial Data Tracker. (2020). The COVID Tracking Project at the Atlantic. https://covidtracking.com/race 37 Taylor, L. (2018). Housing And Health: An Overview Of The …","","","","","","","","","","","","",""
"Journal Article","Iskander JK,Bianchi KM","","Changes in the Scientific Information Environment During the COVID-19 Pandemic: The Importance of Scientific Situational Awareness in Responding to the Infodemic","Health Secur","Health security","2020","","","","COVID Tracking Project","","","","liebertpub.com","","","","","2020-12-17","","","2326-5108","2326-5094","http://dx.doi.org/10.1089/hs.2020.0194;https://www.ncbi.nlm.nih.gov/pubmed/33347394;https://www.liebertpub.com/doi/10.1089/hs.2020.0194?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.liebertpub.com/doi/abs/10.1089/hs.2020.0194;https://www.liebertpub.com/doi/pdfplus/10.1089/hs.2020.0194","10.1089/hs.2020.0194","33347394","","","","… review. Data dashboards, Johns Hopkins University, New York Times, USAFacts, WorldoMeter, COVID Tracking Project , Aggregated national and/or international data on COVID-19 case counts, morbidity, and mortality. Source …","COVID-19; Citizen engagement; Epidemic management/response; Public health preparedness/response; Situational awareness","","","John K. Iskander, MD, is Senior Advisor and Katherine M. Bianchi is a Fellow; both in the Office of the Surgeon General (OSG), Office of the Assistant Secretary for Health, Department of Health and Human Services (HHS), Washington DC. John K. Iskander is also a Captain, United States Public Health Service. This work does not represent the official position of the OSG or HHS. Names of institutions, companies, and products are provided for identification purposes only and do not imply any endorsement by the OSG or HHS.","en","Research Article","","","","","","",""
"Website","Cottingham BW","","Improving distance education in the early grades","","","2020","","","","COVID Tracking Project","","","","edpolicyinca.org","","","","","2020","2021-01-04","","","","https://edpolicyinca.org/sites/default/files/2020-12/pb_cottingham_dec200.pdf","","","","","","… EdSource. edsource.org/2020/why-los-angeles-countys-neediest-school- districts-dont-apply-for- waivers-to-reopen-campuses/643453 27 Blagg et al., 2020. 28 The COVID Tracking Project . (nd). The COVID racial data tracker …","","","","","","","","","","","","",""
"Journal Article","Backs FI","","FLATTENED","alabamaappleseed.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.alabamaappleseed.org/wp-content/uploads/2020/12/Alabama-Appleseed-Covid-Report-Flattened.pdf","","","","","","… According to the COVID Tracking Project at The Atlantic, Black Alabamians had the highest death rate from COVID-19 at 84 per 100,000, followed by white Alabamians at 50 per 100,000 and Hispanic or Latinx at 38 per 100,000 …","","","","","","","","","","","","",""
"Website","Sen A,Lahiri P","","Estimation of mask effectiveness perception for small domains using multiple data sources","","","2020","","","","COVID Tracking Project","","","","math.umd.edu","","","","","2020","2021-01-04","","","","http://www.math.umd.edu/~plahiri/pdfs/SenLahiri.pdf","","","","","","… Thus we have combined the data in UAS coronavirus survey in conjunction with US Census Bureau data and Covid Tracking Project data to derive state level synthetic estimator of … Covid Tracking Project : Along with UAS data another data used to facilitate the …","","","","","","","","","","","","",""
"Journal Article","Ohno-Machado L","","Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers","Innovations","Innovations","","15","","16","COVID Tracking Project","","","","pdfs.semanticscholar.org","","","","","","","","0095-4519","","https://pdfs.semanticscholar.org/7679/d56c5c8c2620e12e86cc5313d7fbfd65b0d3.pdf","","","","","","Page 1. 1 Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers Madhavan S1, Bastarache L2, Brown …","","","","","","","","","","","","",""
"Website","Ala'raj M,Majdalawieh M,Nizamuddin N","","[No title]","","","","","","","COVID Tracking Project","","","","","","","","","","2021-01-04","","","","https://www.researchgate.net/profile/Nishara_Nizamuddin/publication/346776194_Modeling_and_forecasting_of_COVID-19_using_a_hybrid_dynamic_model_based_on_SEIRD_with_ARIMA_corrections/links/5fd31427299bf188d40b14bb/Modeling-and-forecasting-of-COVID-19-using-a-hybrid-dynamic-model-based-on-SEIRD-with-ARIMA-corrections.pdf","","","","","","… The model was tested and validated on the US COVID statistics dataset from the COVID Tracking Project . For validation, we use unseen recent statistical data … The proposed method was tested on US statistics from The COVID Tracking Project ( COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Montenegro de Wit M","","What grows from a pandemic? Toward an abolitionist agroecology","J. Peasant Stud.","The Journal of peasant studies","2020","","","1-38","COVID Tracking Project","","","","Routledge","","","","","2020-12-14","","","0306-6150","","https://doi.org/10.1080/03066150.2020.1854741;http://dx.doi.org/10.1080/03066150.2020.1854741;https://www.tandfonline.com/doi/abs/10.1080/03066150.2020.1854741;https://www.tandfonline.com/doi/pdf/10.1080/03066150.2020.1854741","10.1080/03066150.2020.1854741","","","","","ABSTRACT COVID-19 has exposed racialized vulnerabilities in the dominant agrifood system and granted opportunities to build anew. In this paper, I explore a series of breakdowns, from pandemic ecologies to uncontrolled infection among meatpacking workers. Agroecology has the potential to heal manifold metabolic rifts through which these problems arise. Ecologically, it offers biodiversity-based agriculture to maintain landscape complexity and buffer viral spillovers. Socially, intentional work is needed to center racism in the original accumulations through which metabolic rifts emerge. Specifically, agroecologists can mobilize lessons from abolition, a strategy premised on dismantling exploitative systems through growing relationships and institutions that affirm life.","","","","","","","","","","","","",""
"Journal Article","Zhaoying Xian MD,Javed Z,Jordan JE,Safa Alkarawi MD,Safi UK,Ms KS,Neuroradiologist HR","","Racial and Ethnic Disparities in COVID-19 Infection and Mortality in the United States: A state-wise update","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.12.03.20243360v2.full.pdf","","","","","","… all states in the United States. Methods: Publicly available data from “The COVID Tracking Project at The Atlantic” was accessed between 09/09/2020 and 09/14/2020 … Data source We used publicly available data from “The COVID Tracking Project at The Atlantic” …","","","","","","","","","","","","",""
"Journal Article","Sprau S","","Resource Allocation in Healthcare","","","2020","","","","COVID Tracking Project","","","","scholarworks.gvsu.edu","","","","","2020","2021-01-04","","","","https://scholarworks.gvsu.edu/honorsprojects/794/;https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=1800&context=honorsprojects","","","","","","The overall purpose of this research was to find ways that resources are allocated throughout the healthcare system. Resources are not always what we think of when it comes to healthcare. While it does include personal protective equipment, ventilators, and beds, it also includes the personnel that are required to deliver the care essential to survival. It is well known that many ethical issues revolve around the allocation of such resources in healthcare, but it is unknown what the best solution to sharing these resources is during pandemics such as COVID-19.","","","","","","","Honors Projects","","","","","",""
"Journal Article","Fragala MS,Goldberg ZN,Goldberg SE","","Return to Work: Managing Employee Population Health During the COVID-19 Pandemic","Popul. Health Manag.","Population health management","2020","","","","COVID Tracking Project","","","","liebertpub.com","","","","","2020-12-21","","","1942-7891","1942-7905","http://dx.doi.org/10.1089/pop.2020.0261;https://www.ncbi.nlm.nih.gov/pubmed/33347795;https://www.liebertpub.com/doi/10.1089/pop.2020.0261?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.liebertpub.com/doi/abs/10.1089/pop.2020.0261;https://www.liebertpub.com/doi/pdfplus/10.1089/pop.2020.0261","10.1089/pop.2020.0261","33347795","","","","Coronavirus disease-2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has abruptly transformed the outlook of employer health benefits plans for 2020 and 2021. Containing the spread of the virus and facilitating care of those infected have quickly emerged as immediate priorities. Employers have adjusted health benefits coverage to make COVID-19 testing and treatment accessible and remove barriers to care in order to facilitate the containment of the disease. Employers also are introducing strategies focused on testing, surveillance, workplace modifications, and hygiene to keep workforces healthy and workplaces safe. This paper is intended to provide evidence-based perspectives for self-insured employers for managing population health during the COVID-19 pandemic. Such considerations include (1) return to work practices focused on mitigating the spread of COVID-19 through safety practices, testing and surveillance; and (2) anticipating the impact of COVID-19 on health benefits and costs (including adaptations in delivery of care, social and behavioral health needs, and managing interrupted care for chronic conditions).","COVID-19 pandemic; SARS-CoV-2; employee population health; health benefits and costs; return to work practices","","","Quest Diagnostics, Secaucus, New Jersey, USA.","en","Research Article","","","","","","",""
"Website","Cottingham BW","","Improving early childhood distance education","","","2020","","","","COVID Tracking Project","","","","edpolicyinca.org","","","","","2020","2021-01-04","","","","https://edpolicyinca.org/sites/default/files/2020-12/pb_cottingham_dec2020_0.pdf","","","","","","… EdSource. edsource.org/2020/why-los-angeles-countys-neediest-school- districts-dont-apply-for- waivers-to-reopen-campuses/643453 27 Blagg et al., 2020. 28 The COVID Tracking Project . (nd). The COVID racial data tracker …","","","","","","","","","","","","",""
"Journal Article","Gurubaran A,Holy C,Shah S,Nandi B,Dwarakanathan H,Bhardwaj A,Kakade O,Coplan P","","PIN74 The Impact of Social Distancing on Sars-COV-2 Mortality: A US Analysis","Value Health","Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research","2020","23","","S557","COVID Tracking Project","","","","Elsevier","","","","","2020-12","2021-01-04","","1098-3015","","https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728568/;http://dx.doi.org/10.1016/j.jval.2020.08.915;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728568;https://www.valueinhealthjournal.com/article/S1098-3015(20)33171-5/pdf","10.1016/j.jval.2020.08.915","","","","PMC7728568","… CoV-2 infections. Methods. Number of new COVID-19 fatalities and tests performed per day per state was obtained from The COVID Tracking Project . Social distancing activity was obtained from Unacast. Unacast creates SocD …","","","","","en","","","","","","","",""
"Website","Bergquist S,Otten T,Sarich N","","[No title]","","","","","","","COVID Tracking Project","","","","researchgate.net","","","","","","2021-01-04","","","","https://www.researchgate.net/profile/Thomas_Otten4/publication/343922940_COVID-19_pandemic_in_the_United_States/links/5fbf8996a6fdcc6cc669edb8/COVID-19-pandemic-in-the-United-States.pdf","","","","","","… elements listed in Table 4. The JHU dashboard gathers data from the Center for Systems Sci- ence and Engineering at JHU, and multiple other sources, including US county and state health departments and data aggregating web- sites including the COVID Tracking …","","","","","","","","","","","","",""
"Journal Article","Levengood TW","","Financial precarity for lesbian, gay, bisexual, and transgender (LGBT) individuals during the US COVID-19 epidemic--a descriptive study","cesr.usc.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://cesr.usc.edu/sites/default/files/A19%20-%20Levengood.pdf","","","","","","… 6. Cigna. LGBT Health Disparities. https://www.cigna.com/individuals-families/health- wellness/lgbt-disparities. Published 2020. Accessed 8/21, 2020. 7. The COVID Tracking Project . Racial Data Dashboard. https://covidtracking.com/race/dashboard. Published 2020 …","","","","","","","","","","","","",""
"Journal Article","Higgins V,Sohaei D,Diamandis EP,Prassas I","","COVID-19: from3 an acute to chronic disease? Potential long-term health consequences","Crit. Rev. Clin. Lab. Sci.","Critical reviews in clinical laboratory sciences","2020","","","1-23","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-12-21","","","1040-8363","1549-781X","http://dx.doi.org/10.1080/10408363.2020.1860895;https://www.ncbi.nlm.nih.gov/pubmed/33347790;https://www.tandfonline.com/doi/full/10.1080/10408363.2020.1860895;https://www.tandfonline.com/doi/abs/10.1080/10408363.2020.1860895?casa_token=BW9C3TqjqOYAAAAA:FsV-hqRjpw-xCQFkqVP7PTacEbu8bs3h1FgGQ_l8lAWNIuQw98rWMYTlxYcbRWhT23lAXLWCmEmH;https://www.tandfonline.com/doi/pdf/10.1080/10408363.2020.1860895?casa_token=IYEKuFhRitwAAAAA:JyoOBi3l_zsNrcgDHoFvA0f_zgAby9HEpDL40BNRcDZDEoPrv1FyUuPf9iDPavJOLcP58fWfu9L0","10.1080/10408363.2020.1860895","33347790","","","","Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite pulmonary impairments being the most prevalent, extra-pulmonary manifestations of COVID-19 are abundant. Confirmed COVID-19 cases have now surpassed 57.8 million worldwide as of 22 November 2020. With estimated case fatality rates (number of deaths from COVID-19 divided by number of confirmed COVID-19 cases) varying between 1 and 7%, there will be a large population of recovered COVID-19 patients that may acquire a multitude of long-term health consequences. While the multi-organ manifestations of COVID-19 are now well-documented, the potential long-term implications of these manifestations remain to be uncovered. In this review, we turn to previous similar coronaviruses (i.e. SARS-CoV-1 and Middle East respiratory syndrome coronavirus [MERS-CoV]) in combination with known health implications of SARS-CoV-2 infection to predict potential long-term effects of COVID-19, including pulmonary, cardiovascular, hematologic, renal, central nervous system, gastrointestinal, and psychosocial manifestations, in addition to the well-known post-intensive care syndrome. It is necessary to monitor COVID-19 patients after discharge to understand the breadth and severity of long-term effects. This can be accomplished by repurposing or initiating large cohort studies to not only focus on the long-term consequences of SARS-CoV-2 infection, but also on acquired immune function as well as ethno-racial group and household income disparities in COVID-19 cases and hospitalizations. The future for COVID-19 survivors remains uncertain, and if this virus circulates among us for years to come, long-term effects may accumulate exponentially.","COVID-19; Coronavirus; chronic effects; long-COVID; pandemic","","","Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada. Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Canada. Department of Clinical Biochemistry, University Health Network, Toronto, Canada. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada.","en","Research Article","","","","","","",""
"Journal Article","Szarpak L,Pruc M,Nadolny K,Smereka J,Ladny JR","","Role of a field hospital in COVID-19 pandemic","Disaster and Emergency Medicine Journal","","2020","","","","COVID Tracking Project","","","","journals.viamedica.pl","","","","","2020","","","","","https://journals.viamedica.pl/disaster_and_emergency_medicine/article/download/DEMJ.a2020.0046/52704","","","","","","… More than 41.000 people are currently being hospitalized because of COVID-19 in the United States, a 40 per cent increase over the past month according to the COVID Tracking Project in Octo- ber, which further highlights the need to build and use field hospitals to …","","","","","","","","","","","","",""
"Journal Article","Mohler G,Short MB,Schoenberg F,Sledge D","","Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana","Statistics and Public Policy","Statistics and Public Policy","2020","","","1-17","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-12-14","","","","","https://doi.org/10.1080/2330443X.2020.1859030;http://dx.doi.org/10.1080/2330443X.2020.1859030;https://www.tandfonline.com/doi/abs/10.1080/2330443X.2020.1859030;https://www.tandfonline.com/doi/pdf/10.1080/2330443X.2020.1859030","10.1080/2330443X.2020.1859030","","","","","Abstract Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt , and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.","","","","","","","","","","","","",""
"Preprint Manuscript","Simonyan Y,Smith NC","","Coronavirus Ethics: Judgments of Market Ethics in a Pandemic","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-17","2021-01-04","","","","https://papers.ssrn.com/abstract=3750616;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3750616;http://dx.doi.org/10.2139/ssrn.3750616;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3750616","10.2139/ssrn.3750616","","","","","COVID-19 has required major changes in behavior and created significant health and economic concerns for many individuals. In this context, we have explored ethical judgments as part of a larger project on market ethics. We found that marketing practices judged as highly unethical before the pandemic, were judged to be much less unethical one year later. Of the questionable practices examined during the lockdown, those related to the pandemic (e.g., price gouging on hand sanitizer) were generally evaluated the most unethical, equal to or more unethical than the most egregious practices previously tested. Experience of lockdown affected ethical judgments, with number of people in the household, lockdown duration, and time spent on social media associated with less unethical judgments. Broader effects of the pandemic, including negative affect, diminished well-being, and financial difficulties, were also associated with less ethical concern. Implications for policymakers and marketing practitioners are discussed.","","","","","","","","","","","","",""
"Journal Article","Reyes MV","","STUDENT ESSAY The Disproportional Impact of COVID-19 on African Americans","hhrjournal.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.hhrjournal.org/2020/12/student-essay-the-disproportional-impact-of-covid-19-on-african-americans/","","","","","","… 2017), pp. 181–201. [11] Atlantic Monthly Group, COVID tracking project . Available at https://covidtracking.com. [12] “Why the African American community is being hit hard by COVID-19,” Healthline (April 13, 2020). Available …","","","","","","","","","","","","",""
"Journal Article","Hersh W","","The Informatics Response to COVID-19 Department of Medicine Grand Rounds--12/8/2020","dmice.ohsu.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://dmice.ohsu.edu/hersh/dom-20-grandrounds.pdf","","","","","","… Johns Hopkins University Center for Systems Science and Engineering – https://coronavirus.jhu.edu/map.html • University of Washington Institute for Health Metrics and Evaluation – https://covid19.healthdata.org/ • COVID Tracking Project – https://covidtracking.com …","","","","","","","","","","","","",""
"Journal Article","Hersh W","","Impact of COVID-19 on Digital Health and Health Informatics CBIS'20--XVII Congresso Brasileiro de Informática em Saúde 8 December 2020","","","2020","","","","COVID Tracking Project","","","","dmice.ohsu.edu","","","","","2020","","","","","https://dmice.ohsu.edu/hersh/cbis-20-keynote.pdf","","","","","","… Johns Hopkins University Center for Systems Science and Engineering – https://coronavirus.jhu.edu/map.html • University of Washington Institute for Health Metrics and Evaluation – https://covid19.healthdata.org/ • COVID Tracking Project – https://covidtracking.com …","","","","","","","","","","","","",""
"Journal Article","Ledder G,Homp M","","Using a COVID-19 Model in Various Classroom Settings to Assess Effects of Interventions","PRIMUS","PRIMUS","2020","","","1-19","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-12-22","","","1051-1970","","https://doi.org/10.1080/10511970.2020.1861143;http://dx.doi.org/10.1080/10511970.2020.1861143;https://www.tandfonline.com/doi/abs/10.1080/10511970.2020.1861143?casa_token=6E-ughWfsTIAAAAA:SE4_TQw9OCN8LU86mHmiyLamhPci0K8Z2YhDCBfXMkO6qOSPMwqvaINeqFlmuHVTCHjWxaXZGr7n;https://www.tandfonline.com/doi/pdf/10.1080/10511970.2020.1861143?casa_token=ISbDqf-Scv8AAAAA:gja2LGc_I5Vdcv6s5V2yw3EKx4SJgXNegYVQVMcOrey6PcAfjtmOfcQMmJZBffBhLWW4olwSlnIF","10.1080/10511970.2020.1861143","","","","","The COVID-19 pandemic has made mathematical epidemiology a topic of critical importance, providing mathematics educators with an unparalled opportunity. This opportunity is accompanied by a challenge: how do mathematics educators, some of whom have little personal experience with mathematical modeling, teach mathematical epidemiology to their students in courses ranging from precalculus to differential equations, and do so in a way that builds understanding of epidemic disease dynamics as well as mathematical methods? We address this issue with a collection of materials that allow students to conduct virtual experiments with a COVID-19 model to assess the effects of public health policies and community behavior. The materials are designed to require only a bare minimum of coding by students so as to focus students' efforts on interpretation of results.","","","","","","","","","","","","",""
"Website","Sittaramane V,Shankar P","","[No title]","","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","2021-01-04","","","","https://www.researchgate.net/profile/Gulzar_Shah/publication/346953002_Sewage_Monitoring_in_Rural_Communities_A_Powerful_Strategy_for_COVID-19_Surveillance/links/5fd3866b45851553a0abcf23/Sewage-Monitoring-in-Rural-Communities-A-Powerful-Strategy-for-COVID-19-Surveillance.pdf","","","","","","… Science of the Total Environment, 728, 138764. The Atlantic Monthly Group. (2020). The COVID Tracking Project : US historical data. Retrieved from https://covidtracking. com/data/ us-daily Centers for Disease Control and Prevention. (2020a) …","","","","","","","","","","","","",""
"Journal Article","Ibarra VJG,Almeida LHV,et al.","","Comportamiento de los ecuatorianos frente al COVID 19","Horizontes de","","2020","","","","COVID Tracking Project","","","","","","","","","2020","","","","","https://181.198.77.142/index.php/enfermeria/article/view/992;https://181.198.77.142/index.php/enfermeria/article/download/992/1906","","","","","","… Figura 1.- Mapa de coronavirus en tiempo real Fuente.- WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports …","","","","","","","","","","","","",""
"Journal Article","Nitsche ML","","Cercos al conocimiento: las lecciones del COVID19","uchile.cl","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.uchile.cl/documentos/cercos-al-conocimiento-las-lecciones-del-covid19_170205_38_3459.pdf","","","","","","… así salvar vidas. Así, se crearon múltiples plataformas de datos abiertos como The Human Coronaviruses Data Initiative, COVID-19 Open-Source Dashboard, Wikiproject COVID-19 y COVID Tracking Project . Espacios de publicación …","","","","","","","","","","","","",""
"Journal Article","Kochuparambil J,Issac A,George S,Panicker NK","","PIN70 Knowledge Towards Universal Safety Precautions Among Healthcare Population in India during Initial Phase of COVID-19: A Web Based Survey","Value Health","Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research","2020","23","","S557","COVID Tracking Project","","","","Elsevier","","","","","2020-12-01","","","1098-3015","","https://doi.org/10.1016/j.jval.2020.08.911;http://dx.doi.org/10.1016/j.jval.2020.08.911;https://www.valueinhealthjournal.com/article/S1098-3015(20)33167-3/abstract","10.1016/j.jval.2020.08.911","","","","","… This study's aim was to evaluate the association between the SocD stringency and mortality from SARS-CoV-2 infections. Methods: Number of new COVID-19 fatalities and tests performed per day per state was obtained from The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Serediuk V,Piniazhko O,Topachevskyi O,Dumenko T,Kovtun L","","PIN114 Estimation of Quality-Adjusted Life YEARS (QALY) Losses Associated with COVID-19 Deaths in Ukraine","Value Health","Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research","2020","23","","S562","COVID Tracking Project","","","","Elsevier","","","","","2020-12-01","","","1098-3015","","https://doi.org/10.1016/j.jval.2020.08.955;http://dx.doi.org/10.1016/j.jval.2020.08.955;https://www.valueinhealthjournal.com/article/S1098-3015(20)33211-3/abstract","10.1016/j.jval.2020.08.955","","","","","… Methods: Data for SARS-CoV-2 cases and daily testing counts was obtained from The COVID Tracking Project . The family sizes and population density were obtained from the US census. Social distancing scores were purchased from UnaCast …","","","","","","","","","","","","",""
"Journal Article","Shields W","","The COVID-19 Pandemic: Early Lessons for Public Governance The United States Experience","Good Public Governance in a Global Pandemic","","","","","429","COVID Tracking Project","","","","iris.unibocconi.it","","","","","","","","","","https://iris.unibocconi.it/bitstream/11565/4033608/1/good%20public%20governance.pdf#page=438","","","","","","… Emergency Management Agency [FEMA], 2020). Another: an average 269000 new tests conducted daily with 15.6 million Americans tested to date as of May 28 (The COVID Tracking Project , 2020). Whether the number of these …","","","","","","","","","","","","",""
"Book","Stottlemyre T","","The Observer: A Modern Fable on Mastering Your Mind","","","2020","","","","COVID Tracking Project","","","","Made For Success Publishing","","","","","2020-12-29","","9781641465557","","","https://play.google.com/store/books/details?id=bdYPEAAAQBAJ;https://books.google.com/books?hl=en&lr=&id=bdYPEAAAQBAJ&oi=fnd&pg=PT6&dq=%22COVID+tracking+project%22&ots=kH5aXMFPwu&sig=wvCXC7u8snquA31Mz1awVLch5kg","","","","","","The two anchors in Kat's frenzied life have been her father; a famous baseball pitcher turned team manager, and her son, who is following in his grandfather's footsteps. When both anchors become unstable, Kat's life tips dangerously out of balance. The market and her finances flip, and relationships start slipping through her fingers. Eager for solutions, she turns to find uncanny wisdom from places she never expected.The Observer unpacks the idea of 180-degree thinking, which changes everything for Kat. Now, seemingly impossible goals come into focus with crystal clear clarity. As Kat focuses on the right things, the impossible becomes her new reality.Imparted with truth and wisdom, The Observer is a classic for discovering the peak performer within yourself. This timeless story of success principles is more important today than it has ever been before as uncertainty lurks right around the corner.“A powerful work with insights that, once applied, will help you lift your life to a completely new level.”—Robin Sharma, #1 bestselling author of The 5AM Club and The Monk Who Sold His FerrariKat has it all—money, success, recognition, influence—except the one thing she desperately desires: a fulfilled life. A business entrepreneur in the high-end sportswear industry, Kat is driven in relentless pursuit of ever-greater success.","","","","","en","","","","","","185","",""
"Journal Article","Stillwell H","","virology blog About viruses and viral disease Helen Stilwell","virology.ws","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.virology.ws/category/helen-stilwell/","","","","","","… Conclusions. As of March 24, 359,161 COVID-19 tests have been performed in the US (The COVID Tracking Project ) … Synthego. (2020). https://www.synthego.com/blog/crispr- coronavirus-detection. The COVID Tracking Project . https://covidtracking.com/us-daily …","","","","","","","","","","","","",""
"Journal Article","Hua T,Kim CC,Zhang Z,Lyford A","","COVID-19 Tweets of Governors and Health Experts: Deaths, Masks, and the Economy","timhua.me","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://timhua.me/CovidResearch.pdf","","","","","","Page 1. 1 COVID-19 Tweets of Governors and Health Experts: Deaths, Masks, and the Economy Tim Hua, Chris Chankyo Kim, Zihan Zhang, & Alex Lyford Abstract As COVID-19 spread throughout the United States, governors …","","","","","","","","","","","","",""
"Website","Gleckman H,Favreault February MM","","Reforming long-term care with lessons from the COVID-19 pandemic","","","2021","","","","COVID Tracking Project","","","","urban.org","","","","","2021","2021-04-02","","","","https://www.urban.org/sites/default/files/publication/103758/reforming-long-term-care-with-lessons-from-the-covid-19-pandemic_0.pdf","","","","","","… The COVID Tracking Project at the Atlantic estimates that as of February 18, 2021, more than 170,600 residents of long-term care facilities had died from COVID-19, representing about 35 percent of all COVID-19-related deaths in the United States while making up less …","","","","","","","","","","","","",""
"Journal Article","Feng C,Lee TY,Tam D","","Disease Outbreak Radar: A Tool for Epidemiologists","cs.ubc.ca","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.cs.ubc.ca/~tmm/courses/547-20/projects/cloris-derek-harry/proposal.pdf","","","","","","… Page 4. 7 COVID Tracking Project by The Atlantic [16] 8 Visualizations That Really Work on Harvard Business Review [17] … 22, 2020). [16] “Visualization Guide,” ​The COVID Tracking Project ​. https://covidtracking.com/about-data/visualization-guide (accessed Oct. 22 …","","","","","","","","","","","","",""
"Journal Article","Buchanan W,Perera K,Sah T,Yan S","","County-Level COVID-19 Case Predictions using Deep Learning","cs230.stanford.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://cs230.stanford.edu/projects_fall_2020/reports/55789981.pdf","","","","","","… We obtained our data via the C3.ai COVID-19 API (https://c3.ai/covid-19-api- documentation/) which allows us to pull the latest data surrounding COVID from sources such as the John Hopkins University, the COVID Tracking Project , and The New York Times as well …","","","","","","","","","","","","",""
"Journal Article","Rubio Bermúdez Á","","ANÁLISIS DEL IMPACTO EN EL COVID-19 ASOCIADO A COMPORTAMIENTOS DE MOVILIDAD Y MEDIDAS GUBERNAMENTALES","openaccess.uoc.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://openaccess.uoc.edu/webapps/o2/handle/10609/126468;http://openaccess.uoc.edu/webapps/o2/bitstream/10609/126468/1/AlvaroRubioTFG.pdf","","","","","","… se recolectan, unifican y consolidan datos provenientes de un amplio abanico de organizaciones y fuentes reconocidas como; World Health Organization (WHO), European Centre for Disease Prevention and Control (ECDC), US CDC, COVID Tracking Project , National …","","","","","","","","","","","","",""
"Journal Article","Richter D","","1. Der notwendige Lockdown? Fragestellung, methodisches Vorgehen–und ein erkenntnistheoretisches Problem","War der Coronavirus-Lockdown notwendig?","","2021","","","","COVID Tracking Project","","","","degruyter.com","","","","","2021","","","","","https://www.degruyter.com/document/doi/10.14361/9783839455456-002/html","","","","","","… und war und ist oftmals ein unverzichtba- res Übersetzungsmedium zwischen Forschung und nicht-wissenschaftlichem Publikum.Hilfreich ist auch das neue Genre des Datenjournalismus gewesen, beispielsweise das amerikanische › COVID Tracking Project ‹ der …","","","","","","","","","","","","",""
"Journal Article","Feng C,Lee TY,Tam D","","Disease Outbreak Radar: A Tool for Public Health Users","cs.ubc.ca","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.cs.ubc.ca/~tmm/courses/547-20/projects/cloris-derek-harry/final-report.pdf","","","","","","Page 1. Disease Outbreak Radar: A Tool for Public Health Users Cloris Feng, Tae Yoon Lee, Derek Tam Fig. 1. Clipping of the final Outbreak Radar dashboard tool. The primary navigation panel is composed of the heatmap …","","","","","","","","","","","","",""
"Journal Article","Spreat S,Adams T,Barnes D,Caputo J,Diamond D,Hansen-Turton T,Jones D,Kolesk S","","COVID19 Prevalence and Antibody Seroprevalence Among Individuals with Intellectual Disability","sij","Social Innovations Journal","2020","3","","","COVID Tracking Project","","","","socialinnovationsjournal.com","","","","","2020-09-18","2021-04-02","","2692-2053","2692-2053","https://socialinnovationsjournal.com/index.php/sij/article/view/538;https://socialinnovationsjournal.com/index.php/sij/article/download/538/400","","","","","","Approximately 20% of the residential census at a population healthcare facility tested positive for COVID19 during the period from March 2020 through early June 2020. Individuals residing within the facility had intellectual disability, autism, and/or brain injury. Fifteen were hospitalized, but all subsequently were discharged. Two hospitalized clients died as a result of factors unrelated to COVID-19, aned a third was pronounced dead upon arrival at the Emergency room, again as a result of factors unrelated to COVID-19. individuals died as a result of factors unrelated to COVID-19. Approximately ¾ of the infected clients developed antibodies within 28 days of initial diagnosis. The development of antibodies could not be predicted from readily available demographic or medical variables","covid-19; antibodies;","","","","en","","","","","","","",""
"Journal Article","Breitzman A","","A Data Scientist Looks at Covid-19 Part I: Local and National Statistics","","","2020","","","","COVID Tracking Project","","","","rdw.rowan.edu","","","","","2020","","","","","https://rdw.rowan.edu/cgi/viewcontent.cgi?article=1177&context=csm_facpub","","","","","","… states and couldn't find what I wanted • So I found the raw data and created the following figures • State data is current as of 4pm 4/5/2020 • It comes from https:// covidtracking . com /about-tracker/ which is a site that aggregates data from each state health authority …","","","","","","","","","","","","",""
"Website","Hamersma S","","New York's recovery drove national COVID-19 case reduction","","","2020","","","","COVID Tracking Project","","","","lernercenter.syr.edu","","","","","2020","2021-04-02","","","","https://lernercenter.syr.edu/wp-content/uploads/2020/07/Hamersma_COVIDCaseTrends_Final.pdf","","","","","","… It is important for NY to remain vigilant as it continues through its various phases of reopening. Data Source: Data represent three-day averages of confirmed cases and come from the COVID Tracking Project ( covidtracking . com ) …","","","","","","","","","","","","",""
"Conference Paper","Cordier M","","Covid-19: quel développement soutenable pour demain?","","","2021","","","","COVID Tracking Project","","","","hal.archives-ouvertes.fr","","","Les vendredis de l'OVSQ","","2021","","","","","https://hal.archives-ouvertes.fr/hal-03131189/file/Covid-19_OVSQ%20v4.pdf","","","","","","… Hopkins University. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, the COVID Tracking Project (testing and hospitalizations), and city, county, state and national public health departments. Available …","","","","","","","","","","","","",""
"Journal Article","O'Hearn M,Liu J,Cudhea F,Micha R,Mozaffarian D","","Coronavirus Disease 2019 Hospitalizations Attributable to Cardiometabolic Conditions in the United States: A Comparative Risk Assessment Analysis","J. Am. Heart Assoc.","Journal of the American Heart Association","2021","10","5","e019259","COVID Tracking Project","","","","Am Heart Assoc","","","","","2021-02","","","2047-9980","","http://dx.doi.org/10.1161/JAHA.120.019259;https://www.ncbi.nlm.nih.gov/pubmed/33629868;https://www.ahajournals.org/doi/10.1161/JAHA.120.019259?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.ahajournals.org/doi/abs/10.1161/JAHA.120.019259;https://www.ahajournals.org/doi/pdf/10.1161/JAHA.120.019259","10.1161/JAHA.120.019259","33629868","","","","BACKGROUND Risk of coronavirus disease 2019 (COVID-19) hospitalization is robustly linked to cardiometabolic health. We estimated the absolute and proportional COVID-19 hospitalizations in US adults attributable to 4 major US cardiometabolic conditions, separately and jointly, and by race/ethnicity, age, and sex. METHODS AND RESULTS We used the best available estimates of independent associations of cardiometabolic conditions with a risk of COVID-19 hospitalization; nationally representative data on cardiometabolic conditions from the National Health and Nutrition Examination Survey 2015 to 2018; and US COVID-19 hospitalizations stratified by age, sex, and race/ethnicity from the Centers for Disease Control and Prevention's Coronavirus Disease 2019-Associated Hospitalization Surveillance Network database and from the COVID Tracking Project to estimate the numbers and proportions of COVID-19 hospitalizations attributable to diabetes mellitus, obesity, hypertension, and heart failure. Inputs were combined in a comparative risk assessment framework, with probabilistic sensitivity analyses and 1000 Monte Carlo simulations to jointly incorporate stratum-specific uncertainties in data inputs. As of November 18, 2020, an estimated 906 849 COVID-19 hospitalizations occurred in US adults. Of these, an estimated 20.5% (95% uncertainty interval [UIs], 18.9-22.1) of COVID-19 hospitalizations were attributable to diabetes mellitus, 30.2% (UI, 28.2-32.3) to total obesity (body mass index ≥30 kg/m2), 26.2% (UI, 24.3-28.3) to hypertension, and 11.7% (UI, 9.5-14.1) to heart failure. Considered jointly, 63.5% (UI, 61.6-65.4) or 575 419 (UI, 559 072-593 412) of COVID-19 hospitalizations were attributable to these 4 conditions. Large differences were seen in proportions of cardiometabolic risk-attributable COVID-19 hospitalizations by age and race/ethnicity, with smaller differences by sex. CONCLUSIONS A substantial proportion of US COVID-19 hospitalizations appear attributable to major cardiometabolic conditions. These results can help inform public health prevention strategies to reduce COVID-19 healthcare burdens.","COVID‐19; diabetes mellitus; heart failure; hypertension; obesity","","","Friedman School of Nutrition Science and Policy Tufts University Boston MA. Population Health Science and Policy Icahn School of Medicine Mount Sinai NY.","en","Research Article","","","","","","",""
"Journal Article","Noland RB","","Mobility and the effective reproduction rate of COVID-19","J Transp Health","Journal of transport & health","2021","20","","101016","COVID Tracking Project","","","","Elsevier","","","","","2021-03","","","2214-1405","","http://dx.doi.org/10.1016/j.jth.2021.101016;https://www.ncbi.nlm.nih.gov/pubmed/33542894;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843082;https://linkinghub.elsevier.com/retrieve/pii/S2214-1405(21)00010-4;https://www.sciencedirect.com/science/article/pii/S2214140521000104;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843082/","10.1016/j.jth.2021.101016","33542894","","","PMC7843082","Objectives: Due to the infectiousness of COVID-19, the mobility of individuals has sharply decreased, both in response to government policy and self-protection. This analysis seeks to understand how mobility reductions reduce the spread of the coronavirus (SAR-CoV-2), using readily available data sources. Methods: Mobility data from Google is correlated with estimates of the effective reproduction rate, R t , which is a measure of viral infectiousness (Google, 2020). The Google mobility data provides estimates of reductions in mobility, for six types of trips and activities. R t for US states are downloaded from an on-line platform that derives daily estimates based on data from the Covid Tracking Project (Wissel et al., 2020; Systrom et al., 2020). Fixed effects models are estimated relating mean R t and 80% upper level credible interval estimates to changes in mobility and a time-trend value and with both 7-day and 14-day lags. Results: All mobility variables are correlated with median R t and the upper level credible interval of R t . Staying at home is effective at reducing R t, . Time spent at parks has a small positive effect, while other activities all have larger positive effects. The time trend is negative suggesting increases in self-protective behavior. Predictions suggest that returning to baseline levels of activity for retail, transit, and workplaces, will increase R t above 1.0, but not for other activities. Mobility reductions of about 20-40% are needed to achieve an R t below 1.0 (for the upper level 80% credible interval) and even larger reductions to achieve an R t below 0.7. Conclusions: Policy makers need to be cautious with encouraging return to normal mobility behavior, especially returns to workplaces, transit, and retail locations. Activity at parks appears to not increase R t as much. This research also demonstrates the value of using on-line data sources to conduct rapid policy-relevant analysis of emerging issues.","COVID-19; Mobility; Social-distancing","","","Alan M. Voorhees Transportation Center, Edward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, NJ, 08901, United States.","en","Research Article","","","","","","",""
"Journal Article","Asgari Mehrabadi M,Dutt N,Rahmani AM","","The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis","JMIR Public Health Surveill","JMIR public health and surveillance","2021","","","","COVID Tracking Project","","","","europepmc.org","","","","","2021-03-09","","","2369-2960","","http://dx.doi.org/10.2196/22880;https://www.ncbi.nlm.nih.gov/pubmed/33690143;https://doi.org/10.2196/22880;https://europepmc.org/article/med/33690143","10.2196/22880","33690143","","","","BACKGROUND: The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regards to the increase of daily cases in the US. In particular, we are concerned with searches for dine-in restaurants and bars. Data were obtained from Google trends API and COVID tracking project. METHODS: To test causation of one time series on another, we used Granger's Causality Test. We considered the causation of two different search query trends related to dine-in restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. RESULTS: Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of .004 for Granger's causality test. CONCLUSIONS: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for states/territories with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes. CLINICALTRIAL:","","","","Department of Electrical Engineering and Computer Science, University of California Irvine, Berk Hall, 1st Fl., Irvine, US. Department of Computer Science, University of California Irvine, Irvine, US. School of Nursing, University of California Irvine, Irvine, US.","en","Research Article","","","","","","",""
"Website","Amin S,Peddu D,Majmudar G,Weber P","","[No title]","","","","","","","COVID Tracking Project","","","","","","","","","","2021-04-02","","","","https://www.researchgate.net/profile/Dhiraj-Peddu/publication/350043036_The_Effect_of_State_Mandate_Timing_on_COVID-19_Incidence_Rates/links/604d130f92851c2b23c9036c/The-Effect-of-State-Mandate-Timing-on-COVID-19-Incidence-Rates.pdf","","","","","","… Methods Raw incidence data were obtained from The COVID Tracking Project and state mandate information directly from state government websites … Health Affairs. 2020; 10.1377. doi:10.1377/hlthaff.2020.00818. 5. COVID Tracking Project - Covidtracking.com. 2020 …","","","","","","","","","","","","",""
"Journal Article","Zheng DX,Jella TK,Levoska MA,Ning AY,Cullison CR,Carroll BT,Scott JF","","Workforce geography of older dermatologists during the COVID-19 pandemic","Dermatol. Ther.","Dermatologic therapy","2021","","","e14917","COVID Tracking Project","","","","Wiley Online Library","","","","","2021","","","1396-0296","","https://onlinelibrary.wiley.com/doi/abs/10.1111/dth.14917?casa_token=k9oxdd09fMYAAAAA:QOe0rTD2vaZpNWrLmoaOuoidNJvqG-56UukJsu07k7nrcoAyyd4qoDamh31DuOtMO2_1tYkFW7AvuoIQ;https://onlinelibrary.wiley.com/doi/pdf/10.1111/dth.14917?casa_token=vPNT22P_550AAAAA:Oz_fsuazJ-BCCa3IaMLGv2Gf3y5yKKlgvZKbGVT1s9fhMKWwSioUHHrRHocq3MR5ltHsUk11m0TT_dB2","","","","","","… Data availability statement: The data that support the findings of this study are openly available in The COVID Tracking Project at https://covidtracking.com (reference number 1), AAMC State Physician Workforce Report at https://aamc.org/data-reports/workforce/data …","","","","","","","","","","","","",""
"Journal Article","Ridgway E,Pieper J,Juang P","","194: Prediction of ICU Utilization Associated With the COVID-19 Pandemic","Crit. Care Med.","Critical care medicine","2021","49","1","82","COVID Tracking Project","","","","journals.lww.com","","","","","2021-01","2021-04-02","","0090-3493","","https://journals.lww.com/ccmjournal/Fulltext/2021/01001/194__Prediction_of_ICU_Utilization_Associated_With.162.aspx;http://dx.doi.org/10.1097/01.ccm.0000726664.30866.70","10.1097/01.ccm.0000726664.30866.70","","","","","An abstract is unavailable.","","","","","","","","","","","","",""
"Journal Article","Chambers E","","The Green New Deal As Covid-19 Relief","","","2021","","","","COVID Tracking Project","","","","ir.library.illinoisstate.edu","","","","","2021","2021-04-02","","","","https://ir.library.illinoisstate.edu/urs2021geo/1/;https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1000&context=urs2021geo","","","","","","The COVID-19 pandemic has created or exacerbated concerns relating to unemployment, healthcare, and systemic inequities in the U.S. This comes at the same time as immense threats to human and environmental health posed by climate change. This poster outlines how it is possible and why it is necessary to address these intersecting crises simultaneously through a Green New Deal that implements solutions to the environmental crisis along with a myriad of other crises facing the U.S. Our nation needs a program to eliminate health disparities drawn out by the pandemic as well as to resolve high unemployment levels and remediate the deep inequalities seen in both the healthcare and employment systems. People of color and low-income individuals have been disproportionately affected by the pandemic. Higher rates of pre-existing health conditions among people of color coupled with COVID-19 have led to greater fatality rates within these groups (Villarosa, 2020). Further, Black and Latinx people as well as lowincome individuals are experiencing some of the worst unemployment rates during the pandemic (Parker et al., 2020). These systemic inequities do not begin and end with COVID-19. Any plan that seeks to move the U.S. out of the pandemic without also addressing underlying inequities will fail to prepare the country for future global crises. It is all the more pertinent to consider the nation’s approach to the pandemic when it is considered in conjunction with climate change. Scientists advise that we must cut global greenhouse gas emissions in half by 2030 and have net zero greenhouse gas emissions by 2050 to prevent massive losses in global ecosystems, plant and animal species, and human life (Klein, 2019, pp. 23-24). The Green New Deal presents a way to simultaneously address the COVID-19 pandemic and the climate change crisis. By developing legislation that addresses climate change, creates good quality jobs, provides universal health care, and remedies the systemic inequalities that have compounded the effects of COVID-19, the U.S. can come out of the pandemic having built a more sustainable and equitable world.","","","","","","","","","","","","",""
"Journal Article","Littlefield RS","","Controlling the Narrative","Communicating Science in Times of Crisis: COVID-19 Pandemic","","2021","","","358","COVID Tracking Project","","","","John Wiley & Sons","","","","","2021","","","","","https://books.google.com/books?hl=en&lr=&id=i-klEAAAQBAJ&oi=fnd&pg=PA358&dq=%22covid+tracking+project%22&ots=jpeETUCtpQ&sig=aXKMxVre0mrUCrgvBYcao4KM29k","","","","","","… Prevention (CDC), National Institutes of Health, National Institute of Allergy and Infectious Diseases, World Health Organization, American Public Health Association, Johns Hopkins University of Medicine's Coronavirus Resource Center, The COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Nowling W,Seeger MM","","Communicating Death and Dying in the COVID-19 Pandemic","Communicating Science in Times of Crisis: COVID-19 Pandemic","","2021","","","375","COVID Tracking Project","","","","John Wiley & Sons","","","","","2021","","","","","https://books.google.com/books?hl=en&lr=&id=i-klEAAAQBAJ&oi=fnd&pg=PA375&dq=%22covid+tracking+project%22&ots=jpeETUCtpQ&sig=mLdzvSihbwSuimPdCjqOZKcA_xE","","","","","","Page 388. 375 17 Communicating Death and Dying in the COVID-19 Pandemic William Nowling and Matthew M. Seeger Wayne State University Introduction The pre-crisis, onset, emergence, and expansion of the COVID-19 …","","","","","","","","","","","","",""
"Journal Article","Gaglioti AH,Li C,Douglas MD,Baltrus PT,Blount MA,Zahidi R,Caplan LS,Willock RJ,Fasuyi OB,Mack DH","","Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties","J. Public Health Manag. Pract.","Journal of public health management and practice: JPHMP","2021","27","3","268","COVID Tracking Project","","","","journals.lww.com","","","","","2021","2021-04-02","","1078-4659","","https://journals.lww.com/jphmp/Fulltext/2021/05000/Population_Level_Disparities_in_COVID_19_.10.aspx;http://dx.doi.org/10.1097/PHH.0000000000001354","10.1097/PHH.0000000000001354","","","","","19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. Design and Setting: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. Main Outcome Measures: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. Results: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp β = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp β = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). Conclusions: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19....","","","","","","","","","","","","",""
"Journal Article","Nguyen TH,Shah GH,Schwind JS,Richmond HL","","Community Characteristics and COVID-19 Outcomes: A Study of 159 Counties in Georgia, United States","J. Public Health Manag. Pract.","Journal of public health management and practice: JPHMP","2021","27","3","251-257","COVID Tracking Project","","","","journals.lww.com","","","","","2021","","","1078-4659","1550-5022","http://dx.doi.org/10.1097/PHH.0000000000001330;https://www.ncbi.nlm.nih.gov/pubmed/33762540;https://doi.org/10.1097/PHH.0000000000001330;https://journals.lww.com/jphmp/Fulltext/2021/05000/Community_Characteristics_and_COVID_19_Outcomes__A.8.aspx?context=LatestArticles","10.1097/PHH.0000000000001330","33762540","","","","BACKGROUND: The COVID-19 pandemic affects population groups differently, worsening existing social, economic, and health inequities. PURPOSE: This study examined 159 counties within Georgia to identify community characteristics associated with county-level COVID-19 case, hospitalization, and death rates. METHODS: Data from the 2020 County Health Rankings, the 2010 US Census, and the Georgia Department of Public Health COVID-19 Daily Status Report were linked using county Federal Information Processing Standard codes and evaluated through multivariable linear regression models. RESULTS: The percentages of children in poverty, severe housing problems, and people not proficient in the English language were significant predictors associated with increases in case, hospitalization, and death rates. Diabetic prevalence was significantly associated with increases in the hospitalization and death rates; in contrast, the percentages of people with excessive drinking and female were inversely associated with hospitalization and death rates. Other independent variables showing an association with death rate included the percentages of people reporting fair or poor health and American Indian/Alaska Native. IMPLICATION: Local authorities' proper allocation of resources and plans to address community social determinants of health are essential to mitigate disease transmission and reduce hospitalizations and deaths associated with COVID-19, especially among vulnerable groups.","","","","Interdisciplinary Health Sciences Department, College of Allied Health Sciences, Augusta University, Augusta, Georgia (Dr Nguyen); and Department of Biostatistics, Epidemiology, and Environmental Health Sciences (Drs Schwind and Richmond), Health Policy & Community Health Department, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia (Dr Shah).","en","Research Article","","","","","","",""
"Journal Article","Pierce HH","","An Ace Up Our Sleeves: The COVID-19 Vaccine Rollout Revealed Our Strengths and Our Neglected Public Health Infrastructure","J. Public Health Manag. Pract.","Journal of public health management and practice: JPHMP","2021","27","3","223-225","COVID Tracking Project","","","","journals.lww.com","","","","","2021","","","1078-4659","1550-5022","http://dx.doi.org/10.1097/PHH.0000000000001370;https://www.ncbi.nlm.nih.gov/pubmed/33762537;https://doi.org/10.1097/PHH.0000000000001370;https://journals.lww.com/jphmp/Fulltext/2021/05000/An_Ace_Up_Our_Sleeves__The_COVID_19_Vaccine.2.aspx?context=LatestArticles","10.1097/PHH.0000000000001370","33762537","","","","You may be trying to access this site from a secured browser on the server. Please enable scripts and reload this page. Log in Your account has been temporarily locked Your account has been temporarily locked due to incorrect …","","","","Association of American Medical Colleges, Washington, District of Columbia.","en","Research Article","","","","","","",""
"Journal Article","Ransome Y,Ojikutu BO,Buchanan M,Johnston D,Kawachi I","","Neighborhood Social Cohesion and Inequalities in COVID-19 Diagnosis Rates by Area-Level Black/African American Racial Composition","J. Urban Health","Journal of urban health: bulletin of the New York Academy of Medicine","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-03-23","","","1099-3460","1468-2869","http://dx.doi.org/10.1007/s11524-021-00532-3;https://www.ncbi.nlm.nih.gov/pubmed/33759068;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7986648;https://dx.doi.org/10.1007/s11524-021-00532-3;https://link.springer.com/article/10.1007/s11524-021-00532-3","10.1007/s11524-021-00532-3","33759068","","","PMC7986648","Geographic inequalities in COVID-19 diagnosis are now well documented. However, we do not sufficiently know whether inequalities are related to social characteristics of communities, such as collective engagement. We tested whether neighborhood social cohesion is associated with inequalities in COVID-19 diagnosis rate and the extent the association varies across neighborhood racial composition. We calculated COVID-19 diagnosis rates in Philadelphia, PA, per 10,000 general population across 46 ZIP codes, as of April 2020. Social cohesion measures were from the Southeastern Pennsylvania Household Health Survey, 2018. We estimated Poisson regressions to quantify associations between social cohesion and COVID-19 diagnosis rate, testing a multiplicative interaction with Black racial composition in the neighborhood, which we operationalize via a binary indicator of ZIP codes above vs. below the city-wide average (41%) Black population. Two social cohesion indicators were significantly associated with COVID-19 diagnosis. Associations varied across Black neighborhood racial composition (p <0.05 for the interaction test). In ZIP codes with ≥41% of Black people, higher collective engagement was associated with an 18% higher COVID-19 diagnosis rate (IRR=1.18, 95%CI=1.11, 1.26). In contrast, areas with <41% of Black people, higher engagement was associated with a 26% lower diagnosis rate (IRR=0.74, 95%CI=0.67, 0.82). Neighborhood social cohesion is associated with both higher and lower COVID-19 diagnosis rates, and the extent of associations varies across Black neighborhood racial composition. We recommend some strategies for reducing inequalities based on the segmentation model within the social cohesion and public health intervention framework.","Black/African American; COVID-19; Ecological; Inequality; Racial disparities; Social capital; Social cohesion; Spatial; Structural determinants","","","Department of Social and Behavioral Sciences, Yale School of Public Health, 60 College Street, LEPH 4th Floor, New Haven, CT, 06510, USA. yusuf.ransome@yale.edu. Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, USA. Infectious Disease Division, Massachusetts General Hospital, Boston, MA, USA. Division of Global Health Equity, Harvard Medical School, Boston, MA, USA. Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA. Department of Social and Behavioral Sciences, Yale School of Public Health, 60 College Street, LEPH 4th Floor, New Haven, CT, 06510, USA. Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Sahneh FD,Fries W,Watkins JC,Lega J","","The COVID-19 Pandemic from the Eye of the Virus","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-23","","","","","http://arxiv.org/abs/2103.12848","","","2103.12848","","","The all-pervasive lens that humans ordinarily use to watch and analyze the pandemic is time. This article considers an alternative. Instead of tracking incidence as a function of time, new cases are counted as a function of cumulative cases. This resource-centric perspective, which is more natural and physically justified, is the perspective of the virus. In this article, we demonstrate the relevance of this approach by characterizing an outbreak as an independent increments Gaussian process that fluctuates about a deterministic curve, called the incidence-cumulative cases (ICC) curve. We illustrate these concepts on Influenza A and COVID-19 outbreaks in the US. The novel perspective presented here reveals universal properties of disease spread that would otherwise remain hidden.","","","","","","","","arXiv","2103.12848","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Brauer S","","Rethinking Our COVID-19 Strategy","precisionnanomedicine.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://precisionnanomedicine.com/article/21569.pdf","","","","","","Page 1. Prnano.com, https://doi.org/10.33218/001c.21569 Andover House, Andover, MA USA The official Journal of CLINAM – ISSN:2639-9431 (online) License: CC BY-NC-SA 4.0 738 Open Access Opinion article Precis. Nanomed. 2021 April;4(1):738-749 …","","","","","","","","","","","","",""
"Journal Article","Miller SA,Grubert E","","US industrial sector decoupling of energy use and greenhouse gas emissions under COVID: durability and decarbonization","Environ. Res. Commun.","Environmental Research Communications","2021","3","3","031003","COVID Tracking Project","","","","IOP Publishing","","","","","2021-03-31","2021-04-02","","2515-7620","","https://iopscience.iop.org/article/10.1088/2515-7620/abf0f2/meta;https://iopscience.iop.org/article/10.1088/2515-7620/abf0f2;https://iopscience.iop.org/article/10.1088/2515-7620/abf0f2/pdf;http://dx.doi.org/10.1088/2515-7620/abf0f2","10.1088/2515-7620/abf0f2","","","","","The 2020 response to the coronavirus pandemic has had a profound and rapid effect on social behavior, the economy, and consumption. Associated declines in greenhouse gas (GHG) emissions have prompted calls to action to use the pandemic experience to accelerate decarbonization. Such action depends on understanding how GHG emissions reductions were achieved and whether they can be sustained. In this work, we focus on the industrial sector by comparing United States (US) industrial energy consumption, CO2 emissions, and key materials production between the first two quarters (Q1 and Q2) of 2020, when pandemic response became active, relative to 2019. We show a striking decoupling between energy use and GHG emissions in the US industrial sector between Q2 2020 and Q2 2019, yet pandemic decarbonization in the industrial sector is unlikely to be durable. Observations suggest three major takeaways for US industrial decarbonization: (1) efforts to decarbonize transportation will contribute to industrial decarbonization due to the large impacts of petroleum refining; (2) increasing demands for materials that use energy resources as feedstocks (e.g., plastics) can result in an apparent decoupling in energy demand and GHG emissions that is not indicative of a durable pathway for reducing GHG emissions; and (3) temporary reduction in demand for industrial infrastructure materials would have resulted in greater reductions of GHG emissions than the relative change in fuels used during this period. Cumulatively, while shifts that would lower GHG emissions occurred, no substantial structural changes to industrial activity were observed. As such, society still requires systemic change to interdependencies on other sectors and the methods we use to produce and deploy our industrial materials.","","","","","en","","","","","","","",""
"Journal Article","über die erneuerbaren Energien DW,Pusztai A,täglich Gift U,gehört das Wasser WW,des Wassers G","","Archiv der Kategorie:„künstliche Intelligenz “","keinblattvormmund13.wordpress","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://keinblattvormmund13.wordpress.com/category/kuenstliche-intelligenz/","","","","","","Beiträge über „künstliche Intelligenz“ von isodora13.","","","","","","","","","","","","",""
"Journal Article","über die erneuerbaren Energien DW,Pusztai A,täglich Gift U,gehört das Wasser WW,des Wassers G","","Schlagwort-Archive: Stiftung Corona Ausschuss","keinblattvormmund13.wordpress","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://keinblattvormmund13.wordpress.com/tag/stiftung-corona-ausschuss/","","","","","","Beiträge über Stiftung Corona Ausschuss von isodora13.","","","","","","","","","","","","",""
"Journal Article","Levere M,Rowan P,Wysocki A","","The adverse effects of the COVID-19 pandemic on nursing home resident well-being","J. Am. Med. Dir. Assoc.","Journal of the American Medical Directors Association","2021","","","","COVID Tracking Project","","","","Elsevier","","","","","2021-03-20","","","1525-8610","","https://www.sciencedirect.com/science/article/pii/S1525861021003066;http://dx.doi.org/10.1016/j.jamda.2021.03.010;https://www.jamda.com/article/S1525-8610(21)00306-6/pdf","10.1016/j.jamda.2021.03.010","","","","","Objective Quantify the effects of the COVID-19 pandemic on nursing home resident well-being. Design Quantitative analysis of resident-level assessment data Setting and participants Long-stay residents living in Connecticut nursing homes Methods We used Minimum Data Set assessments to measure nursing home resident outcomes observed in each week between March and July 2020 for long-stay residents (e.g., those in the nursing home for at least 100 days) who lived in a nursing home at the beginning of the pandemic. We compared outcomes to those observed at the beginning of the pandemic, controlling for both resident characteristics and patterns for outcomes observed in 2017 to 2019. Results We found that nursing home resident outcomes worsened on a broad array of measures. The prevalence of depressive symptoms increased by 6 percentage points relative to before the pandemic in the beginning of March—representing a 15 percent increase. The share of residents with unplanned substantial weight loss also increased by 6 percentage points relative to the beginning of March—representing a 150 percent increase. We also found significant increases in episodes of incontinence (4 percentage points) and significant reductions in cognitive functioning. Our findings suggest that loneliness and isolation play an important role. Though unplanned substantial weight loss was greatest for those who contracted COVID-19 (about 10 percent of residents observed in each week), residents who did not contract COVID-19 also physically deteriorated (about 7.5 percent of residents in each week). Conclusions and implications These analyses show that the pandemic had substantial impacts on nursing home residents beyond what can be quantified by cases and deaths, adversely affecting the physical and emotional well-being of residents. Future policy changes to limit the spread of COVID-19 or other infectious disease outbreaks should consider any additional costs beyond the direct effects of morbidity and mortality due to COVID-19.","COVID-19; nursing home residents; well-being; pandemic","","","","","","","","","","","",""
"Journal Article","Moore WJ,Webb A,Morrisette T,Sullivan LK,Alosaimy S,Hossain S,Howe Z,Vlashyn OO,Paloucek FP,Rybak MJ,Wang S","","Impact of COVID-19 pandemic on training of pharmacy residents and fellows: Results from a national survey of postgraduate pharmacy trainees","Am. J. Health. Syst. Pharm.","American journal of health-system pharmacy: AJHP: official journal of the American Society of Health-System Pharmacists","2021","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2021-03-19","","","1079-2082","1535-2900","http://dx.doi.org/10.1093/ajhp/zxab114;https://www.ncbi.nlm.nih.gov/pubmed/33740818;https://academic.oup.com/ajhp/article-lookup/doi/10.1093/ajhp/zxab114;https://academic.oup.com/ajhp/advance-article-abstract/doi/10.1093/ajhp/zxab114/6178956;https://academic.oup.com/ajhp/advance-article-pdf/doi/10.1093/ajhp/zxab114/36646420/zxab114.pdf?casa_token=KIE39WBuvOgAAAAA:MBaHPy-56PGcNN29BXL5Su9PnGRVeL1QpqT-kJJIQonbxMHxbdnVM4mbIqmw3U3iN7X4V8R9inWP3g","10.1093/ajhp/zxab114","33740818","","","","PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has impacted the activities of healthcare workers, including postgraduate pharmacy trainees. Quality training experiences must be maintained to produce competent pharmacy practitioners and maintain program standards. METHODS: A cross-sectional survey of postgraduate pharmacy trainees in the United States was conducted to evaluate training experience changes and assess perceived impacts on residents and fellows following the COVID-19 pandemic's onset. RESULTS: From June 4 through June 22, 2020, 511 pharmacy trainees in 46 states completed the survey. Participants' median age was 26 (interquartile range [IQR], 25-28) years, with included responses from postgraduate year 1 residents (54% of sample), postgraduate year 2 residents (40%), and postgraduate fellows (6%). Compared to experiences prior to the onset of the COVID-19 pandemic, fewer trainees conducted direct patient care (38.5% vs 91.4%, P < 0.001), more worked from home (31.7% vs 1.6%, P < 0.001), and less time was spent with preceptors per day (2 [IQR, 2-6] hours vs 4 [IQR, 1-4] hours, P < 0.001). Sixty-five percent of respondents reported experiencing changes in their training program, 39% reported being asked to work in areas outside of their routine training experience, and 89% stated their training shifted to focus on COVID-19 to some degree. Most respondents perceived either major (9.6%) or minor (52.0%) worsening in quality of experience, with major and minor improvement in quality of experience reported by 5.5% and 8.4% of respondents, respectively. CONCLUSION: Pharmacy resident/fellow experiences were perceived to have been extensively impacted by the COVID-19 pandemic in varying ways. Our findings describe shifts in postgraduate training and may aid in the development of best practices for optimizing trainee experiences in future crises.","COVID-19; pharmacy education; pharmacy fellowship; pharmacy residency training; postgraduate training","","","Department of Pharmacy, Northwestern Medicine, Chicago, IL, USA. Department of Pharmacy, Oregon Health & Science University, Portland OR, USA. Anti-Infective Research Laboratory, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI, USA. Department of Pharmacy, Arizona Burn Center - Valleywise Health, Phoenix, AZ, USA. Department of Pharmacy, Dana-Farber Cancer Institute, Boston, MA, USA. Department of Pharmacy, Indiana University Health AHC, Indianapolis, IN, USA. Department of Pharmacy, Ohio State University Wexner Medical Center, Columbus, OH, USA. Department of Pharmacy Practice, University of Illinois-Chicago College of Pharmacy, Chicago, IL, USA. Department of Pharmacy Practice, Midwestern University Chicago College of Pharmacy, Downers Grove, IL, and Department of Pharmacy, Northwestern Medicine, Downers Grove, IL, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Sun LG,Daniels B,Spencer DM,Sloan C,Blades N,Gomez T","","Disaster Vulnerability in 3D","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-19","2021-04-02","","","","https://papers.ssrn.com/abstract=3807674;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3807674;http://dx.doi.org/10.2139/ssrn.3807674;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3807674","10.2139/ssrn.3807674","","","","","From the beginning of the COVID-19 pandemic, we have been told that different people face different risks from the virus. This laser focus on disaster vulnerability is, given the history of disasters, quite unusual. Usually vulnerability is the “silent killer”—something we notice only after the disaster is over and the body count and other impacts become clear. This focus on vulnerability, along with the scope and timescale of the pandemic, provides a unique vantage point from which to view disaster vulnerability. This Article leverages this unique vantage point to consider vulnerability in a more nuanced way and to illuminate how a data-driven approach to vulnerability could improve disaster policy during the pandemic and other disasters. Drawing on new empirical data, as well as experience in past disasters, we explore three dimensions of vulnerability and their implications for policymakers. First, using statistical analysis and GIS mapping, our team of public health, statistics, and legal experts develops and presents an empirical tool, a COVID-19 Vulnerability Index, to look at the geography of vulnerability—the physical distribution of people across the United States who are particularly vulnerable to the novel coronavirus, including the elderly, racial minorities, frontline workers, and those with underlying health conditions. We then demonstrate how this vulnerability index could have been used to inform two critical and contentious policy decisions that occupied decision-makers from the onset of the pandemic: mask mandates and voter accommodations during the 2020 elections.Incorporating insights from our exploration of the geographic dimension of vulnerability and lessons of past disasters, we then explore a second dimension of disaster vulnerability: competing or conflicting vulnerabilities, or situations in which policymakers must navigate choices that require prioritizing one aspect of a group’s vulnerability over another or one vulnerable group over another. To do this we consider two other important problems that have faced policymakers during the pandemic: school closures and vaccine distribution.Finally, we explore a third dimension of vulnerability: political vulnerability. This dimension of vulnerability encompasses a variety of ways that disasters make already vulnerable groups even more vulnerable to certain kinds of harms, including political neglect, stigmatization, disenfranchisement, displacement, and other forms of exploitation. In particular, we consider how vulnerability data may be both an unintended roadmap for exploitation and an important check on disaster inequity. Throughout, we make the case that we cannot truly see—and address—disaster vulnerability if we focus only on geographic vulnerability; seeing vulnerability in three dimensions requires accounting for competing and conflicting vulnerabilities and political vulnerabilities, as well.","disaster, vulnerability, social vulnerability, COVID-19","","","","","","","","","","","","Available at SSRN"
"Preprint Manuscript","Rossello NB,Pezzutto M,Castagliuolo I,Schenato L,Garone E","","Testing more and earlier = better control of the epidemic and lower social costs","","","2021","","","","COVID Tracking Project","","Research Square","","","","","","","2021-03-17","2021-04-02","","","","https://www.researchsquare.com/article/rs-316585/latest.pdf;https://www.researchsquare.com/article/rs-316585/v1;http://dx.doi.org/10.21203/rs.3.rs-316585/v1","10.21203/rs.3.rs-316585/v1","","","","","Abstract In this note we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. Surprisingly, the results not only confirm that increasing the number of tests lowers the number of infected individuals, but also that it has a very beneficial effect limiting the number of quarantined individuals, and thus the socio-economical costs of the epidemics. The results also show that the timing in the application of the measures is as important as the measures themselves. The results suggest that fast decision making and investments to increase testing capabilities are highly rewarded not only from the public health viewpoint, but also from the socio-economical one. The study is carried out in simulation using stochastic cellular automata representing a community of 50'000 individuals. The selection of the tested individuals is carried out based on a contact tracing strategy focused on the closer contacts.","","","","Universite Libre de Bruxelles Ecole polytechnique de Bruxelles; University of Padova: Universita degli Studi di Padova; University of Padova Department of Molecular Medicine: Universita degli Studi di Padova Dipartimento di Medicina Molecolare; Université Libre de Bruxelles École polytechnique de Bruxelles: Universite Libre de Bruxelles Ecole polytechnique de Bruxelles","","","","","","","","","Research Square"
"Journal Article","Hill J,Rodriguez DX,McDaniel PN","","Immigration Status as a Health Care Barrier in the USA during COVID-19","J Migr Health","Journal of migration and health","2021","","","100036","COVID Tracking Project","","","","Elsevier","","","","","2021-03-20","","","2666-6235","","http://dx.doi.org/10.1016/j.jmh.2021.100036;https://www.ncbi.nlm.nih.gov/pubmed/33778797;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979269;https://linkinghub.elsevier.com/retrieve/pii/S2666-6235(21)00003-9;https://www.sciencedirect.com/science/article/pii/S2666623521000039","10.1016/j.jmh.2021.100036","33778797","","","PMC7979269","In the context of the United States of America (U.S.), COVID-19 has influenced migrant experiences in a variety of ways, including the government's use of public health orders to prevent migration into the country and the risk of immigrants contracting COVID-19 while in detention centers. However, this paper focuses on barriers that immigrants of diverse statuses already living in the U.S.-along with their families-may face in accessing health services during the pandemic, as well as implications of these barriers for COVID-19 prevention and response efforts . We report findings from a scoping review about immigration status as a social determinant of health and discuss ways that immigration status can impede access to health care across levels of the social ecology . We then explore how recent changes to federal immigration policies and current COVID-19 federal relief efforts may serve to create additional barriers to health care for immigrants and their families. Improving health care access for immigrant populations in the U.S. will require interventions at all levels of the social ecology and across vari ous social determinants of health, both in response to COVID-19 and to strengthen health systems more broadly.","COVID-19; health care access; health policy; immigrant health; social determinants of health; social ecological model","","","Doctoral Candidate, International Conflict Management, School of Conflict Management, Peacebuilding, and Development, College of Humanities and Social Sciences, Kennesaw State University. Associate Professor of Social Work and Human Services, Wellstar College of Health and Human Services, Kennesaw State University. Associate Professor of Geography, College of Humanities and Social Sciences, Kennesaw State University.","en","Research Article","","","","","","",""
"Journal Article","Macias Gil R,Touzard-Romo F,Sanchez MC,Pandita A,Kalligeros M,Mylona EK,Shehadeh F,Mylonakis E","","Characteristics and outcomes of Hispanic/Latinx patients with coronavirus disease 19 (COVID-19) requiring hospitalization in Rhode Island: a retrospective cohort study","Ann. Epidemiol.","Annals of epidemiology","2021","58","","64-68","COVID Tracking Project","","","","Elsevier","","","","","2021-03-16","","","1047-2797","1873-2585","http://dx.doi.org/10.1016/j.annepidem.2021.03.003;https://www.ncbi.nlm.nih.gov/pubmed/33737227;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962584;https://linkinghub.elsevier.com/retrieve/pii/S1047-2797(21)00040-5;https://www.sciencedirect.com/science/article/pii/S1047279721000405;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962584/","10.1016/j.annepidem.2021.03.003","33737227","","","PMC7962584","OBJECTIVE: Explore potential racial/ethnic differences, describe general clinical characteristic, and severe outcomes (intensive care unit [ICU] admission, mechanical ventilation [intubation], and death) between Hispanic/Latinx (hereafter: Hispanics or Latinx community) and non-Hispanic patients hospitalized with COVID-19. METHODS: Retrospective cohort of 326 patients hospitalized with COVID-19 through April 19, 2020. Sociodemographic and hospital course data were collected and analyzed. A multivariate logistic regression analysis was implemented to examine associations. RESULTS: Compared with non-Hispanic Whites (NHW), Hispanics were younger (53 years, median age) and had higher rates of Medicaid and less commercial/HMO/PPO coverage (P < .001). Similarly, in the age sub-grouped multivariate analysis for outcomes, Hispanics ≥65-year-old were 2.66 times more likely to be admitted to ICU (95% CI: 1.07-6.61; P = .03), and 3.67 times more likely to get intubated (95% CI: 1.29-10.36; P = .01). CONCLUSIONS: Hospitalized Hispanic patients of ≥65-year-old with COVID-19 were more likely to have higher risk of more severe outcomes (ICU admission and intubation) compared with NHW. Hispanic patient's social determinants of health and underlying medical conditions may explain the heightened risk for severe outcomes. Further studies are necessary to more accurately identify and address health disparities in Hispanics and other vulnerable populations amidst COVID-19 and future pandemics.","Covid-19; Health disparity; Hospitalization, Hispanic; Latinx; Sars-cov-2","","","Department of Infectious Diseases, Kaiser Permanente Northern California, Vallejo, CA. Division of Infectious Diseases, Alpert Medical School of Brown University, Providence, RI. Department of Medicine, University of Colorado- Anschutz Medical Campus. Division of Infectious Diseases, Alpert Medical School of Brown University, Providence, RI. Electronic address: emylonakis@lifespan.org.","en","Research Article","","","","","","",""
"Journal Article","Goldman J,Osinusi A,Marty FM","","Home→ Covid Clinical Studies","texascovid19.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://texascovid19.com/racial-disproportionality-in-covid-clinical-trials/?include_category=covid-19","","","","","","… in the SIMPLE trials (49%) with representative US populations from the Coronavirus Disease 2019 (COVID-19)–Associated Hospitalization Surveillance Network (COVID-NET) of the Centers for Disease Control and Prevention4 and from the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Ledsam J,Arik S,Shor J,Sinha R,Yoon J,Le L,Dusenberry M,Yoder N,Popendorf K,Epshteyn A,Others","","A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan","","","2021","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2021","","","","","https://www.researchsquare.com/article/rs-312419/latest.pdf","","","","","","Page 1. A prospective evaluation of AI-augmented 1 epidemiology to forecast COVID-19 in the USA and 2 Japan 3 Sercan ¨O. Arık1,*,+, Joel Shor2,+, Rajarishi Sinha1,+, Jinsung Yoon1,+, Joseph R. 4 Ledsam2,+, Long T. Le1 …","","","","","","","","","","","","",""
"Preprint Manuscript","Knauer NJ","","The Federal Response to the COVID-19 Pandemic - A Study in Maladministration","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-13","2021-04-02","","","","https://papers.ssrn.com/abstract=3803884;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3803884;http://dx.doi.org/10.2139/ssrn.3803884;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3803884","10.2139/ssrn.3803884","","","","","When the first suspected human-to-human transmission of the novel coronavirus was reported in January 2020, the United States had in place an elaborate set of pandemic disaster and response plans that spanned hundreds of pages. The George W. Bush administration spearheaded national pandemic planning in 2005 as part of the post-September 11 efforts to modernize the country’s disaster response capabilities. Subsequent administrations revisited and revised the various pandemic plans, including the Trump administration as recently as 2017 and 2018. Despite these detailed plans, the Trump administration was slow to respond to the emerging public health crisis or implement any of the prescribed protocols. Federal officials lost valuable time as they downplayed the risk posed by COVID-19 and repeatedly assured the American people that the virus would simply “go away.” By March 2020, a frightening spike in cases in the Northeast made the pandemic impossible to ignore. President Trump and other administration officials shifted tactics and began to characterize COVID-19 as the quintessential “black swan” – a threat that no one could have foreseen. President Trump repeatedly told the American people that “no one could have predicted something like this” even though official federal policy suggested a very different story. Far from being a black swan, the COVID-19 pandemic was widely anticipated and, according to many epidemiologists, inevitable.This article argues that our botched federal response was not so much a failure of policy per se, but rather a failure of political will. The federal government had a robust pandemic policy in place; it simply chose not to follow it. This failure of political will illustrates the dangers that arise when public health measures are politicized and weaponized for partisan advantage and demands strong interventions to ensure federal accountability and transparency. The first section of this article outlines the evolution of our national pandemic plans within the broader context of disaster and response planning. The second section explains the pandemic staging framework that is used to organize and coordinate decision making within a pandemic. The third section charts the federal response during the crucial first three months of the public health crisis, specifically identifying instances where the federal government failed to follow its own clearly articulated pandemic policy. The final section outlines some lessons learned from the pandemic and proposes reforms to insulate public health measures from partisan wrangling and keep our federal government faithful to its foremost obligation, namely to promote the general welfare","pandemic, COVID-19, coronavirus, disaster planning, preparedness and response, federalism, public health","","","","","","","","","","","","Available at SSRN 3803884"
"Journal Article","Huber K,Andrea Thoumi MPP,Silcox C,Tewarson H,McClellan M","","Elaine FH Chhean, MPH Research Associate Duke-Margolis Center for Health Policy","","","2021","","","","COVID Tracking Project","","","","rockefellerfoundation.org","","","","","2021","","","","","https://www.rockefellerfoundation.org/wp-content/uploads/2021/03/State-and-Local-Testing-Strategies-for-Responding-to-Covid-19-Outbreaks-in-Communities-Considerations-for-Equitable-Distribution-1.pdf","","","","","","Page 1. State and Local Testing Strategies for Responding to Covid-19 Outbreaks in Communities: Considerations for Equitable Distribution March 15, 2021 Funded by Page 2. 2 STATE AND LOCAL TESTING STRATEGIES …","","","","","","","","","","","","",""
"Journal Article","Lanius C,Weber R,MacKenzie Jr WI","","Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey","Soc Netw Anal Min","Social network analysis and mining","2021","11","1","32","COVID Tracking Project","","","","Springer","","","","","2021-03-12","","","1869-5450","","http://dx.doi.org/10.1007/s13278-021-00739-x;https://www.ncbi.nlm.nih.gov/pubmed/33747252;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954364;https://link.springer.com/article/10.1007/s13278-021-00739-x","10.1007/s13278-021-00739-x","33747252","","","PMC7954364","The COVID-19 infodemic is driven partially by Twitter bots. Flagging bot accounts and the misinformation they share could provide one strategy for preventing the spread of false information online. This article reports on an experiment (N = 299) conducted with participants in the USA to see whether flagging tweets as coming from bot accounts and as containing misinformation can lower participants' self-reported engagement and attitudes about the tweets. This experiment also showed participants tweets that aligned with their previously held beliefs to determine how flags affect their overall opinions. Results showed that flagging tweets lowered participants' attitudes about them, though this effect was less pronounced in participants who frequently used social media or consumed more news, especially from Facebook or Fox News. Some participants also changed their opinions after seeing the flagged tweets. The results suggest that social media companies can flag suspicious or inaccurate content as a way to fight misinformation. Flagging could be built into future automated fact-checking systems and other misinformation abatement strategies of the social network analysis and mining community.","COVID-19; Fact-checking; Misinformation; Survey study; Twitter","","","University of Alabama in Huntsville, Huntsville, AL USA.","en","Research Article","","","","","","",""
"Journal Article","Palatella L,Vanni F,Lambert D","","A phenomenological estimate of the true scale of CoViD-19 from primary data","Chaos Solitons Fractals","Chaos, Solitons & Fractals","2021","146","","110854","COVID Tracking Project","","","","Elsevier","","","","","2021-05-01","","","0960-0779","","https://www.sciencedirect.com/science/article/pii/S0960077921002071;http://dx.doi.org/10.1016/j.chaos.2021.110854;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7955922/;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7955922","10.1016/j.chaos.2021.110854","","","","pmc7955922","Estimation of the prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of CoViD-19, and for implementing effective public policy intervention strategies. We discuss a simple yet effective approach to estimate the true number of people infected by SARS-CoV-2, using raw epidemiological data reported by official health institutions in the largest EU countries and the USA.","Renewal equation; Scale of epidemics; SARS-CoV2 prevalence","","","","","","","","","","","",""
"Journal Article","Zalla LC,Martin CL,Edwards JK,Gartner DR,Noppert GA","","A Geography of Risk: Structural Racism and COVID-19 Mortality in the United States","Am. J. Epidemiol.","American journal of epidemiology","2021","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2021-03-12","","","0002-9262","1476-6256","http://dx.doi.org/10.1093/aje/kwab059;https://www.ncbi.nlm.nih.gov/pubmed/33710272;https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/kwab059;https://academic.oup.com/aje/advance-article-abstract/doi/10.1093/aje/kwab059/6168675;https://academic.oup.com/aje/advance-article-pdf/doi/10.1093/aje/kwab059/36583521/kwab059.pdf?casa_token=rpLMLgYcBB0AAAAA:3xHMNGfBtb4Ull1XE2vWJ41mvbqUkdFCHWhN6nE5ztIsiE0Rpks7yvXFDaNwGP5vRU9Zn1C-PQ6pSQ","10.1093/aje/kwab059","33710272","","","","Coronavirus disease 2019 (COVID-19) is disproportionately burdening racial and ethnic minority groups in the US. Higher risks of infection and mortality among racialized minorities are a consequence of structural racism, reflected in specific policies that date back centuries and persist today. Yet, our surveillance activities do not reflect what we know about how racism structures risk. When measuring racial and ethnic disparities in deaths due to COVID-19, the CDC statistically accounts for the geographic distribution of deaths throughout the US to reflect the fact that deaths are concentrated in areas with different racial and ethnic distributions than that of the larger US. In this commentary, we argue that such an approach misses an important driver of disparities in COVID-19 mortality, namely the historical forces that determine where individuals live, work, and play, and consequently determine their risk of dying from COVID-19. We explain why controlling for geography downplays the disproportionate burden of COVID-19 on racialized minority groups in the US. Finally, we offer recommendations for the analysis of surveillance data to estimate racial disparities, including shifting from distribution-based to risk-based measures, to help inform a more effective and equitable public health response to the pandemic.","COVID-19; health status disparities; mortality; public health surveillance; residential segregation; structural racism","","","Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. Department of Epidemiology & Biostatistics, College of Human Medicine, Michigan State University, East Lansing, Michigan.","en","Research Article","","","","","","",""
"Website","Wohlauer M,Etkin RY,Eidt JF","","UpToDate","","","","","","","COVID Tracking Project","","","","","","","","","","2021-04-02","","","","https://www.uptodate.com/contents/covid-19-acute-limb-ischemia","","","","","","{{configCtrl2.info.metaDescription}}","","","","","","","","","","","","",""
"Journal Article","Mann C,Palim M,Jebaraj M","","2021 Arkansas Business Forecast","","","2021","","","","COVID Tracking Project","","","","scholarworks.uark.edu","","","","","2021","2021-04-02","","","","https://scholarworks.uark.edu/cberpub/34/;https://scholarworks.uark.edu/cgi/viewcontent.cgi?article=1033&context=cberpub","","","","","","“The annual Business Forecast is a chance for business and community leaders from Northwest Arkansas, the state and the region to get first-hand insight into the direction for the next year from top economists,” said Matt Waller, dean of the Walton College. “The insights provided by these three experts will inform and shape decisions that help to drive the business community in Arkansas in 2021.” Waller said those valuable insights and networking opportunities are only possible through the continued strong support of event sponsors. “Each year, the interest and level of participation in the Business Forecast event continues to grow and expand,” Jebaraj said. “Hearing economists talk on the condition of the economy in these tough times and on areas of strength and resilience will help everyone better understand where we are headed in 2021.”","","","","","","","Publications and Presentations","","","","","",""
"Journal Article","Michaels D","","Clearing the Air: Science-Based Strategies to Protect Workers from COVID-19 Infections","","","2021","","","","COVID Tracking Project","","","","edlabor.house.gov","","","","","2021","","","","","https://edlabor.house.gov/imo/media/doc/MichaelsDavidTestimony03112021.pdf","","","","","","Page 1. Testimony of Professor David Michaels The George Washington University Hearing Before the United States House of Representatives Committee on Education and Labor Subcommittee on Workforce Protections Clearing …","","","","","","","","","","","","",""
"Journal Article","Ξυπολιά Γ","","Αξιοποίηση Εργαλείων και Εφαρμογών Τεχνητής Νοημοσύνης στη Διαχείριση και Αντιμετώπιση της Πανδημίας COVID-19","","","2021","","","","COVID Tracking Project","","","","artemis.cslab.ece.ntua.gr","","","","","2021-03-09","2021-04-02","","","","http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/17876;http://artemis.cslab.ece.ntua.gr:8080/jspui/bitstream/123456789/17876/1/%CE%94%CE%99%CE%A0%CE%9B%CE%A9%CE%9C%CE%91%CE%A4%CE%99%CE%9A%CE%97_%CE%95%CE%A1%CE%93%CE%91%CE%A3%CE%99%CE%91_%CE%9E%CE%A5%CE%A0%CE%9F%CE%9B%CE%99%CE%91_%CE%93%CE%95%CE%A9%CE%A1%CE%93%CE%99%CE%91.pdf","","","","","","Η πανδημία COVID-19, για την οποία είναι υπεύθυνος ο νέος κορωνοϊός SARS-CoV-2, έχει οδηγήσει στην πιο σοβαρή υγειονομική κρίση των τελευταίων 100 ετών, προκαλώντας έναν τρομακτικό και συνεχώς αυξανόμενο αριθμό θανάτων, μία πρωτόγνωρη πίεση στα συστήματα υγείας και τεράστιες οικονομικές και κοινωνικές συνέπειες παγκοσμίως. Από την εμφάνιση του νέου ιού μέχρι και σήμερα, μια πληθώρα ερευνητών, οργανισμών και εταιρειών έχουν επιδοθεί σε έναν αγώνα δημιουργίας εφαρμογών που χρησιμοποιούν εργαλεία και μεθόδους τεχνητής νοημοσύνης, μηχανικής μάθησης και ανάλυσης μεγάλων δεδομένων, σε μια προσπάθεια να συνεισφέρουν στην αντιμετώπιση διαφορετικών πτυχών της πανδημίας. Μέσω της Διπλωματικής αυτής Εργασίας, επιχειρείται μια ανασκόπηση των υπαρχουσών εφαρμογών στις εξής τέσσερις περιοχές: την Επιδημιολογία, την Κλινική Ιατρική, τη Φαρμακολογία και την Επιδημία της Πληροφορίας (Infodemic). Αρχικά, στον τομέα την Επιδημιολογίας, μελετάμε εφαρμογές που βοηθούν στην έγκαιρη ανίχνευση και προειδοποίηση, την αυτοματοποιημένη ιχνηλάτηση επαφών, την πρόβλεψη και την προσομοίωση της εξάπλωσης, την ομαδοποίηση γεωγραφικών περιοχών με βάση κοινά χαρακτηριστικά και την αξιολόγηση κινδύνου ανά περιοχή. Στην περιοχή της Κλινικής Ιατρικής, παρουσιάζουμε μεθόδους διάγνωσης της ασθένειας με χρήση ιατρικής απεικόνισης και άλλων εναλλακτικών πηγών πληροφορίας, πρόγνωσης της πορείας των ασθενών, καθώς και βέλτιστης διαχείρισης των υφιστάμενων πόρων και ανάπτυξης εξατομικευμένων θεραπειών. Σε ό,τι αφορά τη Φαρμακολογία, περιγράφουμε ενδιαφέρουσες εφαρμογές για την πρόβλεψη της τρισδιάστατης δομής των πρωτεϊνών, την ανακάλυψη θεραπειών και πιθανών στόχων εμβολίων, καθώς και εφαρμογές για τη βελτίωση των κλινικών δοκιμών. Τέλος, ορίζουμε την έννοια της Επιδημίας της Πληροφορίας και παρουσιάζουμε λύσεις που στοχεύουν στην ανίχνευση και τον υπολογισμό της εξάπλωσης των ψευδών ειδήσεων και της παραπληροφόρησης, αλλά και στην κατανόηση του συναισθήματος και των αντιδράσεων της κοινής γνώμης. Καθοριστικό ρόλο στην επιτυχία αυτών των εφαρμογών τεχνητής νοημοσύνης και μηχανικής μάθησης διαδραματίζουν τα δεδομένα με τα οποία εκπαιδεύονται τα αντίστοιχα συστήματα. Για το λόγο αυτό, κάνουμε μια ανασκόπηση των διαφορετικών πηγών δεδομένων και παρουσιάζουμε δημοφιλή σύνολα δεδομένων, τα οποία είναι δημόσια διαθέσιμα και μπορούν να χρησιμοποιηθούν για την ανάπτυξη καινοτόμων λύσεων. Ολοκληρώνουμε την Εργασία με την περιγραφή μιας σειράς κοινών προκλήσεων που έχουν να αντιμετωπίσουν οι πιο πάνω εφαρμογές σε σχέση με το πλήθος και την ποιότητα των δεδομένων, την προστασία ευαίσθητων και προσωπικών πληροφοριών, την ασφάλεια, την επεξηγησιμότητα, τη μεροληψία και άλλα ζητήματα ηθικής. Αναγνωρίζοντας τη σοβαρότητα τέτοιων προκλήσεων, προτείνουμε μια σειρά από κατευθύνσεις για την αντιμετώπισή τους, στοχεύοντας παράλληλα στη μεγιστοποίηση του οφέλους από τη χρήση των εφαρμογών αυτών.","COVID-19; SARS-CoV-2; πανδημία; κορωνοϊός; τεχνητή νοημοσύνη; μηχανική μάθηση; βαθιά μάθηση; μεγάλα δεδομένα; επιδημιολογία; ιχνηλάτηση επαφών; διάγνωση; πρόγνωση; ανάλυση ιατρικής εικόνας; ανακάλυψη φαρμάκων; ανακάλυψη εμβολίων; επιδημία πληροφορίας","","","","el","","","","","","","",""
"Journal Article","Kost GJ","","The Impact of Increasing Prevalence, False Omissions, and Diagnostic Uncertainty on Coronavirus Disease 2019 (COVID-19) Test Performance","Arch. Pathol. Lab. Med.","Archives of pathology & laboratory medicine","2021","","","","COVID Tracking Project","","","","meridian.allenpress.com","","","","","2021-03-08","","","0003-9985","1543-2165","http://dx.doi.org/10.5858/arpa.2020-0716-SA;https://www.ncbi.nlm.nih.gov/pubmed/33684204;https://meridian.allenpress.com/aplm/article-lookup/doi/10.5858/arpa.2020-0716-SA;https://meridian.allenpress.com/aplm/article-abstract/doi/10.5858/arpa.2020-0716-SA/462534","10.5858/arpa.2020-0716-SA","33684204","","","","CONTEXT: Coronavirus disease 2019 (COVID-19) test performance depends on predictive values in settings of increasing disease prevalence. Geospatially distributed diagnostics with minimal uncertainty facilitate efficient point-of-need strategies. OBJECTIVES: To use original mathematics to interpret COVID-19 test metrics; assess Food and Drug Administration Emergency Use Authorizations and Health Canada targets; compare predictive values for multiplex, antigen, polymerase chain reaction kit, point-of-care antibody, and home tests; enhance test performance; and improve decision-making. DESIGN: PubMed/newsprint generated articles documenting prevalence. Mathematica and open access software helped perform recursive calculations, graph multivariate relationships, and visualize performance by comparing predictive value geometric mean-squared patterns. RESULTS: Tiered sensitivity/specificity comprise: T1) 90%, 95%; T2) 95%, 97.5%; and T3) 100%, ≥99%. Tier 1 false negatives exceed true negatives at >90.5% prevalence; false positives exceeded true positives at <5.3% prevalence. High sensitivity/specificity tests reduce false negatives and false positives yielding superior predictive values. Recursive testing improves predictive values. Visual logistics facilitate test comparisons. Antigen test quality falls off as prevalence increases. Multiplex severe acute respiratory syndrome (SARS)-CoV-2)*Influenza A/B*Respiratory-Syncytial Virus (RSV) testing performs reasonably well compared to Tier 3. Tier 3 performance with a Tier 2 confidence band lower limit will generate excellent performance and reliability. CONCLUSIONS: The overriding principle is select the best combined performance and reliability pattern for the prevalence bracket. Some public health professionals recommend repetitive testing to compensate for low sensitivity. More logically, improved COVID-19 assays with less uncertainty conserve resources. Multiplex differentiation of COVID-19 from Influenza A/B-RSV represents an effective strategy if seasonal flu surges next year.","","","","","en","Research Article","","","","","","",""
"Conference Paper","Škorić L,Stojanovski J","","Mitovi i zablude o znanstvenoj publicistici","","","2021","","","","COVID Tracking Project","","","","repozitorij.mef.unizg.hr","","","Podne manje kvarat: utorkom o otvorenoj znanosti","","2021","","","","","https://repozitorij.mef.unizg.hr/islandora/object/mef:2874;https://repozitorij.mef.unizg.hr/islandora/object/mef:2874/datastream/FILE0/download","","","","","","… GenBank - OA bazi podataka. Istraživački podaci: The Human Coronaviruses Data Initiative, the COVID-19 Open Source Dashboard, Wikiproject COVID-19 i COVID Tracking Project COVID-19 izvori: CORD-19, LitCovid, Outbreak Science Rapid PREreview …","","","","","","","","","","","","",""
"Journal Article","de Jesus Gonzalez Á,Burgos-López L,Felix ER,Kenny Nienhusser H","","Policy implementation as a tool for advancing equity in community college","Educ. Policy Anal. Arch.","Education policy analysis archives","2021","29","0","25","COVID Tracking Project","","","","epaa.asu.edu","","","","","2021-03-08","2021-04-02","","1068-2341","1068-2341","https://epaa.asu.edu/ojs/article/view/6689;http://dx.doi.org/10.14507/epaa.29.6689;https://epaa.asu.edu/ojs/article/download/6689/2592","10.14507/epaa.29.6689","","","","","This special issue examines the role of policy implementation in the community college context and the ways reforms are enacted to achieve or advance educational equity. In this introduction, we provide an overview of policy implementation, its current landscape within higher education, and the role it can and must serve for community colleges as a tool to advance equity efforts. The articles in this special issue provide a well-rounded overview of policy implementation efforts across various states and institutions. Authors examine promise programs, equity initiatives, articulation agreements, federally funded support programs, and race-conscious implementation. The community college context serves as a critical site of inquiry given that almost half of the undergraduate population is enrolled at a community college. Therefore, the following articles explore how to leverage policy implementation as a tool toward more equitable outcomes.","policy implementation; community college; equity; implementación de políticas; colegio comunitario; equidad; implementação de políticas; faculdade comunitária; equidade","","","","","","","","","","","",""
"Website","Hinkley S","","Rebuilding the public sector for economic recovery and resilience","","","2021","","","","COVID Tracking Project","","","","groundworkcollaborative.org","","","","","2021","2021-04-02","","","","https://groundworkcollaborative.org/wp-content/uploads/2021/03/RebuildingThePublicSector_FINAL.pdf","","","","","","Page 1. groundworkcollaborative.org REBUILDING THE PUBLIC SECTOR FOR ECONOMIC RECOVERY AND RESILIENCE | 1 REBUILDING THE PUBLIC SECTOR FOR ECONOMIC RECOVERY AND RESILIENCE Sara Hinkley, Ph.D. March 2021 Executive Summary …","","","","","","","","","","","","",""
"Journal Article","Monahan T","","Reckoning with COVID, Racial Violence, and the Perilous Pursuit of Transparency","Surveill. Soc.","Surveillance & society","2021","19","1","1-10","COVID Tracking Project","","","","ojs.library.queensu.ca","","","","","2021-03-05","2021-04-02","","1477-7487","","https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/14698;http://dx.doi.org/10.24908/ss.v19i1.14698;https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/download/14698/9526","10.24908/ss.v19i1.14698","","","","","This essay reflects on the many upheavals of the past year and their implications for critical scholarship on surveillance. The COVID-19 pandemic, anti-science policies, radicalized white supremacists, police killings of people of color, and the resurgence of the racial justice movement all inflect surveillance practices in the contemporary moment. In particular, today’s polarized political landscape makes it difficult to condemn surveillance in the service of the public good, but irrespective of one’s goals or intentions, the embrace of transparency carries its own risks. Transparency, and scientific vision more broadly, is an extension of the Enlightenment and subsequent scientific revolution, which from the start sought to advance knowledge and consolidate white power through the violent subjugation of nature, women, and racial minorities. One fundamental risk of valorizing transparency is that doing so occludes the ways that relations of domination are indelibly encoded into surveillance systems and practices. Given this, I argue that the project of decolonizing surveillance inquiry should now be our primary focus as a field.","COVID-19; Transparency; Racial Justice; Decolonization; Surveillance Studies","","","","en","","","","","","","",""
"Preprint Manuscript","Winecoff R,Ayyagari P,McInerney M,Simon K,Bundorf MK","","The hidden role of racial wealth disparities in older adults’ vulnerability to COVID-19","","","2021","","","","COVID Tracking Project","","Research Square","","","","","","","2021-03-03","","","","","https://www.researchsquare.com/article/rs-271452/latest.pdf;https://www.researchsquare.com/article/rs-271452/v1;http://dx.doi.org/10.21203/rs.3.rs-271452/v1","10.21203/rs.3.rs-271452/v1","","","","","Abstract Background: To examine racial and ethnic differences in wealth and other economic, exposure and baseline health-related risks of COVID-19 among older adults in the U.S. Methods: Using rich data on wealth and long-term care use among older Americans unique to the 2016 Health and Retirement Study, we quantify differences in COVID-19 vulnerability among non-Hispanic white, non-Hispanic Black and Hispanic respondents aged 50+. We measure wealth, other economic (insurance, income); exposure (long-term care, employment, telework, household size); and health (chronic conditions, smoking) risk stratified by age (50-64, 65+). Results: Blacks and Hispanics face dramatically greater financial risk that potentially increases exposure to COVID-19, relative to whites; Blacks and Hispanics are four to five times more likely to have no financial wealth. Blacks are also more likely than whites to use long-term care. Blacks and Hispanics also are less likely to have health insurance and face greater risk of exposure to COVID-19 because they are less likely to telework, and Hispanic older adults reside in larger households. Black and Hispanic older adults are also more likely to have a chronic condition associated with worse COVID-19 outcomes. Conclusions: Our results suggest that wealth differences may play a substantial role in contributing to the very large racial and ethnic disparities in the health burden of COVID-19. Racial disparities in long-term care, where COVID-19 risks are higher, contribute to make older Black Americans even more vulnerable to COVID-19.","","","","Indiana University Bloomington School of Public and Environmental Affairs; University of South Florida; Tufts University; Indiana University Bloomington; Duke University","","","","","","","","","Research Square"
"Journal Article","Pivert KA,Boyle SM,Halbach SM,Chan L,Shah HH,Waitzman JS,Mehdi A,Norouzi S,Sozio SM","","Impact of the COVID-19 Pandemic on Nephrology Fellow Training and Well-Being in the United States: A National Survey","J. Am. Soc. Nephrol.","Journal of the American Society of Nephrology: JASN","2021","","","","COVID Tracking Project","","","","Am Soc Nephrol","","","","","2021-03-03","","","1046-6673","1533-3450","http://dx.doi.org/10.1681/ASN.2020111636;https://www.ncbi.nlm.nih.gov/pubmed/33658283;https://jasn.asnjournals.org/cgi/pmidlookup?view=long&pmid=33658283;https://jasn.asnjournals.org/content/early/2021/03/03/ASN.2020111636.abstract;https://jasn.asnjournals.org/content/jnephrol/early/2021/03/03/ASN.2020111636.full.pdf","10.1681/ASN.2020111636","33658283","","","","BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic's effects on nephrology fellows' educational experiences, preparedness for practice, and emotional wellbeing are unknown. METHODS: We recruited current adult and pediatric fellows and 2020 graduates of nephrology training programs in the United States to participate in a survey measuring COVID-19's effects on their training experiences and wellbeing. RESULTS: Of 1005 nephrology fellows-in-training and recent graduates, 425 participated (response rate 42%). Telehealth was widely adopted (90% for some or all outpatient nephrology consults), as was remote learning (76% of conferences were exclusively online). Most respondents (64%) did not have in-person consults on COVID-19 inpatients; these patients were managed by telehealth visits (27%), by in-person visits with the attending faculty without fellows (29%), or by another approach (9%). A majority of fellows (84%) and graduates (82%) said their training programs successfully sustained their education during the pandemic, and most fellows (86%) and graduates (90%) perceived themselves as prepared for unsupervised practice. Although 42% indicated the pandemic had negatively affected their overall quality of life and 33% reported a poorer work-life balance, only 15% of 412 respondents who completed the Resident Well-Being Index met its distress threshold. Risk for distress was increased among respondents who perceived the pandemic had impaired their knowledge base (odds ratio [OR], 3.04; 95% confidence interval [CI], 2.00 to 4.77) or negatively affected their quality of life (OR, 3.47; 95% CI, 2.29 to 5.46) or work-life balance (OR, 3.16; 95% CI, 2.18 to 4.71). CONCLUSIONS: Despite major shifts in education modalities and patient care protocols precipitated by the COVID-19 pandemic, participants perceived their education and preparation for practice to be minimally affected.","COVID-19; COVID-19 pandemic; nephrology training; physician burnout","","","Data Science and Public Impact, American Society of Nephrology, Washington, DC kpivert@asn-online.org. Section of Nephrology, Hypertension, and Kidney Transplantation, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania. Department of Pediatrics, Division of Nephrology, University of Washington and Seattle Children's Hospital, Seattle, Washington. Charles Bronfman Institute of Personalized Medicine, Department of Genetics and Genomics; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York. Division of Kidney Diseases and Hypertension, Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Great Neck, New York. Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts. Department of Nephrology and Hypertension-Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, Ohio. Department of Nephrology, Loma Linda University Medical Center, Loma Linda, California. Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.","en","Research Article","","","","","","",""
"Journal Article","Perniciaro SR,Weinberger DM","","50 policies, 1 pandemic, 500,000 deaths: Associations between state-level COVID-19 testing recommendations, tests per capita, undercounted deaths, vaccination policies, 2 and doses per capita in the United States 3","europepmc.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://europepmc.org/articles/pmc7668755/bin/nihpp2020.09.04.20188326-supplement-1.pdf","","","","","","… github.com/weinbergerlab/excess_pi_covid). 16 Positive tests and the overall number of tests in each state were collected from the website of The COVID Tracking Project (https://covidtracking.com/). Vaccine policy data were …","","","","","","","","","","","","",""
"Journal Article","Wright MT","","Generation-Blindness and the COVID-19 Websites of Highly Selective Universities","","","2021","","","","COVID Tracking Project","","","","repository.upenn.edu","","","","","2021","2021-04-02","","","","https://repository.upenn.edu/education_inequality_workshop/7/;https://repository.upenn.edu/cgi/viewcontent.cgi?article=1006&context=education_inequality_workshop","","","","","","This study analyzes how highly selective universities used their COVID-19 websites to publicly address first-generation students and the challenges these students faced at the onset of the COVID-19 pandemic in 2020. Specifically, the study investigates whether universities were generation-blind in their responses. The universities’ responses are defined as generation-blind if their COVID-19 websites did not a) reference or acknowledge generational identity; and/or did not b) address the issues that first-generation students faced at the onset of the pandemic and transition to remote learning. Findings show that highly selective universities almost never mentioned the term “first-generation students” on these websites and rarely addressed several critical issues that concerned first-generation students. These issues include: the challenge of navigating the complexities of the first-generation identity during the pandemic; the struggles that family members of these students faced (i.e. job loss); the students’ imperative to support their families (i.e. helping to watch younger siblings); and the difficulties students faced by having to use their homes as learning environments.","","","","","","","Penn Education and Inequality Working Papers","","","","","",""
"Journal Article","Vardavas R,de Lima PN,Baker L","","Modeling COVID-19 Nonpharmaceutical Interventions","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2021.02.28.21252642v1.full.pdf","","","","","","Page 1. Modeling COVID-19 Nonpharmaceutical Interventions: Exploring periodic NPI strategies Raffaele Vardavas ∗ , Pedro Nascimento de Lima, and Lawrence Baker RAND Corporation, Santa Monica, CA 90401, USA Abstract …","","","","","","","","","","","","",""
"Preprint Manuscript","Javeri IY,Toutiaee M,Arpinar IB,Miller TW,Miller JA","","Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-02","","","","","http://arxiv.org/abs/2103.01992","","","2103.01992","","","Statistical methods such as the Box-Jenkins method for time-series forecasting have been prominent since their development in 1970. Many researchers rely on such models as they can be efficiently estimated and also provide interpretability. However, advances in machine learning research indicate that neural networks can be powerful data modeling techniques, as they can give higher accuracy for a plethora of learning problems and datasets. In the past, they have been tried on time-series forecasting as well, but their overall results have not been significantly better than the statistical models especially for intermediate length times series data. Their modeling capacities are limited in cases where enough data may not be available to estimate the large number of parameters that these non-linear models require. This paper presents an easy to implement data augmentation method to significantly improve the performance of such networks. Our method, Augmented-Neural-Network, which involves using forecasts from statistical models, can help unlock the power of neural networks on intermediate length time-series and produces competitive results. It shows that data augmentation, when paired with Automated Machine Learning techniques such as Neural Architecture Search, can help to find the best neural architecture for a given time-series. Using the combination of these, demonstrates significant enhancement for two configurations of our technique for a COVID-19 dataset, improving forecasting accuracy by 19.90% and 11.43%, respectively, over the neural networks that do not use augmented data.","","","","","","","","arXiv","2103.01992","cs.LG","","","arXiv [cs.LG]"
"Journal Article","Liebenberg L,Lombard M,Shermer M,Xhukwe U,Biesele M,Carruthers P,Kxao O,Hansson SO,Langwane HK,Elbroch LM,Others","","Tracking Science: An Alternative for Those Excluded by Citizen Science","Citizen Science: Theory and Practice","","2021","6","1","","COVID Tracking Project","","","","Ubiquity Press","","","","","2021","","","","","https://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.284/print/","","","","","","… al. 2018). The current pandemic is being tracked by The COVID Tracking Project (https://covidtracking.com/), the Financial Times “Coronavirus tracker” (ft.com), and Bloomberg's “Tracking Covid-19” (bloomberg.com). We can …","","","","","","","","","","","","",""
"Preprint Manuscript","Rubio-Herrero J,Wang Y","","A Flexible Rolling Regression Framework for Time-Varying SIRD models: Application to COVID-19","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-02","","","","","http://arxiv.org/abs/2103.02048","","","2103.02048","","","The present paper introduces a data-driven framework for describing the time-varying nature of an SIRD model in the context of COVID-19. By embedding a rolling regression in a mixed integer bilevel nonlinear programming problem, our aim is to provide the research community with a model that reproduces accurately the observed changes in the number of infected, recovered, and death cases, while providing information about the time dependency of the parameters that govern the SIRD model. We propose this optimization model and a genetic algorithm to tackle its solution. Moreover, we test this algorithm with 2020 COVID-19 data from the state of Minnesota and found that our results are consistent both qualitatively and quantitatively, thus proving that the framework proposed is an effective an flexible tool to describe the dynamics of a pandemic.","","","","","","","","arXiv","2103.02048","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Cohen J,Fung A","","Democracy and the Digital Public Sphere","Digital Technology and Democratic Theory","","2021","","","","COVID Tracking Project","","","","degruyter.com","","","","","2021","","","","","https://www.degruyter.com/document/doi/10.7208/9780226748603/pdf#page=29","","","","","","Page 29. 1 Democracy and the Digital Public Sphere Joshua Cohen and Archon Fung The more the bonding force of communicative action wanes in private life spheres and the embers of communicative freedom die out, the …","","","","","","","","","","","","",""
"Journal Article","Krofah E,Schneeman K","","Are There Silver Linings for Biomedical Innovation?","","","2021","","","","COVID Tracking Project","","","","milkeninstitute.org","","","","","2021","","","","","https://milkeninstitute.org/sites/default/files/reports-pdf/MI_SilverLining_report_012221.pdf","","","","","","Page 1. LESSONS LEARNED FROM COVID-19: Are There Silver Linings for Biomedical Innovation? ESTHER KROFAH AND KRISTIN SCHNEEMAN Page 2. LESSONS LEARNED FROM COVID-19: ARE THERE SILVER LININGS FOR BIOMEDICAL INNOVATION …","","","","","","","","","","","","",""
"Journal Article","Hilsman EG","","OFFICE OF SUICIDE & VIOLENCE PREVENTION","nova.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.nova.edu/suicideprevention/forms/newsletter-2021-01.pdf","","","","","","… rate of employment in the service industry. Source: COVID Tracking Project ; 2018 American Community Survey five-year estimates from the US Census Bureau. Credit: Daniel Wood/NPR The low average SES of Hispanics, compared …","","","","","","","","","","","","",""
"Journal Article","March RJ","","The FDA and the COVID‐19: A political economy perspective","South. Econ. J.","Southern economic journal","2021","","","","COVID Tracking Project","","","","Wiley Online Library","","","","","2021","","","0038-4038","","https://onlinelibrary.wiley.com/doi/abs/10.1002/soej.12494?casa_token=l3CqgPEZle8AAAAA:5YNVtDwOL2nPWQkq08WbeWstXAKU9jzquiByw5xVbU_0QuS9nin2TOFMdPrA2ko0ajvnolfb5ozDJAZS;https://onlinelibrary.wiley.com/doi/pdf/10.1002/soej.12494?casa_token=cttS8P7SugcAAAAA:h264b4U2MQIEZ55yQu6gO5NNN8jYBSZ-0ap-iNZV6_oyA8cLBlMar854k2cJ_1Rake4gD05w90rB-LB-","","","","","","… FDA, 2020). Seeking additional input came at a high cost. The COVID Tracking Project (2020) finds an average of approximately 2,200 patients died from COVID-19 daily from November 22 to December 18. Further analysis …","","","","","","","","","","","","",""
"Journal Article","Villa J,Pannu T,McWilliams C,Kizer C,Rosenthal R,Higuera C,Patel P","","Results of preoperative screening for COVID-19 correlate with the incidence of infection in the general population -a tertiary care experience","Hosp. Pract. ","Hospital practice ","2021","","","1-5","COVID Tracking Project","","","","Taylor & Francis","","","","","2021-03-14","","","2154-8331","","http://dx.doi.org/10.1080/21548331.2021.1898158;https://www.ncbi.nlm.nih.gov/pubmed/33647224;https://www.tandfonline.com/doi/full/10.1080/21548331.2021.1898158;https://www.tandfonline.com/doi/abs/10.1080/21548331.2021.1898158?casa_token=zypHVoO7EfUAAAAA:EcPcPeHd-40iED31F-1MFLkzGpYHToU76R8cFfG_bBOPuEDvFZe-WDw2BYaZcRQkZBOZey1b5XOC7Q;https://www.tandfonline.com/doi/pdf/10.1080/21548331.2021.1898158?casa_token=_NJk6JsNEd0AAAAA:OExDCsZiX69RDrAH9VgcZtTbPQdPkjxOB_NscfviKGBh7Sx74yZnelv2tqsyKo8w6-GW2hD5thROMw","10.1080/21548331.2021.1898158","33647224","","","","Objectives: Many hospitals have recently instituted policies mandating preoperative COVID-19 testing. However, it is uncertain whether institutions can dictate such policies based on infection rates found in the general population. Therefore, the main aims of the study were to determine (1) what proportion of preoperative patients tested positive, (2) what percentage was asymptomatic, and (3) whether variations throughout time in numbers of positive patients reflected changes observed in our state.Methods: All COVID-19 preoperative screening tests (nasopharyngeal-swab RT-PCR testing) performed in our hospital between 04/13/2020 and 08/27/2020 were retrospectively reviewed. The unit of analysis was number of patients who tested negative/positive. Medical records of positive patients were reviewed to determine the presence of COVID-19 symptoms. A curve was created showing our number of positive patients per week and another one presenting the number of positive patients per day in Florida, both figures were compared.Results: A total of 7,213 patients from all specialties were preoperatively tested, out of which 85 were positive for an overall infection rate of 1.2%. In 18% (15/85) of positive patients, it was not possible to determine symptomatology. Among remaining patients, 49% (34/70) were asymptomatic while 51% (36/70) were symptomatic for COVID-19. Peak of positive cases occurred in mid-July in both curves, and the upward and downward tendencies in positive numbers mirrored each other.Conclusion: COVID-19 infection rate among our preoperative patients was very low. Nearly 50% of positive patients were asymptomatic. Our data suggest that a tertiary hospital can promulgate COVID-19 preoperative screening policies based on infection trends observed in the general population. However, in addition to the test, patients should be encouraged to self-quarantine for 14 days before surgery.","COVID-19; general population; infection rate; preoperative screening; tertiary care institution","","","Levitetz Department of Orthopaedic Surgery, Cleveland Clinic Florida, Weston, United States. Infectious Disease Department, Cleveland Clinic Florida, Weston, United States. Quality Management Department, Cleveland Clinic Florida, Weston, United States. Department of General Surgery, Cleveland Clinic Florida, Weston, United States.","en","Research Article","","","","","","",""
"Journal Article","Wongvibulsin S,Garibaldi BT,Antar AAR,Wen J,Wang MC,Gupta A,Bollinger R,Xu Y,Wang K,Betz JF,Muschelli J,Bandeen-Roche K,Zeger SL,Robinson ML","","Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19","Ann. Intern. Med.","Annals of internal medicine","2021","","","","COVID Tracking Project","","","","acpjournals.org","","","","","2021-03-02","","","0003-4819","1539-3704","http://dx.doi.org/10.7326/M20-6754;https://www.ncbi.nlm.nih.gov/pubmed/33646849;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934337;https://www.acpjournals.org/doi/10.7326/M20-6754?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.acpjournals.org/doi/abs/10.7326/M20-6754?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.acpjournals.org/doi/abs/10.7326/M20-6754;https://www.acpjournals.org/doi/pdf/10.7326/M20-6754","10.7326/M20-6754","33646849","","","PMC7934337","BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTING: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.","","","","Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.). Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.). Johns Hopkins University, Baltimore, Maryland (Y.X., K.W.).","en","Research Article","","","","","","",""
"Journal Article","Garibaldi BT,Wang K,Robinson ML,Zeger SL,Bandeen-Roche K,Wang MC,Alexander GC,Gupta A,Bollinger R,Xu Y","","Comparison of Time to Clinical Improvement With vs Without Remdesivir Treatment in Hospitalized Patients With COVID-19","JAMA Netw Open","JAMA network open","2021","4","3","e213071","COVID Tracking Project","","","","jamanetwork.com","","","","","2021-03-01","","","2574-3805","","http://dx.doi.org/10.1001/jamanetworkopen.2021.3071;https://www.ncbi.nlm.nih.gov/pubmed/33760094;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2021.3071;https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2777863;https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2777863/garibaldi_2021_oi_210112_1616042465.1957.pdf","10.1001/jamanetworkopen.2021.3071","33760094","","","","Importance: Clinical effectiveness data on remdesivir are urgently needed, especially among diverse populations and in combination with other therapies. Objective: To examine whether remdesivir administered with or without corticosteroids for treatment of coronavirus disease 2019 (COVID-19) is associated with more rapid clinical improvement in a racially/ethnically diverse population. Design, Setting, and Participants: This retrospective comparative effectiveness research study was conducted from March 4 to August 29, 2020, in a 5-hospital health system in the Baltimore, Maryland, and Washington, DC, area. Of 2483 individuals with confirmed severe acute respiratory syndrome coronavirus 2 infection assessed by polymerase chain reaction, those who received remdesivir were matched to infected individuals who did not receive remdesivir using time-invariant covariates (age, sex, race/ethnicity, Charlson Comorbidity Index, body mass index, and do-not-resuscitate or do-not-intubate orders) and time-dependent covariates (ratio of peripheral blood oxygen saturation to fraction of inspired oxygen, blood pressure, pulse, temperature, respiratory rate, C-reactive protein level, complete white blood cell count, lymphocyte count, albumin level, alanine aminotransferase level, glomerular filtration rate, dimerized plasmin fragment D [D-dimer] level, and oxygen device). An individual in the remdesivir group with k days of treatment was matched to a control patient who stayed in the hospital at least k days (5 days maximum) beyond the matching day. Exposures: Remdesivir treatment with or without corticosteroid administration. Main Outcomes and Measures: The primary outcome was rate of clinical improvement (hospital discharge or decrease of 2 points on the World Health Organization severity score), and the secondary outcome, mortality at 28 days. An additional outcome was clinical improvement and time to death associated with combined remdesivir and corticosteroid treatment. Results: Of 2483 consecutive admissions, 342 individuals received remdesivir, 184 of whom also received corticosteroids and 158 of whom received remdesivir alone. For these 342 patients, the median age was 60 years (interquartile range, 46-69 years), 189 (55.3%) were men, and 276 (80.7%) self-identified as non-White race/ethnicity. Remdesivir recipients had a shorter time to clinical improvement than matched controls without remdesivir treatment (median, 5.0 days [interquartile range, 4.0-8.0 days] vs 7.0 days [interquartile range, 4.0-10.0 days]; adjusted hazard ratio, 1.47 [95% CI, 1.22-1.79]). Remdesivir recipients had a 28-day mortality rate of 7.7% (22 deaths) compared with 14.0% (40 deaths) among matched controls, but this difference was not statistically significant in the time-to-death analysis (adjusted hazard ratio, 0.70; 95% CI, 0.38-1.28). The addition of corticosteroids to remdesivir was not associated with a reduced hazard of death at 28 days (adjusted hazard ratio, 1.94; 95% CI, 0.67-5.57). Conclusions and Relevance: In this comparative effectiveness research study of adults hospitalized with COVID-19, receipt of remdesivir was associated with faster clinical improvement in a cohort of predominantly non-White patients. Remdesivir plus corticosteroid administration did not reduce the time to death compared with remdesivir administered alone.","","","","Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland. Division of Infectious Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland. Division of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. Division of Biostatistics and Bioinformatics at The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.","en","Research Article","","","","","","",""
"Preprint Manuscript","Mohd S,Mustafee N,Madan K,Ramamohan V","","Leveraging Healthcare Facility Network Simulations for Capacity Planning and Facility Location in a Pandemic","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-28","2021-04-02","","","","https://papers.ssrn.com/abstract=3794811;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3794811;http://dx.doi.org/10.2139/ssrn.3794811;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3794811","10.2139/ssrn.3794811","","","","","The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and further downstream to district hospitals (DH). A network-based modelling and simulation (M&S) approach would (a) quantify the extent to which existing PHC, CHC and DH facilities can effectively cope with the forecasted COVID-19 patient load, whilst continuing to offer non-COVID-19 related care functions such as childbirth care, surgeries and outpatient clinics; (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows from the PHS; (c) enable identifying the optimal location of such makeshift facilities. We apply the network-based M&S approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC, and report operational outcomes for existing PHS capacity and estimate the required capacity for the CCC under a specific pandemic response strategy. We identify the optimal location of the CCC through the combined application of the network simulation with a version of the stochastic ruler algorithm customized for this problem. Our work contributes to the literature on network modelling using simulation and optimisation approaches. Although this paper concerns COVID-19 operations management and CCC capacity planning, the approach could be used more generally in a pandemic situation.","OR in Developing Countries, Healthcare Network Simulation, COVID-19 Operations, Capacity Planning, Healthcare Facility Location","","","","","","","","","","","","Available at SSRN"
"Journal Article","Chokshi A,DallaPiazza M,Zhang WW,Sifri Z","","Proximity to international airports and early transmission of COVID-19 in the United States-An epidemiological assessment of the geographic distribution of 490,000 cases","Travel Med. Infect. Dis.","Travel medicine and infectious disease","2021","40","","102004","COVID Tracking Project","","","","Elsevier","","","","","2021-03","","","1477-8939","1873-0442","http://dx.doi.org/10.1016/j.tmaid.2021.102004;https://www.ncbi.nlm.nih.gov/pubmed/33640475;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906855;https://linkinghub.elsevier.com/retrieve/pii/S1477-8939(21)00045-4;https://www.sciencedirect.com/science/article/pii/S1477893921000454;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906855/","10.1016/j.tmaid.2021.102004","33640475","","","PMC7906855","BACKGROUND: Identifying hotspots in a pandemic is essential for early containment. In the context of the rapid global dissemination of the Covid-19 pandemic, describing viral infection rates in relation to international air travel early during the pandemic can help inform future public health policy. The objective of this study is to determine whether proximity to an international airport predicted higher infection rates during the early phase of the Covid-19 pandemic in the United States (US). METHODS: In this cross-sectional study, the authors examined the incidence of Covid-19 in areas near US international airports in the first weeks after detection of Covid-19 in all 50 states, using publicly available county-level incidence of Covid-19 data. They performed a multiple regression to determine the relative effects of population density and air traffic in the Counties Containing Airports (CCA) and the number of Covid-19 cases, and determined the odds of Covid-19 in CCA compared to the rest of the state. RESULTS: Multiple regression analysis revealed that air traffic was significantly correlated with Covid-19 cases during the initial phase of pandemic while population density was not significantly correlated. Three weeks into the pandemic, the pooled odds of Covid-19 cases in CCA was 2.66 (95% CI [2.64, 2.68], p < 0.0001). CONCLUSIONS: The counties in the US containing international airports represented initial hotspots for Covid-19 transmission. Early public health containment efforts focused on these areas may help mitigate disease transmission during future similar novel respiratory virus epidemics.","Containment; Covid-19; Hotspots; Public health interventions","","","Rutgers New Jersey Medical School, Department of Medicine, 185 S Orange Ave, Newark, NJ, 07103, USA. Electronic address: acc224@njms.rutgers.edu. Rutgers New Jersey Medical School, Department of Medicine, 185 S Orange Ave, Newark, NJ, 07103, USA. Electronic address: mld229@njms.rutgers.edu. Rutgers New Jersey Medical School, Department of Surgery, 185 S Orange Ave, Newark, NJ, 07103, USA. Electronic address: wz280@njms.rutgers.edu. Rutgers New Jersey Medical School, Department of Surgery, 185 S Orange Ave, Newark, NJ, 07103, USA. Electronic address: sifrizi@njms.rutgers.edu.","en","Research Article","","","","","","",""
"Journal Article","Barie PS,Ho VP,Hunter CJ,Kaufman EJ,Narayan M,Pieracci FM,Schubl SD,Heffernan DS,Huston JM","","Surgical Infection Society Guidance for Restoration of Surgical Services during the Coronavirus Disease-2019 Pandemic","Surg. Infect. ","Surgical infections","2021","","","","COVID Tracking Project","","","","liebertpub.com","","","","","2021-02-25","","","1096-2964","1557-8674","http://dx.doi.org/10.1089/sur.2020.421;https://www.ncbi.nlm.nih.gov/pubmed/33635145;https://www.liebertpub.com/doi/10.1089/sur.2020.421?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://www.liebertpub.com/doi/abs/10.1089/sur.2020.421?casa_token=6VhB0tytfqwAAAAA:3dd1R3pnhAhFKbpTVm8uSSNZsNpTH876vixtNwy3u_lpwbaLzVGtfzXMnzuM19ccwfm7-I_BQhGuuW4;https://www.liebertpub.com/doi/pdfplus/10.1089/sur.2020.421?casa_token=_JztqoEbyC8AAAAA:Hj7QjRPgUL8XjtnYmx8FJYYpIueTMwncIrjx3VB6cW1xOIhGb5iy_raE5NI2SKxyvpwh2BlQG9wf64w","10.1089/sur.2020.421","33635145","","","","Background: As the coronavirus disease-2019 (COVID-19) pandemic continues globally, high numbers of new infections are developing nationwide, particularly in the U.S. Midwest and along both the Atlantic and Pacific coasts. The need to accommodate growing numbers of hospitalized patients has led facilities in affected areas to suspend anew or curtail normal hospital activities, including elective surgery, even as earlier-affected areas normalized surgical services. Backlogged surgical cases now number in the tens of millions globally. Facilities will be hard-pressed to address these backlogs, even absent the recrudescence of COVID-19. This document provides guidance for the safe and effective resumption of surgical services as circumstances permit. Methods: Review and synthesis of pertinent international peer-reviewed literature, with integration of expert opinion. Results: The \"second-wave\" of serious infections is placing the healthcare system under renewed stress. Surgical teams likely will encounter persons harboring the virus, whether symptomatic or not. Continued vigilance and protection of patients and staff remain paramount. Reviewed are the impact of COVID-19 on the surgical workforce, considerations for operating on a COVID-19 patient and the outcomes of such operations, the size and nature of the surgical backlog, and the logistics of resumption, including organizational considerations, patient and staff safety, preparation of the surgical candidate, and the role of enhanced recovery programs to reduce morbidity, length of stay, and cost by rational, equitable resource utilization. Conclusions: Resumption of surgical services requires institutional commitment (including teams of surgeons, anesthesiologists, nurses, pharmacists, therapists, dieticians, and administrators). Structured protocols and equitable implementation programs, and iterative audit, planning, and integration will improve outcomes, enhance safety, preserve resources, and reduce cost, all of which will contribute to safe and successful reduction of the surgical backlog.","SARS-CoV-2; coronavirus disease-2019 (COVID-19); elective surgery; health disparities; peri-operative testing; resumption of surgical services","","","Department of Surgery, Weill Cornell Medicine, New York, New York, USA. Department of Medicine, Weill Cornell Medicine, New York, New York, USA. Department of Surgery, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio, USA. Department of Surgery, University of Oklahoma College of Medicine, Oklahoma City, Oklahoma, USA. Department of Surgery, Penn Medicine, Philadelphia, Pennsylvania, USA. Department of Surgery, DenverHealth Medical Center, University of Colorado Anschutz School of Medicine, Denver, Colorado, USA. Department of Surgery, University of California-Irvine, Orange, California, USA. Department of Surgery, Providence Veterans Affairs Medical Center, Alpert Medical School of Brown University, Providence, Rhode Island, USA. Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York, USA. Department of Science Education, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York, USA.","en","Research Article","","","","","","",""
"Journal Article","Guo Y,Yu H,Zhang G,Ma DT","","Exploring the impacts of travel-implied policy factors on COVID-19 spread within communities based on multi-source data interpretations","Health Place","Health & place","2021","69","","102538","COVID Tracking Project","","","","Elsevier","","","","","2021-02-25","","","1353-8292","1873-2054","http://dx.doi.org/10.1016/j.healthplace.2021.102538;https://www.ncbi.nlm.nih.gov/pubmed/33706209;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904495;https://linkinghub.elsevier.com/retrieve/pii/S1353-8292(21)00034-4;https://www.sciencedirect.com/science/article/pii/S1353829221000344?casa_token=9RSsBzXLRyUAAAAA:zLGBrBczgeGoEGk1VR969tV7Wg1Bkq7eqO0Y3BMWV4pDJx0pAoamKlaZeJ72f5O1yNTPwK19cQ4","10.1016/j.healthplace.2021.102538","33706209","","","PMC7904495","The global Coronavirus Disease 2019 (COVID-19) pandemic has led to the implementation of social distancing measures such as work-from-home orders that have drastically changed people's travel-related behavior. As countries are easing up these measures and people are resuming their pre-pandemic activities, the second wave of COVID-19 is observed in many countries. This study proposes a Community Activity Score (CAS) based on inter-community traffic characteristics (in and out of community traffic volume and travel distance) to capture the current travel-related activity level compared to the pre-pandemic baseline and study its relationship with confirmed COVID-19 cases. Fourteen other travel-related factors belonging to five categories (Social Distancing Index, residents staying at home, travel frequency and distance, mobility trend, and out-of-county visitors) and three social distancing measures (stay-at-home order, face-covering order, and self-quarantine for out-of-county travels) are also considered to reflect the likelihood of exposure to the COVID-19. Considering that it usually takes days from exposure to confirming the infection, the exposure-to-confirm temporal delay between the time-varying travel-related factors and their impacts on the number of confirmed COVID-19 cases is considered in this study. Honolulu County in the State of Hawaii is used as a case study to evaluate the proposed CAS and other factors on confirmed COVID-19 cases with various temporal delays at a county-level. Negative Binomial models were chosen to study the impacts of travel-related factors and social distancing measures on COVID-19 cases. The case study results show that CAS and other factors are correlated with COVID-19 spread, and models that factor in the exposure-to-confirm temporal delay perform better in forecasting COVID-19 cases later. Policymakers can use the study's various findings and insights to evaluate the impacts of social distancing policies on travel and effectively allocate resources for the possible increase in confirmed COVID-19 cases.","COVID-19; Community activity; Social distancing measures; Temporal delay; Travel behavior","","","Department of Traffic Engineering & Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China. Electronic address: yuntaoguo@tongji.edu.cn. School of Transportation, Southeast University, Nanjing, 210096, China. Electronic address: seudarwin@gmail.com. Civil and Environmental Engineering Department, University of Hawaii at Manoa, Honolulu, HI, 96815, USA. Electronic address: guohui.zhang@hawaii.edu. Civil and Environmental Engineering Department, University of Hawaii at Manoa, Honolulu, HI, 96815, USA. Electronic address: tianwei@hawaii.edu.","en","Research Article","","","","","","",""
"Journal Article","Shim RS,Starks SM","","COVID-19, Structural Racism, and Mental Health Inequities: Policy Implications for an Emerging Syndemic","Psychiatr. Serv.","Psychiatric services ","2021","","","appips202000725","COVID Tracking Project","","","","Am Psychiatric Assoc","","","","","2021-02-24","","","1075-2730","1557-9700","http://dx.doi.org/10.1176/appi.ps.202000725;https://www.ncbi.nlm.nih.gov/pubmed/33622042;https://ps.psychiatryonline.org/doi/10.1176/appi.ps.202000725?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://ps.psychiatryonline.org/doi/abs/10.1176/appi.ps.202000725;https://ps.psychiatryonline.org/doi/pdf/10.1176/appi.ps.202000725","10.1176/appi.ps.202000725","33622042","","","","The complex interactions between the 2019 coronavirus disease (COVID-19) pandemic, structural racism, and mental health inequities have led to devastating health, economic, and social consequences. The intersection of these three conditions, which meets criteria for a syndemic (synergistic epidemics), presents numerous policy challenges-and opportunities. Addressing these issues in a unified manner, using a syndemic theory approach, can lead to significant progress and effective solutions for otherwise intransigent problems in society. This article proposes steps that can be taken to protect \"essential workers\" and other \"vulnerable\" populations; engage and empower communities; optimize community-led crisis response interventions; improve data collection about the intersection of COVID-19, structural racism, and mental health inequities; support school-based interventions; expand financial supports for mental health service delivery; expand health care insurance coverage to increase access and lower out-of-pocket costs; and promote workforce diversity. Emphasis on local, state, and federal policy interventions that prioritize equity and justice and focus on collective health and well-being will ultimately lead us on a more sustainable and equitable path.","Coronavirus/COVID-19; Public policy issues; Racism; Sociopolitical issues","","","Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento (Shim); Department of Clinical Sciences, University of Houston College of Medicine, Houston (Starks).","en","Research Article","","","","","","",""
"Journal Article","Li L,Ma Z,Lee H,Lee S","","Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?","Int J Disaster Risk Reduct","International journal of disaster risk reduction : IJDRR","2021","56","","102142","COVID Tracking Project","","","","Elsevier","","","","","2021-04-01","","","2212-4209","","http://dx.doi.org/10.1016/j.ijdrr.2021.102142;https://www.ncbi.nlm.nih.gov/pubmed/33643835;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902209;https://linkinghub.elsevier.com/retrieve/pii/S2212-4209(21)00108-4;https://www.sciencedirect.com/science/article/pii/S2212420921001084;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902209/","10.1016/j.ijdrr.2021.102142","33643835","","","PMC7902209","The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.","COVID-19; Lockdown; Social distancing; Social media; Text classification","","","Department of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. University of Maryland School of Dentistry, Baltimore, MD, USA.","en","Research Article","","","","","","",""
"Journal Article","Reimer JR,Ahmed SM,Brintz B,Shah RU,Keegan LT,Ferrari MJ,Leung DT","","Using a clinical prediction rule to prioritize diagnostic testing leads to reduced transmission and hospital burden: A modeling example of early SARS-CoV-2","Clin. Infect. Dis.","Clinical infectious diseases: an official publication of the Infectious Diseases Society of America","2021","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2021-02-23","","","1058-4838","1537-6591","http://dx.doi.org/10.1093/cid/ciab177;https://www.ncbi.nlm.nih.gov/pubmed/33621329;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929067;https://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciab177;https://academic.oup.com/cid/advance-article-abstract/doi/10.1093/cid/ciab177/6148743;https://academic.oup.com/cid/advance-article-pdf/doi/10.1093/cid/ciab177/36347799/ciab177.pdf?casa_token=caVaqbCxajcAAAAA:2MpI2WOhrMI0z0tPbM6yB02X3AC2SAN26XkJa8R5wiSjRZVAMPa3I5zVesZEegwgzkueStWlV_Nrmw","10.1093/cid/ciab177","33621329","","","PMC7929067","BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early SARS-CoV-2 as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (AUC: 0.69 (95% CI: 0.68 - 0.70)) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (i.e., \"flattens the curve\"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.","clinical prediction rule; diagnostic testing; transmission dynamics","","","University of Utah, Department of Mathematics, Salt Lake City, UT, United States of America. University of Utah School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, Salt Lake City UT, United States of America. University of Utah School of Medicine, Department of Internal Medicine, Division of Epidemiology, Salt Lake City UT, United States of America. University of Utah School of Medicine, Department of Internal Medicine, Division of Cardiovascular Medicine, Salt Lake City UT, United States of America. The Pennsylvania State University, Department of Biology, State College, PA, United States of America.","en","Research Article","","","","","","",""
"Journal Article","Zimmerman FJ,Anderson NW","","Association of the Timing of School Closings and Behavioral Changes With the Evolution of the Coronavirus Disease 2019 Pandemic in the US","JAMA Pediatr.","JAMA pediatrics","2021","","","","COVID Tracking Project","","","","jamanetwork.com","","","","","2021-02-22","","","2168-6203","2168-6211","http://dx.doi.org/10.1001/jamapediatrics.2020.6371;https://www.ncbi.nlm.nih.gov/pubmed/33616635;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900933;https://jamanetwork.com/journals/jamapediatrics/fullarticle/10.1001/jamapediatrics.2020.6371;https://jamanetwork.com/journals/jamapediatrics/articlepdf/2776608/jamapediatrics_zimmerman_2021_oi_200101_1613082132.79522.pdf;https://jamanetwork.com/journals/jamapediatrics/fullarticle/2776608","10.1001/jamapediatrics.2020.6371","33616635","","","PMC7900933","Importance: The consequences of school closures for children's health are profound, but existing evidence on their effectiveness in limiting severe acute respiratory syndrome coronavirus 2 transmission is unsettled. Objective: To determine the independent associations of voluntary behavioral change, school closures, and bans on large gatherings with the incidence and mortality due to coronavirus disease 2019 (COVID-19). Design, Setting, and Participants: This population-based, interrupted-time-series analysis of lagged independent variables used publicly available observational data from US states during a 60-day period from March 8 to May 18, 2020. The behavioral measures were collected from anonymized cell phone or internet data for individuals in the US and compared with a baseline of January 3 to February 6, 2020. Estimates were also controlled for several state-level characteristics. Exposures: Days since school closure, days since a ban on gatherings of 10 or more people, and days since residents voluntarily conducted a 15% or more decline in time spent at work via Google Mobility data. Main Outcomes and Measures: The natural log of 7-day mean COVID-19 incidence and mortality. Results: During the study period, the rate of restaurant dining declined from 1 year earlier by a mean (SD) of 98.3% (5.2%) during the study period. Time at work declined by a mean (SD) of 40.0% (7.9%); time at home increased by a mean (SD) of 15.4% (3.7%). In fully adjusted models, a delay of 1 day in implementing mandatory school closures was associated with a 3.5% reduction (incidence rate ratio [IRR], 0.965; 95% CI, 0.946-0.984) in incidence, whereas each day of delay in behavioral change was associated with a 9.3% reduction (IRR, 0.907; 95% CI, 0.890-0.925) in incidence. For mortality, each day of delay in school closures was associated with a subsequent 3.8% reduction (IRR, 0.962; 95% CI, 0.926-0.998), and each day of delay in behavioral change was associated with a 9.8% reduction (IRR, 0.902; 95% CI, 0.869-0.936). Simulations suggest that a 2-week delay in school closures alone would have been associated with an additional 23 000 (95% CI, 2000-62 000) deaths, whereas a 2-week delay in voluntary behavioral change with school closures remaining the same would have been associated with an additional 140 000 (95% CI, 65 000-294 000) deaths. Conclusions and Relevance: In light of the harm to children of closing schools, these findings suggest that policy makers should consider better leveraging the public's willingness to protect itself through voluntary behavioral change.","","","","Center for Health Advancement, Department of Health Policy and Management, Fielding School of Public Health at University of California, Los Angeles.","en","Research Article","","","","","","",""
"Preprint Manuscript","Book L","","Tax Administration and Racial Justice: The Illegal Denial Of Tax Based Pandemic Relief To The Nation's Incarcerated","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-21","2021-04-02","","","","https://papers.ssrn.com/abstract=3790092;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3790092;http://dx.doi.org/10.2139/ssrn.3790092;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790092","10.2139/ssrn.3790092","","","","","In the midst of a devastating pandemic that would sicken millions, kill hundreds of thousands, and cause widespread financial distress, Congress passed the Coronavirus Aid, Relief, and Economic Security (CARES) Act. CARES provided for the IRS to deliver up to $1,200 for adults and $500 for dependent children. It was ostensibly structured as a refundable credit to be claimed on a 2020 tax return, but with a twist. The statute authorized the IRS to pay it in advance, even to those who did not have a tax return filing obligation, and to do so as “rapidly as possible.” While there were some problems, the IRS generally did remarkably well, and within six months it had delivered about 160 million payments totaling over $270 billion.This Essay addresses one of those exceptional problems: it involves the IRS’s unexplained change in position on the eligibility of those incarcerated in our nation’s federal, state, and local prisons and jails. At first, the incarcerated, just like other Americans suffering the effects of the pandemic, received the money that they were entitled to receive under the CARES legislation. That changed. In early May of 2020, the IRS announced on its web page that those who were incarcerated were not eligible for immediate cash benefits, worked with prison officials to claw back payments it had made, and stopped in their tracks hundreds of thousands of payments that it had not yet made. By October, the government faced a complete rebuke of its policy in Scholl v Mnuchin, a class action suit that held that the IRS’s actions were contrary to law and arbitrary and capricious under the Administrative Procedure Act. By looking at the IRS actions that led to Scholl v Mnuchin, this Essay explores the relationship of tax administration and racial justice. It reveals how tax administration can normalize and reinforce patterns of racial inequality through the presence of racialized administrative burdens. Finally, this Essay then considers how the IRS’s actions with respect to restricting payments to the incarcerated population can offer lessons to minimize the risk that future IRS actions will harm people of color, especially given the IRS’s role in delivering benefits.","IRS, pandemic, incarcerated, refundable credit, administrative burdens, racialized burdens","","","","","","","","","","","","South Carolina Law Review"
"Journal Article","Mwananyanda L,Gill CJ,MacLeod W,Kwenda G,Pieciak R,Mupila Z,Lapidot R,Mupeta F,Forman L,Ziko L,Etter L,Thea D","","Covid-19 deaths in Africa: prospective systematic postmortem surveillance study","BMJ","BMJ ","2021","372","","n334","COVID Tracking Project","","","","bmj.com","","","","","2021-02-17","","","0959-8138","1756-1833","http://dx.doi.org/10.1136/bmj.n334;https://www.ncbi.nlm.nih.gov/pubmed/33597166;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887952;https://www.bmj.com/lookup/pmidlookup?view=long&pmid=33597166;https://www.bmj.com/content/372/bmj.n334;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887952/","10.1136/bmj.n334","33597166","","","PMC7887952","OBJECTIVE: To directly measure the fatal impact of coronavirus disease 2019 (covid-19) in an urban African population. DESIGN: Prospective systematic postmortem surveillance study. SETTING: Zambia's largest tertiary care referral hospital. PARTICIPANTS: Deceased people of all ages at the University Teaching Hospital morgue in Lusaka, Zambia, enrolled within 48 hours of death. MAIN OUTCOME MEASURE: Postmortem nasopharyngeal swabs were tested via reverse transcriptase quantitative polymerase chain reaction (PCR) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Deaths were stratified by covis-19 status, location, age, sex, and underlying risk factors. RESULTS: 372 participants were enrolled between June and September 2020; PCR results were available for 364 (97.8%). SARS-CoV-2 was detected in 58/364 (15.9%) according to the recommended cycle threshold value of <40 and in 70/364 (19.2%) when expanded to any level of PCR detection. The median age at death among people with a positive test for SARS-CoV-2 was 48 (interquartile range 36-72) years, and 69% (n=48) were male. Most deaths in people with covid-19 (51/70; 73%) occurred in the community; none had been tested for SARS-CoV-2 before death. Among the 19/70 people who died in hospital, six were tested before death. Among the 52/70 people with data on symptoms, 44/52 had typical symptoms of covid-19 (cough, fever, shortness of breath), of whom only five were tested before death. Covid-19 was identified in seven children, only one of whom had been tested before death. The proportion of deaths with covid-19 increased with age, but 76% (n=53) of people who died were aged under 60 years. The five most common comorbidities among people who died with covid-19 were tuberculosis (22; 31%), hypertension (19; 27%), HIV/AIDS (16; 23%), alcohol misuse (12; 17%), and diabetes (9; 13%). CONCLUSIONS: Contrary to expectations, deaths with covid-19 were common in Lusaka. Most occurred in the community, where testing capacity is lacking. However, few people who died at facilities were tested, despite presenting with typical symptoms of covid-19. Therefore, cases of covid-19 were under-reported because testing was rarely done not because covid-19 was rare. If these data are generalizable, the impact of covid-19 in Africa has been vastly underestimated.","","","","Contributed equally. Department of Global Health, Boston University School of Public Health, Boston, MA 02118, USA. Right To Care - Zambia. Department of Biomedical Sciences, University of Zambia, Lusaka, Zambia. ZPRIME Molecular Laboratory, University Teaching Hospital, Lusaka, Zambia. Department of Global Health, Boston University School of Public Health, Boston, MA, USA. Division of Internal Medicine, Infectious Diseases Section, University Teaching Hospital, Lusaka, Zambia. Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA.","en","Research Article","","","","","","",""
"Journal Article","Snyder J,Ballakrishnen S,Tita G,Owens E,Decaro J,Sandoval J,Scurich N,Muñiz A,Hipp J,Lynch M,Others","","Transmitting Desire: An Experiment on a Novel Measure of Gun Desirability in a Pandemic","jlsola.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.jlsola.com/files/2021.02.11_preprint/2021.02.11%20preprint%20manuscript.pdf","","","","","","Page 1. Preprint – Transmitting Desire | Sola 2021 1 TITLE Transmitting Desire: An Experiment on a Novel Measure of Gun Desirability in a Pandemic ACKNOWLEDGEMENTS I am deeply grateful to Bryan Sykes for his mentorship, good humor, and belief in this project …","","","","","","","","","","","","",""
"Thesis","Μαραγκού Ε","","Πανδημία COVID-19 και Ελληνικός Τουρισμός. Στρατηγικός Σχεδιασμός Αντιμετώπισης των Προκυπτουσών Δυσχερειών","","","2021","","","","COVID Tracking Project","","","","Πρόγραμμα Δημόσιας Διοίκησης, Σχολή Οικονομικών Επιστημών και Διοίκησης, Πανεπιστήμιο Νεάπολις Πάφου","","","","","2021-01","2021-04-02","","","","http://hephaestus.nup.ac.cy/handle/11728/11671;http://hephaestus.nup.ac.cy/bitstream/handle/11728/11671/E.MARAGKOU-Fulltext.pdf?sequence=1&isAllowed=y","","","","","","Ο τουρισμός αποτελεί μια ιδιαίτερα σημαντική οικονομική δραστηριότητα για πολλά κράτη, όπως η Ελλάδα, όντας ένας από τους βασικούς τομείς της οικονομικής δραστηριότητας και παρέχοντας ένα σημαντικό μέρος του συνολικού ΑΕΠ αυτών, είτε άμεσα είτε έμμεσα. Η πρόσφατη πανδημία που μαστίζει την υφήλιο από το Μάρτιο του 2020 εξαιτίας του ιού SARS-CoV-2 πέραν των σημαντικών επιπτώσεων στην ανθρώπινη υγεία είχε σαν αποτέλεσμα και σημαντικές επιπτώσεις στην οικονομική δραστηριότητα. Η τελευταία αποτελεί απόρροια τόσο του προφανούς φόβου για την υγεία αλλά και των αναγκαστικών μέτρων περιορισμού των μετακινήσεων και κοινωνικής αποστασιοποίησης που τέθηκαν σε ισχύ για τον περιορισμό της μετάδοσης του ιού και την μείωση των κρουσμάτων του. Σε αυτό το πλαίσιο, και με δεδομένη την άμεση σύνδεση του τόσο με την ψυχολογία των ανθρώπων όσο και με τον τομέα των μεταφορών και ταξιδιών, ο τουρισμός ήταν από τους τομείς που δέχθηκαν τα πιο σοβαρά πλήγματα. Στην παρούσα εργασία εξετάζεται μέσω πρωτογενούς και δευτερογενούς έρευνας η στρατηγική που αξιοποιήθηκε στην Ελλάδα για το άνοιγμα του τουρισμού την καλοκαιρινή περίοδο του 2020, εν μέσω της πανδημίας. Τα στοιχεία από τους αρμόδιους φορείς που αξιοποιήθηκαν κατά την δευτερογενή έρευνα δείχνουν ότι εν γένει το άνοιγμα του τουρισμού υπό τις συνθήκες που έλαβε χώρα δεν είχε σχεδιαστεί σωστά και δεν βοήθησε στην λειτουργία του τομέα. Τα στοιχεία της πρωτογενούς έρευνας σε δείγμα 160 εργαζομένων ή ιδιοκτητών σε επιχειρήσεις εστίασης και τουρισμού, επιβεβαίωσαν τα ευρήματα της δευτερογενούς έρευνας ήτοι ότι ο σχεδιασμός είχε αρρυθμίες και παραλείψεις. Στα πλαίσια αυτά και αξιοποιώντας τα στοιχεία της ανάλυσης τόσο της πρωτογενούς όσο και της δευτερογενούς έρευνας, καταρτίστηκε και προτάθηκε το πλαίσιο του στρατηγικού σχεδιασμού για την λειτουργία του τουρισμού κατά τις αντίστοιχες περιόδους του 2021.","COVID-19; πανδημία; τουρισμός; στρατηγικός σχεδιασμός; Thesis","","","","","","","","","","","Πρόγραμμα Δημόσιας Διοίκησης, Σχολή Οικονομικών Επιστημών και Διοίκησης, Πανεπιστήμιο Νεάπολις Πάφου",""
"Journal Article","Powell K,Meyers C","","Guidance for Medical Ethicists to Enhance Social Cooperation to Mitigate the Pandemic","HEC Forum","HEC forum: an interdisciplinary journal on hospitals' ethical and legal issues","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-02-15","","","0956-2737","1572-8498","http://dx.doi.org/10.1007/s10730-021-09445-9;https://www.ncbi.nlm.nih.gov/pubmed/33587216;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882860;https://dx.doi.org/10.1007/s10730-021-09445-9;https://link.springer.com/article/10.1007/s10730-021-09445-9","10.1007/s10730-021-09445-9","33587216","","","PMC7882860","The Covid-19 pandemic has presented major challenges to society, exposing preexisting ethical weaknesses in the modern social fabric's ability to respond. Distrust in government and a lessened authority of science to determine facts have both been exacerbated by the polarization and disinformation enhanced by social media. These have impaired society's willingness to comply with and persevere with social distancing, which has been the most powerful initial response to mitigate the pandemic. These preexisting weaknesses also threaten the future acceptance of vaccination and contact tracing, two other tools needed to combat epidemics. Medical ethicists might best help in this situation by promoting truth-telling, encouraging the rational adjudication of facts, providing transparent decision-making and advocating the virtue of cooperation to maximize the common good. Those interventions should be aimed at the social level. The same elements of emphasizing cooperation and beneficence also apply to the design of triage protocols for when resources are overwhelmed. A life-stages approach increases beneficence and reduces harms. Triage should be kept as simple and straightforward as reasonably possible to avoid unwieldly application during a pandemic.","COVD-19; Triage committee; Virtue Ethics","","","Saint Louis University, Saint Louis, MO, USA. kpowell@alum.mit.edu. California State University, Bakersfield, Kegley Institute of Ethics, Bakersfield, CA, USA.","en","Research Article","","","","","","",""
"Journal Article","Arnold C","","Learning to treat covid-19","New Sci.","New scientist ","2021","249","3321","41-45","COVID Tracking Project","","","","Elsevier","","","","","2021-02-13","","","0262-4079","","https://www.sciencedirect.com/science/article/pii/S0262407921002426;http://dx.doi.org/10.1016/S0262-4079(21)00242-6;https://www.sciencedirect.com/science/article/pii/S0262407921002426?casa_token=8QdKGGf1vrYAAAAA:P41OPGtTRWScKqW4VmXo8M6m6xU3lQU0ktQtFuXKvgudzcxOfPQsmgESA6hDQ9VTL5IrU8FyrtM","10.1016/S0262-4079(21)00242-6","","","","","Changes in how we deal with serious coronavirus infections are helping more people survive. Carrie Arnold reports on what is now the gold-standard hospital treatment","","","","","","","","","","","","",""
"Journal Article","Lin KH,Aragão C,Dominguez G","","Firm Size and Employment during the Pandemic","Socius","Socius","2021","7","","2378023121992601","COVID Tracking Project","","","","SAGE Publications","","","","","2021-01-01","","","2378-0231","","https://doi.org/10.1177/2378023121992601;http://dx.doi.org/10.1177/2378023121992601;https://journals.sagepub.com/doi/abs/10.1177/2378023121992601;https://journals.sagepub.com/doi/pdf/10.1177/2378023121992601","10.1177/2378023121992601","","","","","Previous studies have established that firm size is associated with a wage premium, but the wage premium has declined in recent decades. The authors examine the risk for unemployment by firm size during the initial outbreak of coronavirus disease 2019 in the United States. Using both yearly and state-month variation, the authors find greater excess unemployment among workers in small enterprises than among those in larger firms. The gaps cannot be entirely attributed to the sorting of workers or to industrial context. The firm size advantage is most pronounced in sectors with high remotability but reverses in the sectors most affected by the pandemic. Overall, these findings suggest that firm size is linked to greater job security and that the pandemic may have accelerated prior trends regarding product and labor market concentration. They also point out that the initial policy responses did not provide sufficient protection for workers in small and medium-sized businesses.","","","","","","","","","","","","",""
"Journal Article","Yarsky P","","Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states","Math. Comput. Simul.","Mathematics and computers in simulation","2021","","","","COVID Tracking Project","","","","Elsevier","","","","","2021","","","0378-4754","","https://www.sciencedirect.com/science/article/pii/S0378475421000392;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7881743/;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7881743","","","","","pmc7881743","JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …","","","","","","","","","","","","",""
"Preprint Manuscript","Bick A,Blandin A,Mertens K","","Work from Home Before and After the COVID-19 Outbreak","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-11","2021-04-02","","","","https://papers.ssrn.com/abstract=3786142;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3786142;http://dx.doi.org/10.2139/ssrn.3786142;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3786142;https://www.dallasfed.org/-/media/documents/research/papers/2020/wp2017r2.pdf","10.2139/ssrn.3786142","","","","","Based on novel survey data, we document the evolution of commuting behavior in the U.S. over the course of the COVID-19 pandemic. Work from home (WFH) increased sharply and persistently after the outbreak, and much more so among some workers than others. Using theory and evidence, we argue that the observed heterogeneity in WFH transitions is consistent with potentially more permanent changes to work arrangements in some occupations, and not just temporary substitution in response to greater health risks. Consistent with increased WFH adoption, many more---especially higher-educated---workers expect to WFH in the future.","working from home, telecommuting, telework, remote work, COVID-19, pandemic","","","","","","","","","","","","Available at SSRN 3786142"
"Preprint Manuscript","Chyba M,Koniges A,Kunwar P,Lau W,Mileyko Y,Tong A","","COVID-19 Heterogeneity in Islands Chain Environment","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-12","","","","","http://arxiv.org/abs/2102.07646","","","2102.07646","","","As 2021 dawns, the COVID-19 pandemic is still raging strongly as vaccines finally appear and hopes for a return to normalcy start to materialize. There is much to be learned from the pandemic's first year data that will likely remain applicable to future epidemics and possible pandemics. With only minor variants in virus strain, countries across the globe have suffered roughly the same pandemic by first glance, yet few locations exhibit the same patterns of viral spread, growth, and control as the state of Hawai'i. In this paper, we examine the data and compare the COVID-19 spread statistics between the counties of Hawai'i as well as examine several locations with similar properties to Hawai'i.","","","","","","","","arXiv","2102.07646","q-bio.PE","","","arXiv [q-bio.PE]"
"Preprint Manuscript","Yu X,Lu L,Shen J,Li J,Xiao W,Chen Y","","RLIM: A recursive and latent infection model for COVID-19 prediction and turning point in United States","","","2021","","","","COVID Tracking Project","","Research Square","","","","","","","2021-02-10","2021-04-02","","","","https://www.researchsquare.com/article/rs-182025/latest.pdf;https://www.researchsquare.com/article/rs-182025/v1;http://dx.doi.org/10.21203/rs.3.rs-182025/v1","10.21203/rs.3.rs-182025/v1","","","","","Abstract Initially found at Hubei, Wuhan and identified as a novel virus of coronavirus family by WHO, COVID-19 has spread worldwide with an exponentially speed, causing millions of death and public fear. Currently, COVID19 has brought a secondary wave within U.S., India, Brazil and other parts of the world. However, its transmission, incubation, and recovery processes are still unclear from the medical, mathematical and pharmaceutical aspects. Classical Suspect-Infection-Recovery model has limitations to describe the dynamic behavior of COVID-19. Hence, it becomes necessary to introduce a recursive, latent model to predict the number of future COVID-19 infected cases in U.S. In this article, a dynamic model called RLIM based on classical SEIR model is proposed to predict the number of COVID-19 infections with a dynamic secondary infection rate ω in assumption. An intermediate state called SI is introduced between recovery and infection statues to record the number of secondary infected cases from a latent period of recovery. Compared with other models, RLIM fits historical recovery cases and utilizes them to predict future infections. Because RLIM utilizes multiple information sources, and provides error back propagation schematics, it is reasonable to assert that its predictions are more accurate and persuasive. Projections of four U.S. COVID19 states show that with the secondary infectious rate ω varies from 0.01 to 0.3 within a latent period of 14 days chosen, RLIM can predict the newly infected number from January 15 to February 15, 2021 with AFER lower to 14%. It also successfully estimates the turning point of New Yorks infections in January 2021, based on current data records.","","","","Shanghai Dianji University; University of California Merced","","","","","","","","","Research Square"
"Preprint Manuscript","Parker RW","","Why America’s Response to the COVID-19 Pandemic Failed: Lessons from New Zealand’s Success","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-28","2021-04-02","","","","https://papers.ssrn.com/abstract=3794725;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3794725;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3794725;https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=1528&context=law_papers","","","","","","COVID-19 is the ultimate test of administrative law and governance, as every country faces the common challenge of saving lives from a virulent pandemic at a manageable cost to the economy. Polls show that 48 percent of Americans think that COVID-19 posed an essentially impossible test and that the US has performed as well as most other countries in meeting the pandemic challenge. This Essay refutes that misperception. It shows that the U.S. COVID-19 mortality rate for 2020, adjusted for population, was more than twice as high as Canada’s and Germany’s; 40 times higher than Japan’s; 59 times higher than South Korea’s, and 207 times higher than New Zealand’s mortality rate despite over $2 trillion in U.S. deficit spending. In fact, U.S. performance at the level of South Korea, Australia, New Zealand, or Japan in containing the pandemic would have saved over 300,000 American lives in 2020 alone. This Essay then offers a detailed comparison of the COVID-19 response of the Trump Administration to that of New Zealand, which mounted a truly successful response. While some observers have dismissed New Zealand’s success as an artifact of good luck -- or of its geographic situation as a small, rural, island state -- this Essay offers evidence to suggest that these distinctions are of marginal importance compared to a more crucial contrast: New Zealand followed the pandemic containment “playbook” to the letter while in the United States the Trump Administration departed from that playbook at every turn. Moreover, New Zealand’s response was centrally planned and tightly managed while the U.S. response was incoherent and de-centralized. The evidence thus strongly suggests that the tragic disparity between America’s COVID-19 performance and New Zealand’s is primarily due -- not to geography or happenstance -- but to a stark contrast in the pandemic response strategy adopted by New Zealand’s Prime Minister Jacinda Ardern compared to that of President Trump. Leadership matters.","COVID-19, Administrative Law, Comparative Administrative Law","","","","","","","","","","","",""
"Journal Article","Anaele BI,Doran C,McIntire R","","Visualizing COVID-19 Mortality Rates and African-American Populations in the USA and Pennsylvania","J Racial Ethn Health Disparities","Journal of racial and ethnic health disparities","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-02-09","","","2196-8837","","http://dx.doi.org/10.1007/s40615-020-00897-2;https://www.ncbi.nlm.nih.gov/pubmed/33565050;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872308;https://link.springer.com/article/10.1007/s40615-020-00897-2","10.1007/s40615-020-00897-2","33565050","","","PMC7872308","The Centers for Disease Control and Prevention has identified African-Americans as having increased risk of COVID-19-associated mortality. Access to healthcare and related social determinants of health are at the core of this disparity. To explore the geographical links between race and COVID-19 mortality, we created descriptive maps of COVID-19 mortality rates in relation to the percentage of populations self-identifying as African-American across the USA, by state, and Pennsylvania (PA), by county. In addition, we used bivariate and logistic regression analyses to quantify the statistical relationship between these variables, and control for area-level demographic, healthcare access, and comorbidity risk factors. We found that COVID-19 mortality rates were generally higher in areas that had higher African-American populations, particularly in the northeast USA and eastern PA. These relationships were quantified through Pearson correlations showing significant positive associations at the state and county level. At the US state-level, percent African-American population was the only significant correlate of COVID-19 mortality rate. In PA at the county-level, higher percent African-American population was associated with higher COVID-19 mortality rate even after controlling for area-level confounders. More resources should be allocated to address high COVID-19 mortality rates among African-American populations.","African-American; COVID-19; GIS; Pennsylvania","","","College of Population Health, Thomas Jefferson University, Philadelphia, PA, USA. beverly.anaele@gmail.com. College of Population Health, Thomas Jefferson University, Philadelphia, PA, USA.","en","Research Article","","","","","","",""
"Journal Article","Woodland MB","","PA Joint Democratic Policy Committees & Women’s Health Caucus Testimony Briefing Materials Wednesday February 10th, 2021","pahouse.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://pahouse.com/files/Documents/Testimony/2021-02-10_094217__021021.MaternalHealthJointHearing.pdf","","","","","","Page 1. Maternal Health Amidst COVID-19 Joint Democratic Policy Committee, hosted by the Women's Health Caucus February 10, 2021 at 11 am Opening Remarks Senator Katie Muth, ​Senate Policy Chair Representative …","","","","","","","","","","","","",""
"Journal Article","Robertson LS","","Did people's behavior after receiving negative COVID-19 tests contribute to the spread?","J. Public Health ","Journal of public health ","2021","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2021-02-08","","","1741-3842","1741-3850","http://dx.doi.org/10.1093/pubmed/fdab010;https://www.ncbi.nlm.nih.gov/pubmed/33558889;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928719;https://academic.oup.com/jpubhealth/article-lookup/doi/10.1093/pubmed/fdab010;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928719/","10.1093/pubmed/fdab010","33558889","","","PMC7928719","BACKGROUND: Testing on demand for coronavirus disease (COVID-19) is hypothesized to increase spread of the virus as some persons who test negative falsely assume that they can engage in activities that increase spread. METHODS: Daily new COVID-19 hospitalization counts through 2020 from 25 countries that reported testing and hospitalizations were studied by regression of logarithms of new hospitalizations 14 days out against log(new hospitalizations on a given day), log(negative tests), log(positivity rate) and days since the first hospitalizations were reported. The regression coefficients were examined separately for periods in countries that were following three different testing policies. RESULTS: Corrected for the other factors, negative test numbers when tested on demand and tested if symptomatic only are associated with an increase in hospitalizations 14 days after the tests. When only the symptomatic and more vulnerable are tested, negative tests are associated with fewer hospitalizations 2 weeks out. CONCLUSIONS: A policy of testing only vulnerable populations, whether symptomatic or not, appears to avoid spreading the virus as a result of testing policy. False confidence of reduced risk among those who test negative may have contributed to the spread in countries that allowed testing on demand or testing only those who claimed to have symptoms.","corona virus; infectious disease testing; public policy; social behavior","","","Department of Environmental Health, Yale University School of Public Health, New Haven, CT 06510, USA.","en","Research Article","","","","","","",""
"Report","Atkeson A","","A Parsimonious Behavioral SEIR Model of the 2020 COVID Epidemic in the United States and the United Kingdom","","","2021","","","","COVID Tracking Project","","National Bureau of Economic Research","w28434","nber.org","","","","","2021-02-08","2021-04-02","","","","https://www.nber.org/system/files/working_papers/w28434/w28434.pdf;https://www.nber.org/papers/w28434;http://dx.doi.org/10.3386/w28434","10.3386/w28434","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Rebeiro PF,Aronoff DM,Smith MK","","The Impact of State Mask-Wearing Requirements on the Growth of COVID-19 Cases, Hospitalizations, and Deaths in the United States","Clin. Infect. Dis.","Clinical infectious diseases: an official publication of the Infectious Diseases Society of America","2021","","","","COVID Tracking Project","","","","Oxford University Press","","","","","2021-02-07","2021-04-02","","1058-4838","","https://academic.oup.com/cid/advance-article-abstract/doi/10.1093/cid/ciab101/6129930;http://dx.doi.org/10.1093/cid/ciab101;https://academic.oup.com/cid/advance-article-pdf/doi/10.1093/cid/ciab101/36334813/ciab101.pdf?casa_token=ApsY3hPuqVEAAAAA:aQHz0tcqznkIcgfEDxWtHa0eLs9MKUxlji85L_qNl_Foj4Fhohx0oa9XRpi_vw1CbkLnQ96U41PlQA","10.1093/cid/ciab101","","","","","Abstract. In ecologic analyses of US states, piecewise multivariable models showed lower post- vs. pre-mask requirement case-rate slopes, with -1.0% (95%CI: -1.","masks; covid-19; episode-based payment","","","","en","","","","","","","",""
"Journal Article","Bernasconi A,Grandi S","","A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic","Information","Information. An International Interdisciplinary Journal","2021","12","2","69","COVID Tracking Project","","","","Multidisciplinary Digital Publishing Institute","","","","","2021-02-06","2021-04-02","","1343-4500","","https://www.mdpi.com/989584;http://dx.doi.org/10.3390/info12020069;https://www.mdpi.com/2078-2489/12/2/69/pdf","10.3390/info12020069","","","","","Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.","","","","","en","","","","","","","",""
"Journal Article","Cramer EY,Lopez VK,Niemi J,George GE,Cegan JC,Dettwiller ID,England WP,Farthing MW,Hunter RH,Lafferty B,Linkov I,Mayo ML,Parno MD,Rowland MA,Trump BD,Wang L,Gao L,Gu Z,Kim M,Wang Y,Walker JW,Slayton RB,Johansson M,Biggerstaff M","","Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US","medRxiv","medRxiv","2021","","","","COVID Tracking Project","","","","lib.dr.iastate.edu","","","","","2021","2021-04-02","","","","https://lib.dr.iastate.edu/stat_las_pubs/315/;http://dx.doi.org/10.1101/2021.02.03.21250974;https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1317&context=stat_las_pubs","10.1101/2021.02.03.21250974","","","","","Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.","","","","","","","","","","","","",""
"Journal Article","Kao AC,Weiner SJ,Scharf A,Voigt L,Vardhana S,et al.","","Racial and Ethnic Health Equity in the US: Part","journalofethics.ama-assn.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://journalofethics.ama-assn.org/sites/journalofethics.ama-assn.org/files/2021-02/joe-2102.pdf","","","","","","… https://coronavirus. jhu. edu/map. html 4. The Covid Racial Data Tracker. Covid Tracking Project . Accessed October 16, 2020. https://covidtracking. com/race 5. Lu D. The true coronavirus toll in the US has already surpassed 200000. New York Times. August 12, 2020 …","","","","","","","","","","","","",""
"Journal Article","Bari A,Khubchandani A,Wang J,Heymann M,Coffee M","","COVID-19 early-alert signals using human behavior alternative data","Soc Netw Anal Min","Social network analysis and mining","2021","11","1","18","COVID Tracking Project","","","","Springer","","","","","2021-02-04","","","1869-5450","","http://dx.doi.org/10.1007/s13278-021-00723-5;https://www.ncbi.nlm.nih.gov/pubmed/33558823;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859099;https://link.springer.com/article/10.1007/s13278-021-00723-5?wt_mc=Internal.Event.1.SEM.ArticleAuthorIncrementalIssue&utm_source=ArticleAuthorIncrementalIssue&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorIncrementalIssue_20210207&error=cookies_not_supported&error=cookies_not_supported&code=8265bf70-a3bf-48e3-a422-f155242b66a9&code=953839bd-31dc-4747-81ea-f5fa9705f1e6","10.1007/s13278-021-00723-5","33558823","","","PMC7859099","Google searches create a window into population-wide thoughts and plans not just of individuals, but populations at large. Since the outbreak of COVID-19 and the non-pharmaceutical interventions introduced to contain it, searches for socially distanced activities have trended. We hypothesize that trends in the volume of search queries related to activities associated with COVID-19 transmission correlate with subsequent COVID-19 caseloads. We present a preliminary analytics framework that examines the relationship between Google search queries and the number of newly confirmed COVID-19 cases in the United States. We designed an experimental tool with search volume indices to track interest in queries related to two themes: isolation and mobility. Our goal was to capture the underlying social dynamics of an unprecedented pandemic using alternative data sources that are new to epidemiology. Our results indicate that the net movement index we defined correlates with COVID-19 weekly new case growth rate with a lag of between 10 and 14 days for the United States at-large, as well as at the state level for 42 out of 50 states with the exception of 8 states (DE, IA, KS, NE, ND, SD, WV, WY) from March to June 2020. In addition, an increasing caseload was seen over the summer in some southern US states. A sharp rise in mobility indices was followed by a sharp increase, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, and Texas. A sharp decline in mobility indices is often followed by a sharp decline, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, Texas, and New York. The digital epidemiology framework presented here aims to discover predictors of the pandemic's curve, which could supplement traditional predictive models and inform early warning systems and public health policies.","Alternative data sources; COVID-19; Digital epidemiology; Predictive analytics","","","Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY USA. Division of Infectious Diseases and Immunology, Grossman School of Medicine, New York University, New York, NY USA.","en","Research Article","","","","","","",""
"Journal Article","Habibzadeh P,Mofatteh M,Silawi M,Ghavami S,Faghihi MA","","Molecular diagnostic assays for COVID-19: an overview","Crit. Rev. Clin. Lab. Sci.","Critical reviews in clinical laboratory sciences","2021","","","1-20","COVID Tracking Project","","","","Taylor & Francis","","","","","2021-02-17","","","1040-8363","1549-781X","http://dx.doi.org/10.1080/10408363.2021.1884640;https://www.ncbi.nlm.nih.gov/pubmed/33595397;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898297;https://www.tandfonline.com/doi/full/10.1080/10408363.2021.1884640;https://www.tandfonline.com/doi/abs/10.1080/10408363.2021.1884640?casa_token=BmypHCdklKQAAAAA:g5mOJzMQ1_E2JDLQwIWbVQ6eFPIi2-B6AI9yhU15MlWoBlZwL9hBlvFZ3Zoq78dFaHyPl2FYE5_5aA;https://www.tandfonline.com/doi/pdf/10.1080/10408363.2021.1884640?casa_token=jyAkCSDNDBcAAAAA:H2lQzQwex8tw2FdfSCxuXUDleEFY5lejcAthw0zpIUSEWxdC8iv4FecxZZxq4SyWuJFmE9ThRDm_SQ","10.1080/10408363.2021.1884640","33595397","","","PMC7898297","The coronavirus disease 2019 (COVID-19) pandemic has highlighted the cardinal importance of rapid and accurate diagnostic assays. Since the early days of the outbreak, researchers with different scientific backgrounds across the globe have tried to fulfill the urgent need for such assays, with many assays having been approved and with others still undergoing clinical validation. Molecular diagnostic assays are a major group of tests used to diagnose COVID-19. Currently, the detection of SARS-CoV-2 RNA by reverse transcription polymerase chain reaction (RT-PCR) is the most widely used method. Other diagnostic molecular methods, including CRISPR-based assays, isothermal nucleic acid amplification methods, digital PCR, microarray assays, and next generation sequencing (NGS), are promising alternatives. In this review, we summarize the technical and clinical applications of the different COVID-19 molecular diagnostic assays and suggest directions for the implementation of such technologies in future infectious disease outbreaks.","COVID-19; Coronavirus; SARS-CoV-2; molecular diagnostic techniques; nucleic acid amplification techniques","","","Persian BayanGene Research and Training Center, Shiraz University of Medical Sciences, Shiraz, Iran. Sir William Dunn School of Pathology, University of Oxford, Oxford, UK. Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada. Center for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, FL, USA.","en","Research Article","","","","","","",""
"Preprint Manuscript","Bardey D,Fernández Sierra M,Gravel A","","Coronavirus and Social Distancing: Do Non-Pharmaceutical-Interventions Work (at Least) in the Short Run?","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-03","2021-04-02","","","","https://papers.ssrn.com/abstract=3778714;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3778714;http://dx.doi.org/10.2139/ssrn.3778714;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3778714;https://repositorio.uniandes.edu.co/bitstream/handle/1992/48377/dcede2021-04.pdf?sequence=1","10.2139/ssrn.3778714","","","","","Using detailed daily information covering 100 countries and an event-study approach, we estimate the short run effects of implementing Non-Pharmaceutical Interventions (NPIs) on the spread of the COVID-19 virus at the early stages of the pandemic. We study the impact of two NPIs – stay-at-home requirements and workplace closures – on three outcomes: daily residential and workplace mobility; the daily growth rate of cases; and the daily growth rate of fatalities. Acknowledging that we observe a mobility reduction in countries before they implemented NPIs, we find that immediately after NPIs were implemented, mobility declined by 0.2 standard deviation (SD), and two weeks afterwards it was down by 0.7 SDs. 25 days after the NPIs were implemented, the daily growth rate of cases and deaths was lower by 10% and 8.4% respectively. Our results reveal that between 53 and 72 percent of the reduction of the daily growth rate of cases and deaths associated with a reduction of mobility is caused by NPIs.","COVID-19, Non-Pharmaceutical Interventions, Pandemic","","","","","","","","","","","","Documento CEDE"
"Journal Article","Do WWC","","COVID-19: Health Equity Considerations and Racial and Ethnic Minority Groups","rcmi.rcm.upr.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://rcmi.rcm.upr.edu/node/680","","","","","","… COVIDView: A Weekly Surveillance Summary of US COVID-19 Activity. Other resources. · The COVID Tracking Project's The COVID Racial Data Trackerexternal icon. · Emory University's COVID-19 Health Equity Interactive Dashboardexternal icon. References …","","","","","","","","","","","","",""
"Journal Article","Kao AC","","Equity in Breath","AMA Journal of Ethics","AMA Journal of Ethics","2021","23","2","86-90","COVID Tracking Project","","","","American Medical Association","","","","","2021-02-01","2021-04-02","","2376-6980","","https://journalofethics.ama-assn.org/article/equity-breath/2021-02;http://dx.doi.org/10.1001/amajethics.2021.86.","10.1001/amajethics.2021.86.","","","","","What’s counted, how it’s counted, and who counts has life and death implications for individuals and communities.","","","","","","","","","","","","",""
"Journal Article","Curran K","","Health Equity-Building Trust for Vaccine Rollout","chausa.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.chausa.org/cha-we-are-called/stories/detail/health-equity---building-trust-for-vaccine-rollout","","","","","","","","","","","","","","","","","","",""
"News Article","Lamb A","","Informational Readers Part 2: Web-based Story Maps in the Social Sciences","Bowie","","2021","48","3","58-63","COVID Tracking Project","","","","search.proquest.com","","","","","2021-02","","","","","https://search.proquest.com/openview/89cf85a071a827b5b6a7849468176699/1?pq-origsite=gscholar&cbl=38018","","","","","","… Ask youth to compare the 1918 tragedy with the 2020 events. Use The COVID Tracking Project for access to quality data sources. 2) FOcUS ON tOURING Particularly during the pandemic, students aren't able to physically visit historical sites …","","","","","","Magazine Article","","","","","","",""
"Journal Article","Xu JJ,Chen JT,Belin TR,Brookmeyer RS,Suchard MA,Ramirez CM","","Racial and Ethnic Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: An Analysis of 45 States and the District of Columbia","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2021","18","6","2921","COVID Tracking Project","","","","Multidisciplinary Digital Publishing Institute","","","","","2021-03-12","2021-04-02","","1661-7827","","https://www.mdpi.com/1660-4601/18/6/2921;http://dx.doi.org/10.3390/ijerph18062921;https://www.mdpi.com/1660-4601/18/6/2921/pdf","10.3390/ijerph18062921","","","","","The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios—anchoring comparisons to non-Hispanic Whites—in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of 30 December 2020. Using a novel Monte Carlo simulation procedure to perform estimation, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, estimated disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.","","","","","en","","","","","","","",""
"Journal Article","Kumar S,Xu C,Ghildayal N,Chandra C,Yang M","","Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic","Ann. Oper. Res.","Annals of Operations Research","2021","","","1-29","COVID Tracking Project","","","","Springer","","","","","2021-01-29","","","0254-5330","","http://dx.doi.org/10.1007/s10479-021-03955-y;https://www.ncbi.nlm.nih.gov/pubmed/33531729;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843901;https://link.springer.com/article/10.1007/s10479-021-03955-y","10.1007/s10479-021-03955-y","33531729","","","PMC7843901","Influenza and COVID-19 are infectious diseases with significant burdens. Information and awareness on preventative techniques can be spread through the use of social media, which has become an increasingly utilized tool in recent years. This study developed a dynamic transmission model to investigate the impact of social media, particularly tweets via the social networking platform, Twitter on the number of influenza and COVID-19 cases of infection and deaths. We modified the traditional Susceptible-Exposed-Infectious-Recovered (SEIR-V) model with an additional social media component, in order to increase the accuracy of transmission dynamics and gain insight on whether social media is a beneficial behavioral intervention for these infectious diseases. The analysis found that social media has a positive effect in mitigating the spread of contagious disease in terms of peak time, peak magnitude, total infected, and total death; and the results also showed that social media's effect has a non-linear relationship with the reproduction number R 0 and it will be amplified when a vaccine is available. The findings indicate that social media is an integral part in the humanitarian logistics of pandemic and emergency preparedness, and contributes to the literature by informing best practices in the response to similar disasters.","COVID-19; Disaster preparedness; Epidemiological modeling; Humanitarian operations; Infectious disease; Influenza; Social media data","","","Department of Operations and Supply Chain Management, Opus College of Business, University of St. Thomas, Mail # SCH 435, Minneapolis, MN 55403 USA. School of Engineering, University of St. Thomas, Mail Stop OSS100, 2115 Summit Ave., St. Paul, MN 55105 USA. Harvard University - T.H. Chan School of Public Health, Cambridge, MA USA. Department of Management Studies, College of Business Administration, University of Michigan - Dearborn, Dearborn, USA. Department of Operations and Supply Chain Management, Opus College of Business, University of St. Thomas, Mail # TMH 445, Minneapolis, MN 55403 USA.","en","Research Article","","","","","","",""
"Journal Article","Baum F,Freeman T,Musolino C,Abramovitz M,De Ceukelaire W,Flavel J,Friel S,Giugliani C,Howden-Chapman P,Huong NT,London L,McKee M,Popay J,Serag H,Villar E","","Explaining covid-19 performance: what factors might predict national responses?","BMJ","BMJ ","2021","372","","n91","COVID Tracking Project","","","","bmj.com","","","","","2021-01-28","","","0959-8138","1756-1833","http://dx.doi.org/10.1136/bmj.n91;https://www.ncbi.nlm.nih.gov/pubmed/33509924;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842256;http://www.bmj.com/lookup/pmidlookup?view=long&pmid=33509924;https://www.bmj.com/lookup/pmidlookup?view=long&pmid=33509924;https://www.bmj.com/content/372/bmj.n91.full","10.1136/bmj.n91","33509924","","","PMC7842256","Skip to main content. Intended for healthcare professionals …","","","","Flinders University College of Medicine and Public Health, Southgate Institute for Health, Society and Equity, Australia. City University of New York, Silberman School of Social Work, USA. Third World Health Aid, Belgium. Australian National University, Menzies Centre for Health Governance, School of Regulation and Global Governance (RegNet), Australia. Universidade Federal do Rio Grande do Sul, Faculdade de Medicina, Brazil. University of Otago Wellington, Department of Public Health, New Zealand. Hanoi University of Public Health, Vietnam. University of Cape Town, School of Public Health and Family, South Africa. London School of Hygiene and Tropical Medicine, ECOHOST, UK. Lancaster University Division of Health Research, Institute for Health Research, UK. Universidad Peruana Cayetano Heredio, Peru.","en","Research Article","","","","","","",""
"Preprint Manuscript","Lake J,Nie J","","Did COVID-19 Cost Trump the Election?","","","2021","","","","COVID Tracking Project","","","","","","","","","2021","2021-04-02","","","","https://papers.ssrn.com/abstract=3774663;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3774663;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774663;https://www.cesifo.org/DocDL/cesifo1_wp8856.pdf","","","","","","A common narrative is that COVID-19 cost Trump re-election. We do not find supporting evidence; if anything, the pandemic helped Trump. However, we find substantial evidence that voters abandoned Trump in counties with large increases in health insurance coverage since the Affordable Care Act, presumably fearing the roll-back of such expansion. Absent this effect, our estimates imply Trump would been on the precipice of re-election by winning Georgia, Arizona, Nevada, and only losing Wisconsin by a few thousand votes. Finally, while US trade war tariffs boosted Trump’s support, foreign trade war tariffs and US agricultural subsidies had little effect.","","","","","","","","","","","","",""
"Preprint Manuscript","Crocco G","","The Unequal Opportunity to Be Healthy: How Whole Foods Market's Wellness Program and Similar Models Create an Issue of Disparate Impact","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-25","2021-04-02","","","","https://papers.ssrn.com/abstract=3773697;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3773697;http://dx.doi.org/10.2139/ssrn.3773697;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3773697","10.2139/ssrn.3773697","","","","","With the enactment of the Affordable Care Act, employers were encouraged to develop wellness programs to promote the health of employees and combat rising healthcare costs. This Article uses the wellness program of Whole Foods Market as a case study in how these programs have a disparate impact on racial minority populations and thus serve to perpetuate racial stratification and inequality by offering better terms and/or conditions of employment to employees who exhibit desired health statuses.A claim of disparate impact under Title VII of the Civil Rights Act is established if a party demonstrates that the business practice in question, while neutral on its face, has a disparate impact on the basis of race, color, religion, sex, or national origin and the employer fails to demonstrate that the business practice is related to the job in question or is consistent with business necessity. Title VII does not prohibit employment practices that have a disparate impact on a protected class if the challenged policy differentiates on the basis of a conduct-based attribute thought to be within the control of the employee, i.e., a mutable characteristic.According to organizations such as the World Health Organization, the American Medical Association, and the American Psychological Association, socioeconomic status is a consistent and reliable predictor of a vast array of outcomes in a person’s life, including physical health. Given long-standing socioeconomic inequality in the United States, racial minority populations are statistically more likely to exhibit negative health statuses. This unfortunate reality has been highlighted by the disproportional impact the COVID-19 pandemic has had on racial minority populations. This Article addresses how Title VII fails to fulfill its mandate of employment equality by specifically addressing the lenient review of an employer’s business-necessity defense and how the consideration of health statuses as wholly mutable characteristics is inconsistent with reality given an analysis of behavioral and social determinants of health.This Article then calls for a reevaluation of the lenient review of employer’s business-necessity defense and a reframing of the mutability of health statuses to ensure Title VII fulfills its congressional mandate of equal employment opportunities for all.","Wellness Program, Affordable Care Act, COVID-19, Civil Rights Act, Title VII, Disparate Impact, Health Inequality","","","","","","","","","","","","Available at SSRN 3773697"
"Journal Article","Quan D,Luna Wong L,Shallal A,Madan R,Hamdan A,Ahdi H,Daneshvar A,Mahajan M,Nasereldin M,Van Harn M,Opara IN,Zervos M","","Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19","J. Gen. Intern. Med.","Journal of general internal medicine","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-01-27","","","0884-8734","1525-1497","http://dx.doi.org/10.1007/s11606-020-06527-1;https://www.ncbi.nlm.nih.gov/pubmed/33506402;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840076;https://dx.doi.org/10.1007/s11606-020-06527-1;https://link.springer.com/article/10.1007/s11606-020-06527-1","10.1007/s11606-020-06527-1","33506402","","","PMC7840076","BACKGROUND: The impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19). OBJECTIVE: To evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19. DESIGN: Retrospective cohort study. SETTING: Four hospitals in an integrated health system serving southeast Michigan. PARTICIPANTS: Adult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction. MAIN MEASURES: Patient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smoking status, and living environment. KEY RESULTS: Black patients lived in significantly poorer neighborhoods than White patients (median income: $34,758 (24,531-56,095) vs. $63,317 (49,850-85,776), p < 0.001) and were more likely to have Medicaid insurance (19.4% vs. 11.2%, p < 0.001). Patients from neighborhoods with lower median income were significantly more likely to require IMV (lowest quartile: 25.4%, highest quartile: 16.0%, p < 0.001) and ICU admission (35.2%, 19.9%, p < 0.001). After adjusting for age, sex, race, and comorbidities, higher neighborhood income ($10,000 increase) remained a significant negative predictor for IMV (OR: 0.95 (95% CI 0.91, 0.99), p = 0.02) and ICU admission (OR: 0.92 (95% CI 0.89, 0.96), p < 0.001). CONCLUSIONS: Neighborhood disadvantage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.","COVID-19; disadvantage; disparities; race; socioeconomic status","","","Wayne State University School of Medicine, Detroit, MI, USA. Department of Infectious Disease, Henry Ford Hospital, Detroit, MI, USA. Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA. Department of Internal Medicine, Internal Medicine-Pediatrics Section, Wayne State University School of Medicine, Detroit, MI, USA. Global Affairs Professor of Medicine, Assistant Dean Wayne State University School of Medicine, MI, Detroit, USA. mzervos1@hfhs.org. Infectious Diseases, Division Head Henry Ford Health System, MI, Detroit, USA. mzervos1@hfhs.org.","en","Research Article","","","","","","",""
"Preprint Manuscript","Achiume T,Carbado DW","","Critical Race Theory Meets Third World Approaches to International Law","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-26","2021-04-02","","","","https://papers.ssrn.com/abstract=3773735;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3773735;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3773735","","","","","","By and large, Critical Race Theory (CRT) and Third World Approaches to International Law (TWAIL) exist in separate epistemic universes. This Article argues that the borders between these two fields are unwarranted. Specifically, the Article articulates six parallel ways in which CRT and TWAIL have exposed and challenged the racial dimensions of United States law and international law, respectively. It explores the related ways in which both CRT scholars and TWAIL scholars have: contested the legalization of white supremacy; marked and problematized the degree to which regimes of inclusion can operate as technologies of exclusion; staged important if non-identical critiques of colorblindness; engaged and repudiated neoliberal claims about racialized social responsibility and agency; confronted perceptions that both literatures exist outside the boundaries of the presumptively neutral scholarly conventions of constitutional law and international law, engendering either criticism or willful dis-attention or non-engagement by mainstream scholars; and remained invested in reconstruction and transformation of and within law, seeking to maximize its emancipatory potential for racial justice and equality even while remaining clear-eyed about the limits and costs of such engagement and the need to effectuate change in other arenas, such as social movements.","Critical Race Theory (CRT), Third World Approaches to International Law (TWAIL), international law, constitutional law, racial justice, social responsibility","","","","","","","","","","","","forthcoming 2021), UCLA School of Law …"
"Website","Kurita K,Managi S","","[No title]","","","2021","","","","COVID Tracking Project","","","","researchgate.net","","","","","2021","2021-04-02","","","","https://www.researchgate.net/profile/Kenichi_Kurita/publication/348807187_Evolution_of_self-restraint_behavior_COVID-19_and_stigma/links/601157d345851517ef1a9afe/Evolution-of-self-restraint-behavior-COVID-19-and-stigma.pdf","","","","","","… implemented legally enforceable behavioral restrictions. The United States has the highest number of cases worldwide as of October 2, 2020, with 7.4 million infected and 211,000 dead (The COVID Tracking Project , 2020). New York …","","","","","","","","","","","","",""
"Journal Article","Zhan F","","COVID-19 Case Number Prediction Utilizing Dynamic Clustering With Polynomial Regression","2021 IEEE 11th Annual Computing and","","2021","","","","COVID Tracking Project","","","","ieeexplore.ieee.org","","","","","2021","","","","","https://ieeexplore.ieee.org/abstract/document/9375966/?casa_token=U74XIGMkZ0wAAAAA:c75fg-CCVHtHe-1a8OQp1udqawmrDLuHByvi8GuDhs1NvN2bMd6lTQRxMJNrDEnR1fV9HNOnygM;https://ieeexplore.ieee.org/iel7/9375825/9375905/09375966.pdf?casa_token=K4DFCGowyAUAAAAA:cjyhK7yVIGWBFWO9d5fRmk5p4CYXFYoK8iJ7PX0NG_LJgWjob-y9pucqBja3U37MQiz4e5--_u4","","","","","","… II. DATA PREPROCESSING Two datasets were used in this project: • The primary dataset, obtained from The COVID Tracking Project , tracks daily COVID-19 statistics for 50 US states (and Washington DC) in 2020 [2]. Over 40 attributes are present in the dataset, but …","","","","","","","","","","","","",""
"Preprint Manuscript","Dincer OC","","Trust in Government and Compliance with Stay at Home Orders in American States","","","2021","","","","COVID Tracking Project","","","","","","","","","2021","2021-04-02","","","","https://papers.ssrn.com/abstract=3774677;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3774677;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774677;https://www.cesifo.org/DocDL/cesifo1_wp8869.pdf","","","","","","Trust in government is particularly important in implementing public health policies especially during crises such as the COVID-19 pandemic. This study investigates the effects of trust in government and compliance with stay at home orders using data from American states during the first wave of the pandemic. A system of four seemingly unrelated regression (SUR) equations covering four consecutive Saturdays starting with April 25 is estimated with maximum likelihood. The regression results indicate that people are more likely to comply with stay at home orders in more trusting states.","","","","","","","","","","","","",""
"Journal Article","Li L,Liu B,Liu SH,Ji J,Li Y","","Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study","J. Gen. Intern. Med.","Journal of general internal medicine","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-01-26","","","0884-8734","1525-1497","http://dx.doi.org/10.1007/s11606-020-06476-9;https://www.ncbi.nlm.nih.gov/pubmed/33501543;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837644;https://dx.doi.org/10.1007/s11606-020-06476-9;https://link.springer.com/article/10.1007/s11606-020-06476-9","10.1007/s11606-020-06476-9","33501543","","","PMC7837644","BACKGROUND: On April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level. OBJECTIVE: To assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY. DESIGN: A comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state. PARTICIPANTS: We analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020. INTERVENTION: The Executive Order on face mask use in NY. MAIN MEASURES: Daily numbers of COVID-19 confirmed cases and deaths. KEY RESULTS: The average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187-496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44-60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001). CONCLUSIONS: The Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.","COVID-19; epidemic; face mask; public health intervention","","","Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA. yan.li1@mountsinai.org. Department of Obstetrics, Gynaecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. yan.li1@mountsinai.org.","en","Research Article","","","","","","",""
"Preprint Manuscript","Kumar A,Sesham K,Narayan RK,Prasoon P,Kumari C,Kumar S,Kumar S,Pareek V,Shekhawat PS,Kant K,Kulandhasamy M,Pandey SN","","Host Vulnerability Factors Affecting Patient Outcomes in COVID-19: An Update","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-20","2021-04-02","","","","https://papers.ssrn.com/abstract=3769784;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3769784;http://dx.doi.org/10.2139/ssrn.3769784;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769784","10.2139/ssrn.3769784","","","","","The pandemic of the coronavirus disease, 2019 (COVID-19) has caused millions to suffer from the disease and to die globally. Global epidemiological statistics reflect, significantly more numbers of men, aged, those suffering from co-morbidities, and people facing socio-economic inequalities, such as, racial/ethnic minorities, have been affected by COVID-19. Why the disease affects more to these specific population groups is intriguing the researchers globally. Emerging literature in COVID-19 indicates crucial role of factors intrinsic to the host behind such poor outcomes in selected individuals. Our comprehensive review of existing literature unravels a range of host factors which could have decisive role in patient outcomes in COVID-19, in terms of contracting infections, disease severity, and mortality, such as: age, sex, co-morbidities, genetic and phenotypic factors (e.g. polymorphisms or mutations, blood group etc.), clinical and laboratory parameters (at the time of hospital admission), cross protection from previous respiratory virus infections and childhood vaccinations, gut-microbiome, life style, habits and behavior (smoking and substance abuse), and socio-economic and systemic inequalities. In this article, we discuss in brief the most definitive and updated evidence for each of these host factors.","COVID-19; SARS-CoV-2; host factors; disease risk; severity; mortality; patient outcomes","","","","","","","","","","","","Available at SSRN"
"Journal Article","Nguyen A,Zhao X,Lawson B,Jackson D","","Reporting from a Statistical Chaos: Journalistic Lessons from the First Year of Covid-19 Data and Science in the News","","","2021","","","","COVID Tracking Project","","","","eprints.bournemouth.ac.uk","","","","","2021","","","","","http://eprints.bournemouth.ac.uk/35109/1/Reporting%20from%20a%20statistical%20chaos.pdf","","","","","","Page 1. REPORTING FROM A STATISTICAL CHAOS: JOURNALISTIC LESSONS FROM THE FIRST YEAR OF COVID-19 DATA AND SCIENCE IN THE NEWS An Nguyen Xin Zhao Brendan Lawson Dan Jackson Page 2. Reporting from a Statistical Chaos Contents …","","","","","","","","","","","","",""
"Report","Jowers K,Timmins C,Bhavsar N,Hu Q,Marshall J","","Housing Precarity & the COVID-19 Pandemic: Impacts of Utility Disconnection and Eviction Moratoria on Infections and Deaths Across US Counties","","","2021","","","","COVID Tracking Project","","National Bureau of Economic Research","w28394","nber.org","","","","","2021-01-25","2021-04-02","","","","https://www.nber.org/system/files/working_papers/w28394/w28394.pdf;https://www.nber.org/papers/w28394;http://dx.doi.org/10.3386/w28394","10.3386/w28394","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Preprint Manuscript","Flaschel P,Galanis G,Tavani D,Veneziani R","","Pandemics and Aggregate Demand: A Framework for Policy Analysis","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-21","2021-04-02","","","","https://papers.ssrn.com/abstract=3770391;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3770391;http://dx.doi.org/10.2139/ssrn.3770391;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3770391;https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2021-01/12_2021_flaschel_galanis_tavani_veneziani.pdf","10.2139/ssrn.3770391","","","","","This paper studies the interaction between epidemiological dynamics and the dynamics of economic activity in a demand-driven model in the structuralist/post-Keynesian tradition. On the one hand, rising aggregate demand increases the contact rate and therefore the probability of exposure to a virus. On the other hand, rising infection lowers aggregate demand because of reduced household spending. The resulting framework is well-suited for policy analysis through numerical exercises. We show that, first, laissezfaire gives rise to sharp fluctuations in demand and infections before herd immunity is achieved. Second, absent any restrictions on economic activity, physical distancing measures have rather limited mitigating effects. Third, lockdowns are effective, especially at reducing death rates while buying time before a vaccine is available, at the cost of a slightly more pronounced downturn in economic activity compared with alternative policies. This casts some doubt on the so-called “lives versus livelihood” policy trade-off. However, we also highlight the importance of policies aimed at mitigating the effects of the epidemic on workers’ income.","pandemic, aggregate demand, distribution, public policy","","","","","","","","","","","",""
"Book Chapter","Kaur A,Mittal N,Khosla PK,Mittal M","Khosla PK,Mittal M,Sharma D,Goyal LM","Machine Learning Tools to Predict the Impact of Quarantine","","","2021","","","307-323","COVID Tracking Project","","","","Springer Singapore","Singapore","Predictive and Preventive Measures for Covid-19 Pandemic","","","2021","","9789813342361","","","https://doi.org/10.1007/978-981-33-4236-1_17;http://dx.doi.org/10.1007/978-981-33-4236-1_17;https://link.springer.com/chapter/10.1007/978-981-33-4236-1_17","10.1007/978-981-33-4236-1_17","","","","","The world of Industry 4.0 has been quarantined due to the outbreak of highly contagious coronavirus disease (COVID-19) which has been characterized as a pandemic by World Health Organization. Quarantine, which refers to strict isolation, helps to prevent the spread of the disease. However, the prediction and analysis of the impact of the quarantine in containing the disease still remain a challenge. Such predictions may provide assistance to the governments in preparing effective standard operating procedures which could help in mitigating the effects of the pandemic. For this purpose, the area of machine learning has been explored by the researchers to develop tools and technologies that could help in quantifying the effects of quarantine in several parts of the world. Various techniques such as deep learning algorithms, association rules, decision trees, predictive analytics, reinforcement learning and big data visualization are being used to help address the pandemic issue. This chapter provides insights into various machine learning tools and techniques which have been used to measure the impact of quarantine worldwide.","","","","","","","","","","","","",""
"Preprint Manuscript","Sinclair AH,Hakimi S,Stanley M,Adcock RA,Samanez-Larkin GR","","Pairing Facts with Imagined Consequences Improves Pandemic-Related Risk Perception","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01","","","","","psyarxiv.com/53a9f;http://dx.doi.org/10.31234/osf.io/53a9f;https://psyarxiv.com/53a9f/;https://psyarxiv.com/53a9f/download?format=pdf","10.31234/osf.io/53a9f","","","","","The COVID-19 pandemic reached staggering new peaks during an ongoing global resurgence at the end of 2020. Although public health guidelines initially helped to slow the spread of disease, widespread pandemic fatigue and prolonged harm to financial stability and mental wellbeing have contributed to this resurgence. In this late stage of the pandemic, it is clear that new interventions are needed to support long-term behavior change. Here, we examined subjective perceived risk about COVID-19, and the relationship between perceived risk and engagement in risky behaviors. In Study 1 (N = 303), we found that subjective perceived risk is inaccurate but predicts compliance with public health guidelines. In Study 2 (N = 760), we developed a multi-faceted intervention designed to realign perceived risk with actual risk. Participants completed one of three variants of an episodic simulation task; we expected that imagining a COVID-related scenario would increase the salience of risk information and enhance behavior change. Immediately following the episodic simulation, participants completed a risk estimation task with personalized feedback about local risk levels. We found that information prediction error, a measure of surprise, drove beneficial change in perceived risk and willingness to engage in risky activities. Imagining a COVID-related scenario beforehand enhanced the effect of prediction error on learning. Importantly, our intervention produced lasting effects that persisted after a 1-3 week delay. Overall, we describe a fast and feasible online intervention that effectively changed beliefs and intentions about risky behaviors.","COVID-19; episodic simulation; intervention; prediction error; psychology; public health; risk perception","","","","","","","","","","","",""
"Journal Article","Post LA,Lin JS,Moss CB,Murphy RL,Ison MG,Achenbach CJ,Resnick D,Singh LN,White J,Boctor MJ,Welch SB,Oehmke JF","","SARS-CoV-2 Wave Two Surveillance in East Asia and the Pacific: Longitudinal Trend Analysis","J. Med. Internet Res.","Journal of medical Internet research","2021","23","2","e25454","COVID Tracking Project","","","","jmir.org","","","","","2021-02-01","","","1439-4456","1438-8871","http://dx.doi.org/10.2196/25454;https://www.ncbi.nlm.nih.gov/pubmed/33464207;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857528;https://www.jmir.org/2021/2/e25454/;https://www.jmir.org/2021/2/e25454","10.2196/25454","33464207","","","PMC7857528","BACKGROUND: The COVID-19 pandemic has had a profound global impact on governments, health care systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more evident that additional SARS-CoV-2 surveillance is needed to track recent shifts, rates of increase, and persistence associated with the pandemic. OBJECTIVE: The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk, persistence, and weekly shifts, to better understand country risk for explosive growth and those countries who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed. We provide novel indicators to measure disease transmission. METHODS: Using a longitudinal trend analysis study design, we extracted 330 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in East Asia and the Pacific as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: The standard surveillance metrics for Indonesia, the Philippines, and Myanmar were concerning as they had the largest new caseloads at 4301, 2588, and 1387, respectively. When looking at the acceleration of new COVID-19 infections, we found that French Polynesia, Malaysia, and the Philippines had rates at 3.17, 0.22, and 0.06 per 100,000. These three countries also ranked highest in terms of jerk at 15.45, 0.10, and 0.04, respectively. CONCLUSIONS: Two of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration, and positive upwards jerk belong to French Polynesia, Malaysia, and the Philippines, and may result in explosive growth. While all countries in East Asia and the Pacific need to be cautious about reopening their countries since outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern is the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions.","Arellano-Bond estimator; Asia Pacific COVID-19; Asia Pacific public health surveillance; Asia Pacific surveillance metrics; Asian Pacific COVID-19 transmission acceleration; Asian Pacific SARS-CoV-2; Asian Pacific econometrics; Australia; Brunei; COVID-19; COVID-19 7-day lag; COVID-19 transmission deceleration; COVID-19 transmission jerk; Cambodia; China; East Asian Pacific COVID-19 surveillance system; Fiji; French Polynesia; GMM; Guam; Indonesia; Japan; Kiribati; Laos; Malaysia; Mongolia; Myanmar; New Caledonia; Pacific Asian COVID-19 transmission speed; Philippines; SARS-CoV-2; SARS-CoV-2 surveillance; dynamic panel data; generalized method of moments; generalized method of the moments; global COVID-19 surveillance; second wave; wave 2; wave two","","","Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Feinburg School of Medicine, Northwestern University, Chicago, IL, United States. Institute of Food and Agricultural Sciences, University of Florida, Gainsville, FL, United States. Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Division of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. International Food Policy Research Institute, Washington DC, DC, United States.","en","Research Article","","","","","","",""
"Journal Article","Rader B,White LF,Burns MR,Chen J,Brilliant J,Cohen J,Shaman J,Brilliant L,Kraemer MUG,Hawkins JB,Scarpino SV,Astley CM,Brownstein JS","","Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study","Lancet Digit Health","The Lancet. Digital health","2021","3","3","e148-e157","COVID Tracking Project","","","","Elsevier","","","","","2021-03","","","2589-7500","","http://dx.doi.org/10.1016/S2589-7500(20)30293-4;https://www.ncbi.nlm.nih.gov/pubmed/33483277;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817421;https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(20)30293-4;https://www.sciencedirect.com/science/article/pii/S2589750020302934","10.1016/S2589-7500(20)30293-4","33483277","","","PMC7817421","BACKGROUND: Face masks have become commonplace across the USA because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. Although evidence suggests that masks help to curb the spread of the disease, there is little empirical research at the population level. We investigate the association between self-reported mask-wearing, physical distancing, and SARS-CoV-2 transmission in the USA, along with the effect of statewide mandates on mask uptake. METHODS: Serial cross-sectional surveys were administered via a web platform to randomly surveyed US individuals aged 13 years and older, to query self-reports of face mask-wearing. Survey responses were combined with instantaneous reproductive number (Rt) estimates from two publicly available sources, the outcome of interest. Measures of physical distancing, community demographics, and other potential sources of confounding (from publicly available sources) were also assessed. We fitted multivariate logistic regression models to estimate the association between mask-wearing and community transmission control (Rt<1). Additionally, mask-wearing in 12 states was evaluated 2 weeks before and after statewide mandates. FINDINGS: 378 207 individuals responded to the survey between June 3 and July 27, 2020, of which 4186 were excluded for missing data. We observed an increasing trend in reported mask usage across the USA, although uptake varied by geography. A logistic model controlling for physical distancing, population demographics, and other variables found that a 10% increase in self-reported mask-wearing was associated with an increased odds of transmission control (odds ratio 3·53, 95% CI 2·03-6·43). We found that communities with high reported mask-wearing and physical distancing had the highest predicted probability of transmission control. Segmented regression analysis of reported mask-wearing showed no statistically significant change in the slope after mandates were introduced; however, the upward trend in reported mask-wearing was preserved. INTERPRETATION: The widespread reported use of face masks combined with physical distancing increases the odds of SARS-CoV-2 transmission control. Self-reported mask-wearing increased separately from government mask mandates, suggesting that supplemental public health interventions are needed to maximise adoption and help to curb the ongoing epidemic. FUNDING: Flu Lab, Google.org (via the Tides Foundation), National Institutes for Health, National Science Foundation, Morris-Singer Foundation, MOOD, Branco Weiss Fellowship, Ending Pandemics, Centers for Disease Control and Prevention (USA).","","","","Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA. SurveyMonkey, San Mateo, CA, USA. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA. Pandefense Advisors, San Francisco, CA, USA. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA; Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Harvard University, Boston, MA, USA. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Harvard University, Boston, MA, USA. Network Science Institute, Northeastern University, Boston, MA, USA; Santa Fe Institute, Santa Fe, NM, USA. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA; Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Harvard University, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA. Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Harvard University, Boston, MA, USA. Electronic address: john.brownstein@childrens.harvard.edu.","en","Research Article","","","","","","",""
"Preprint Manuscript","Verma R,Yabe T,Ukkusuri SV","","Mobility-based contact exposure explains the disparity of spread of COVID-19 in urban neighborhoods","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-07","","","","","http://arxiv.org/abs/2102.03698","","","2102.03698","","","The rapid early spread of COVID-19 in the U.S. was experienced very differently by different socioeconomic groups and business industries. In this study, we study aggregate mobility patterns of New York City and Chicago to identify the relationship between the amount of interpersonal contact between people in urban neighborhoods and the disparity in the growth of positive cases among these groups. We introduce an aggregate Contact Exposure Index (CEI) to measure exposure due to this interpersonal contact and combine it with social distancing metrics to show its effect on positive case growth. With the help of structural equations modeling, we find that the effect of exposure on case growth was consistently positive and that it remained consistently higher in lower-income neighborhoods, suggesting a causal path of income on case growth via contact exposure. Using the CEI, schools and restaurants are identified as high-exposure industries, and the estimation suggests that implementing specific mobility restrictions on these point-of-interest categories are most effective. This analysis can be useful in providing insights for government officials targeting specific population groups and businesses to reduce infection spread as reopening efforts continue to expand across the nation.","","","","","","","","arXiv","2102.03698","econ.GN","","","arXiv [econ.GN]"
"Journal Article","Dagliati A,Malovini A,Tibollo V,Bellazzi R","","Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview","Brief. Bioinform.","Briefings in bioinformatics","2021","22","2","812-822","COVID Tracking Project","","","","academic.oup.com","","","","","2021-03-22","","","1467-5463","1477-4054","http://dx.doi.org/10.1093/bib/bbaa418;https://www.ncbi.nlm.nih.gov/pubmed/33454728;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929411;https://academic.oup.com/bib/article-lookup/doi/10.1093/bib/bbaa418;https://academic.oup.com/bib/article-abstract/22/2/812/6103007;https://academic.oup.com/bib/article/22/2/812/6103007","10.1093/bib/bbaa418","33454728","","","PMC7929411","The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.","COVID-19 pandemic; Electronic Health Record; clinical research; data sharing; international initiatives","","","Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy. IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy.","en","Research Article","","","","","","",""
"Preprint Manuscript","Soucy JP,Buchan SA,Brown KA","","How should we present the epidemic curve for COVID-19?","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01","","","","","http://dx.doi.org/10.31219/osf.io/zfj8a;https://osf.io/preprints/zfj8a/;https://osf.io/zfj8a/download","10.31219/osf.io/zfj8a","","","","","Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of current response measures. Symptom onset date is commonly used to date cases on the epidemic curve in public health reports and dashboards. However, third-party trackers often plot cases on the epidemic curve by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, the epidemic curve for Ontario, Canada based on public reporting date showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend (the \"ghost trend\") is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as symptom onset date or sample collection date. This is because newly discovered cases are backdated, creating a perpetual downward trend in incidence due to incomplete data in the most recent days. Public reporting date is not subject to backdating bias and can be used to visualize real-time epidemic curves meant to inform the public and policy makers.","Communicable Diseases; COVID-19; Epidemics; Epidemiology; SARS-CoV-2","","","","","","","","","","","",""
"Journal Article","Kraemer MU,Scarpino SV,Marivate V,Gutierrez B,Xu B,Lee G,Hawkins JB,Rivers C,Pigott DM,Katz R,Brownstein JS","","Data curation during a pandemic and lessons learned from COVID-19","Nature Computational Science","Nature Computational Science","2021","1","1","9-10","COVID Tracking Project","","","","Nature Publishing Group","","","","","2021-01-14","2021-04-02","","2662-8457","2662-8457","https://www.nature.com/articles/s43588-020-00015-6;https://www.nature.com/articles/s43588-020-00015-6.pdf;http://dx.doi.org/10.1038/s43588-020-00015-6","10.1038/s43588-020-00015-6","","","","","Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.","","","","","en","","","","","","","",""
"Journal Article","Richter D","","War der Coronavirus-Lockdown notwendig?: Versuch einer wissenschaftlichen Antwort","","","2021","","","","COVID Tracking Project","","","","library.oapen.org","","","","","2021","","","","","https://library.oapen.org/handle/20.500.12657/47381;https://library.oapen.org/bitstream/handle/20.500.12657/47381/9783839455456.pdf?sequence=1","","","","","","Page 1. Page 2. Dirk Richter War der Coronavirus-Lockdown notwendig? Science Studies Page 3. Dirk Richter (Dr. phil. habil.), geb. 1962, ist Wissenschaftler am Departement Ge- sundheit der Berner Fachhochschule und Leiter …","","","","","","","","","","","","",""
"Journal Article","Gatto NM,Schellhorn H","","Optimal control of the SIR model in the presence of transmission and treatment uncertainty","Math. Biosci.","Mathematical biosciences","2021","333","","108539","COVID Tracking Project","","","","Elsevier","","","","","2021-03","","","0025-5564","1879-3134","http://dx.doi.org/10.1016/j.mbs.2021.108539;https://www.ncbi.nlm.nih.gov/pubmed/33460674;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833871;https://linkinghub.elsevier.com/retrieve/pii/S0025-5564(21)00002-X;https://www.sciencedirect.com/science/article/pii/S002555642100002X","10.1016/j.mbs.2021.108539","33460674","","","PMC7833871","The COVID-19 pandemic illustrates the importance of treatment-related decision making in populations. This article considers the case where the transmission rate of the disease as well as the efficiency of treatments is subject to uncertainty. We consider two different regimes, or submodels, of the stochastic SIR model, where the population consists of three groups: susceptible, infected and recovered and dead. In the first regime the proportion of infected is very low, and the proportion of susceptible is very close to 100the proportion of infected is moderate, but not negligible. We show that the first regime corresponds almost exactly to a well-known problem in finance, the problem of portfolio and consumption decisions under mean-reverting returns (Wachter, JFQA 2002), for which the optimal control has an analytical solution. We develop a perturbative solution for the second problem. To our knowledge, this paper represents one of the first attempts to develop analytical/perturbative solutions, as opposed to numerical solutions to stochastic SIR models.","COVID-19; Epidemics; SARS-CoV-2; SIR model; Stochastic optimal control","","","School of Community and Global Health, Claremont Graduate University, Claremont, CA 91711, United States of America. Electronic address: nicole.gatto@cgu.edu. Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, United States of America. Electronic address: Henry.Schellhorn@cgu.edu.","en","Research Article","","","","","","",""
"Preprint Manuscript","Palatella L,Vanni F,Lambert D","","A phenomenological estimate of the Covid-19 true scale from primary data","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-13","","","","","http://arxiv.org/abs/2101.05381","","","2101.05381","","","Estimation of prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of the Covid-19 disease. In fact, unveiling uncounted cases has fundamental implications for public policy interventions strategies. In the present work, we show a basic yet effective approach to estimate the actual number of people infected by Sars-Cov-2, by using epidemiological raw data reported by official health institutions in the largest EU countries and USA.","","","","","","","","arXiv","2101.05381","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Nowotny KM,Bailey Z,Brinkley-Rubinstein L","","The Contribution of Prisons and Jails to US Racial Disparities During COVID-19","Am. J. Public Health","American journal of public health","2021","111","2","197-199","COVID Tracking Project","","","","American Public Health Association","","","","","2021-02-01","","","0090-0036","","https://doi.org/10.2105/AJPH.2020.306040;http://dx.doi.org/10.2105/AJPH.2020.306040;https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2020.306040;https://search.proquest.com/openview/248543f0bf92c11d022b3fb7dd4f6d30/1.pdf?pq-origsite=gscholar&cbl=41804&casa_token=G6Z8cW3VC40AAAAA:UuYkhdwts125zpe4K-StBhGlmsvxlmtET2aX3n-6TsyZpMJK6pJT52v3mmnXouBS2gZ2fIKzfFw","10.2105/AJPH.2020.306040","","","","","… 3. Reinhart E, Chen DL. Incarceration and its disseminations: COVID-19 pandemic lessons from Chicago's Cook County jail. Health Aff (Millwood). 2020;39(8):1412–1418. https://doi.org/10.1377/ hlthaff.2020.00652 4. COVID Tracking Project . About the racial data tracker …","","","","","","","","","","","","",""
"Journal Article","Le K,Nguyen M","","The psychological burden of the COVID-19 pandemic severity","Econ. Hum. Biol.","Economics and human biology","2021","41","","100979","COVID Tracking Project","","","","Elsevier","","","","","2021-01-19","","","1570-677X","1873-6130","http://dx.doi.org/10.1016/j.ehb.2021.100979;https://www.ncbi.nlm.nih.gov/pubmed/33497964;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817435;https://linkinghub.elsevier.com/retrieve/pii/S1570-677X(21)00003-4;https://www.sciencedirect.com/science/article/pii/S1570677X21000034?casa_token=rxRYyUkOHmIAAAAA:3I06lNkM2GycYYGkV0lhmA0MvYWdcQWNeOA0k9wX8vHK-_X1qYk6hOP_UFFT-2xVscsuhoCgFc4","10.1016/j.ehb.2021.100979","33497964","","","PMC7817435","The alarming levels of spread and severity of COVID-19 have dominated global attention. In this time of crisis, there is an urgent need for studies identifying the linkages between the pandemic and social welfare. To help policymakers respond to the situation better, we investigate how the severity of the COVID-19 pandemic can condition people's psychological well-being. Employing the latest weekly panel data within an individual fixed effects framework, we uncover the damaging consequences of the COVID-19 severity, as measured by mortality rate, on the incidences of daily anxiety, worry, displeasure, and depression in the United States. Our work underlines the importance of public spending on mental health, both during and after the pandemic.","COVID-19; Mental health; Mortality rate; Psychological consequences","","","The Faculty of Economics and Public Management, Ho Chi Minh City Open University, Vietnam. Electronic address: kien.le@ou.edu.vn. The Faculty of Economics and Public Management, Ho Chi Minh City Open University, Vietnam. Electronic address: my.ngt@ou.edu.vn.","en","Research Article","","","","","","",""
"Journal Article","Bhosle M","","Alarming Health Inequalities in Impoverished Black and Brown NYC Neighborhoods Impacted by COVID-19","phrma.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://phrma.org/-/media/Project/PhRMA/PhRMA-Org/PhRMA-Org/PDF/G-I/Health-Inequities-COVID_Design_v2_12-16-20.pdf","","","","","","… Updated Oct 15, 2020. Accessed 10 December, 2020. Available at: https://www.apmresearchlab. org/covid/deaths-by-race [16] Racial Data Dashboard. The COVID Tracking Project . 2020. Reviewed 3 November 2020. Available at: https://covidtracking.com/race …","","","","","","","","","","","","",""
"Journal Article","Mast TC,Heyman D,Dasbach E,Roberts C,et al.","","Planning for monitoring the introduction and effectiveness of new vaccines using real-word data and geospatial visualization: An example using rotavirus …","Vaccine: X","","2021","","","","COVID Tracking Project","","","","Elsevier","","","","","2021","","","","","https://www.sciencedirect.com/science/article/pii/S2590136221000012","","","","","","JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …","","","","","","","","","","","","",""
"Journal Article","Parker D,Pianykh O","","Mobility-Guided Estimation of Covid-19 Transmission Rates","Am. J. Epidemiol.","American journal of epidemiology","2021","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2021-01-08","","","0002-9262","1476-6256","http://dx.doi.org/10.1093/aje/kwab001;https://www.ncbi.nlm.nih.gov/pubmed/33412586;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929457;https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/kwab001;https://academic.oup.com/aje/advance-article-abstract/doi/10.1093/aje/kwab001/6067658;https://academic.oup.com/aje/advance-article-pdf/doi/10.1093/aje/kwab001/35515024/kwab001.pdf?casa_token=Wel4lADT8eIAAAAA:tiGTNDxFPxlkmK19mLyVBrGxzgwzjOHo-wZ0zMwqn9T_ceTqiAZHAGWL0WnxF13iPOjhK9y3CyPRRw","10.1093/aje/kwab001","33412586","","","PMC7929457","It is of critical importance to estimate changing transmission rates and their dependence on population mobility. A common approach to this problem involves fitting daily transmission rates using a Susceptive Exposed Infected Recovered (SEIR) model (regularizing them to avoid overfitting), and then computing the relationship between the estimated transmission rate and mobility. Unfortunately, there are often several, very different transmission rate trajectories that can fit the reported cases well, meaning that the choice of regularization determines the final solution (and thus the mobility-transmission rate relationship) selected by the SEIR model. Moreover, the classical approaches to regularization-penalizing the derivative of the transmission rate trajectory-do not correspond to realistic properties of pandemic spread. Consequently, models fit using derivative-based regularization are often biased toward underestimating the current transmission rate and future deaths. In this work, we propose mobility-driven regularization of the SEIR transmission rate trajectory. This method rectifies the artificial regularization problem, produces more accurate and unbiased forecasts of future deaths, and estimates a highly interpretable relationship between mobility and the transmission rate. Mobility data for this analysis was collected by Safegraph (San Francisco, CA) from major US cities between March and August 2020.","COVID-19; SEIR model; cell phone mobility; regularization; reproduction number; transmission rate","","","Department of Statistics, Harvard University, Cambridge, Massachusetts. Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.","en","Research Article","","","","","","",""
"Journal Article","Ahmed SM,Shah RU,Fernandez V,Grineski S,Brintz B,Samore MH,Ferrari MJ,Leung DT,Keegan LT","","Robust Testing in Outpatient Settings to Explore COVID-19 Epidemiology: Disparities in Race/Ethnicity and Age, Salt Lake County, Utah, 2020","Public Health Rep.","Public health reports","2021","","","33354920988612","COVID Tracking Project","","","","journals.sagepub.com","","","","","2021-02-04","","","0094-6214","","http://dx.doi.org/10.1177/0033354920988612;https://www.ncbi.nlm.nih.gov/pubmed/33541222;https://journals.sagepub.com/doi/10.1177/0033354920988612?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://journals.sagepub.com/doi/abs/10.1177/0033354920988612?casa_token=4n7Ft_PGn5cAAAAA:TOZZpAIWC7O1DHL8yT2SjS-BWYZoDyH0HYSU3WXiBh37r_V0iVTJmxNpE4jehidRvnk0G-xMXCXbsA;https://journals.sagepub.com/doi/pdf/10.1177/0033354920988612?casa_token=xuxQFoTuU_QAAAAA:sEGvPnR9vtHhzaor5LMr8x0vSNinFds9WuYxmjKuKC6SWve75pc-nA8jwkumxpDg_D8fqwebM3tRWg","10.1177/0033354920988612","33541222","","","","OBJECTIVE: US-based descriptions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have focused on patients with severe disease. Our objective was to describe characteristics of a predominantly outpatient population tested for SARS-CoV-2 in an area receiving comprehensive testing. METHODS: We extracted data on demographic characteristics and clinical data for all patients (91% outpatient) tested for SARS-CoV-2 at University of Utah Health clinics in Salt Lake County, Utah, from March 10 through April 24, 2020. We manually extracted data on symptoms and exposures from a subset of patients, and we calculated the adjusted odds of receiving a positive test result by demographic characteristics and clinical risk factors. RESULTS: Of 17 662 people tested, 1006 (5.7%) received a positive test result for SARS-CoV-2. Hispanic/Latinx people were twice as likely as non-Hispanic White people to receive a positive test result (adjusted odds ratio [aOR] = 2.0; 95% CI, 1.3-3.1), although the severity at presentation did not explain this discrepancy. Young people aged 0-19 years had the lowest rates of receiving a positive test result for SARS-CoV-2 (<4 cases per 10 000 population), and adults aged 70-79 and 40-49 had the highest rates of hospitalization per 100 000 population among people who received a positive test result (16 and 11, respectively). CONCLUSIONS: We found disparities by race/ethnicity and age in access to testing and in receiving a positive test result among outpatients tested for SARS-CoV-2. Further research and public health outreach on addressing racial/ethnic and age disparities will be needed to effectively combat the coronavirus disease 2019 pandemic in the United States.","COVID-19; SARS-CoV-2; comprehensive testing; health disparities; outpatient","","","208352 Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. 12348 Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. 7060 College of Social and Behavioral Science, University of Utah, Salt Lake City, UT, USA. Center for Natural & Technological Hazards, University of Utah, Salt Lake City, UT, USA. Department of Sociology, University of Utah, Salt Lake City, UT, USA. Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, UT, USA. 118134 Department of Biology, Pennsylvania State University, State College, PA, USA.","en","Research Article","","","","","","",""
"Website","Brown G,Ghysels E,Yi L","","Estimating Undetected COVID-19 Infections","","","2021","","","","COVID Tracking Project","","","","kenaninstitute.unc.edu","","","","","2021","2021-04-02","","","","https://kenaninstitute.unc.edu/wp-content/uploads/2020/08/Unobserved_COVID_Infection__USversion-1.pdf","","","","","","… RMSE 6.64% 15.15% 15.72% 4.77% 10.54% Table 2: Model Parameters for Individual States The historical data of Covid-19 for each state is from The Covid Tracking Project . The time series data used in model estimation is constructed in the same way as the US data …","","","","","","","","","","","","",""
"Journal Article","Kauh TJ,Read JG,Scheitler AJ","","The Critical Role of Racial/Ethnic Data Disaggregation for Health Equity","Popul. Res. Policy Rev.","Population research and policy review","2021","","","1-7","COVID Tracking Project","","","","Springer","","","","","2021-01-08","","","0167-5923","","http://dx.doi.org/10.1007/s11113-020-09631-6;https://www.ncbi.nlm.nih.gov/pubmed/33437108;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791160;https://link.springer.com/article/10.1007/s11113-020-09631-6","10.1007/s11113-020-09631-6","33437108","","","PMC7791160","Population-level health outcomes and measures of well-being are often described relative to broad racial/ethnic categories such as White or Caucasian; Black or African American; Latino or Hispanic; Asian American; Native Hawaiian and Pacific Islander; or American Indian and Alaska Native. However, the aggregation of data into these groups masks critical within-group differences and disparities, limiting the health and social services fields' abilities to target their resources where most needed. While researchers and policymakers have recognized the importance of disaggregating racial/ethnic data-and many organizations have advocated for it over the years-progress has been slow and disparate. The ongoing lack of racial/ethnic data disaggregation perpetuates existing inequities in access to much-needed resources that can ensure health and well-being. In its efforts to help build a Culture of Health and promote health equity, the Robert Wood Johnson Foundation has supported activities aimed to advance the meaningful disaggregation of racial/ethnic data-at the collection, analysis, and reporting phases. This special issue presents further evidence for the importance of disaggregation, the technical and policy challenges to creating change in practice, and the implications of improving the use of race and ethnicity data to identify and address gaps in health.","Culture of health; Data disaggregation; Health equity; Race/ethnicity","","","Research-Evaluation-Learning Unit, Robert Wood Johnson Foundation, 50 College Road East, Princeton, NJ 08543 USA. Department of Sociology, Global Health Institute, Duke University, Durham, NC USA. Center for Health Policy Research, University of California Los Angeles, Los Angeles, CA USA.","en","Research Article","","","","","","",""
"Journal Article","Neelon B,Mutiso F,Mueller NT,Pearce JL,Benjamin-Neelon SE","","Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S","Am. J. Prev. Med.","American journal of preventive medicine","2021","","","","COVID Tracking Project","","","","Elsevier","","","","","2021-03-10","","","0749-3797","","https://www.sciencedirect.com/science/article/pii/S0749379721001355;http://dx.doi.org/10.1016/j.amepre.2021.01.034","10.1016/j.amepre.2021.01.034","","","","","Introduction The response to the COVID-19 pandemic became increasingly politicized in the U.S., and the political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examines the differences in COVID-19 infection, death, and testing by governor party affiliation across the 50 U.S. states and the District of Columbia. Methods A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15, 2020 through December 15, 2020. A Bayesian negative binomial model was fit to estimate the daily risk ratios and posterior intervals comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, Census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020. Results From March 2020 to early June 2020, Republican-led states had lower COVID-19 incidence rates than Democratic-led states. On June 3, 2020, the association reversed, and Republican-led states had a higher incidence (risk ratio=1.10, 95% posterior interval=1.01, 1.18). This trend persisted through early December 2020. For death rates, Republican-led states had lower rates early in the pandemic but higher rates from July 4, 2020 (risk ratio=1.18, 95% posterior interval=1.02, 1.31) through mid-December 2020. Republican-led states had higher test positivity rates starting on May 30, 2020 (risk ratio=1.70, 95% posterior interval=1.66, 1.73) and lower testing rates by September 30, 2020 (risk ratio=0.95, 95% posterior interval=0.90, 0.98). Conclusions Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the U.S. Future policy decisions should be guided by public health considerations rather than by political ideology.","","","","","","","","","","","","",""
"Journal Article","Ellis C,Jacobs M","","The Complexity of Health Disparities: More Than Just Black–White Differences","Perspectives of the ASHA Special Interest Groups","","2021","","","","COVID Tracking Project","","","","American Speech-Language-Hearing Association","","","","","2021-01-05","2021-04-02","","","","https://pubs.asha.org/doi/abs/10.1044/2020_PERSP-20-00199;http://dx.doi.org/10.1044/2020_PERSP-20-00199","10.1044/2020_PERSP-20-00199","","","","","Health disparities have once again moved to the forefront of America's consciousness with the recent significant observation of dramatically higher death rates among African Americans with COVID-19...","","","","","en","","","","","","","",""
"Journal Article","Baybars Ö","","Diplomasideki COVID-19 Etkisi ve Uluslararası Sistemin Değişimi Üzerine Tartışma: Yapısal Realist Bir Analiz","Diplomasi Araştırmaları Dergisi","","","","","","COVID Tracking Project","","","","dergipark.org.tr","","","","","","","","","","https://dergipark.org.tr/en/pub/jdr/issue/60425/890844;https://dergipark.org.tr/en/download/article-file/1616886","","","","","","… sonuçlanmıştır. Bkz. The Covid Tracking Project , Erişim 25 Ekim 2020. https://covidtracking.com/data/national 15 Dünya genelindeki vaka ve ölüm oranlarının neredeyse üçte birinden fazlası ABD başta olmak üzere Avrupa ülkelerinde görülmektedir. Güncel rakamlar …","","","","","","","","","","","","",""
"Preprint Manuscript","Swalwell E,Alagood RK","","Biological Threats Are National Security Risks: Why COVID-19 Should Be a Wake Up Call for Policy Makers","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-05","2021-04-02","","","","https://papers.ssrn.com/abstract=3764000;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3764000;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3764000;https://scholarlycommons.law.wlu.edu/cgi/viewcontent.cgi?article=1136&context=wlulr-online","","","","","","The outbreak of a highly contagious disease like COVID‑19 strikes at the core of national security and the nation’s interest in protecting its citizens from unnecessary harm. This paper explains how the Trump administration's failure to implement its own National Biodefense Strategy harmed U.S. national security and contributed to anti-government violence in the United States. The outbreak of a naturally occurring, novel virus in the United States exposed weaknesses in national security law and policy that put the country at heightened risk from domestic and foreign threats. A national security strategy is the “nation’s plan for the coordinated use of all the instruments of state power—nonmilitary as well as military—to pursue objectives that defend and advance its national interest.” Perhaps the most straightforward national security objective is to protect the country from foreign invasion, but national security involves other objectives that aim to protect people in the United States as well as their values. For example, protecting U.S. elections from foreign interference is a security objective that advances the nation’s interest in democratic governance.","covid, coronavirus, COVID-19, national security, defense, biodefense, antigovernment, white supremacy, terrorism, qanon, conspiracy theory, boogaloo, policy","","","","","","","","","","","","Washington and Lee Law Review"
"Review","Coke CJ,Davison B,Fields N,Fletcher J,Rollings J,Roberson L,Challagundla KB,Sampath C,Cade J,Farmer-Dixon C,Gangula PR","","SARS-CoV-2 Infection and Oral Health: Therapeutic Opportunities and Challenges","J. Clin. Med. Res.","Journal of clinical medicine research","2021","10","1","","COVID Tracking Project","","","","mdpi.com","","","","","2021-01-05","","","1918-3003","2077-0383","http://dx.doi.org/10.3390/jcm10010156;https://www.ncbi.nlm.nih.gov/pubmed/33466289;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795434;https://www.mdpi.com/resolver?pii=jcm10010156;https://www.mdpi.com/2077-0383/10/1/156;https://www.mdpi.com/2077-0383/10/1/156/pdf","10.3390/jcm10010156","33466289","","","PMC7795434","The novel corona virus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and the disease it causes, COVID-19 (Coronavirus Disease-2019) have had multi-faceted effects on a number of lives on a global scale both directly and indirectly. A growing body of evidence suggest that COVID-19 patients experience several oral health problems such as dry mouth, mucosal blistering, mouth rash, lip necrosis, and loss of taste and smell. Periodontal disease (PD), a severe inflammatory gum disease, may worsen the symptoms associated with COVID-19. Routine dental and periodontal treatment may help decrease the symptoms of COVID-19. PD is more prevalent among patients experiencing metabolic diseases such as obesity, diabetes mellitus and cardiovascular risk. Studies have shown that these patients are highly susceptible for SARS-CoV-2 infection. Pro-inflammatory cytokines and oxidative stress known to contribute to the development of PD and other metabolic diseases are highly elevated among COVID-19 patients. Periodontal health may help to determine the severity of COVID-19 infection. Accumulating evidence shows that African-Americans (AAs) and vulnerable populations are disproportionately susceptible to PD, metabolic diseases and COVID-19 compared to other ethnicities in the United States. Dentistry and dental healthcare professionals are particularly susceptible to this virus due to the transferability via the oral cavity and the use of aerosol creating instruments that are ubiquitous in this field. In this review, we attempt to provide a comprehensive and updated source of information about SARS-CoV-2/COVID-19 and the various effects it has had on the dental profession and patients visits to dental clinics. Finally, this review is a valuable resource for the management of oral hygiene and reduction of the severity of infection.","Angiotensin Converting Enzyme 2 (ACE-2); COVID-19; dental practice; inflammation; oxidative stress; periodontitis; saliva","","","Department of Oral Diagnostic Sciences & Research, School of Dentistry, Meharry Medical College, Nashville, TN 37208, USA. Department of Biochemistry & Molecular Biology, The Fred and Pamela Buffet Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA. The Children's Health Research Institute, University of Nebraska Medical Center, Omaha, NE 68198, USA.","en","Review","","","","","","",""
"Journal Article","Haefner J","","Self-Care for Health Professionals During Coronavirus Disease 2019 Crisis","J. Nurse Pract.","The journal for nurse practitioners: JNP","2021","17","3","279-282","COVID Tracking Project","","","","Elsevier","","","","","2021-03","","","1555-4155","","http://dx.doi.org/10.1016/j.nurpra.2020.12.015;https://www.ncbi.nlm.nih.gov/pubmed/33519312;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833183;https://linkinghub.elsevier.com/retrieve/pii/S1555-4155(20)30657-7;https://www.sciencedirect.com/science/article/pii/S1555415520306577;https://www.npjournal.org/article/S1555-4155(20)30657-7/fulltext","10.1016/j.nurpra.2020.12.015","33519312","","","PMC7833183","Health care providers are coping with unprecedented deaths, decisions for which patient receives a lifesaving ventilator, and the personal fear of contracting a virus that presently has no known treatment protocol. This article discusses the concepts of moral injury; compassion fatigue; experiencing secondary stress associated with a continuous demanding daily work environment; and the idea of giving your patient a \"good death\" during a time when even if family and friends are present during the dying process, there is no touching, kissing, or ability to offer physical comfort. Suggestions for self-care for yourself and colleagues are discussed.","compassion fatigue; ethics; good death; job stress; moral injury; quality of life","","","","en","Research Article","","","","","","",""
"Journal Article","Holtzman NAT","","Invited commentary: The Covid-19 pandemic in the United States","Int. J. Equity Health","International journal for equity in health","2021","20","1","3","COVID Tracking Project","","","","equityhealthj.biomedcentral.com","","","","","2021-01-04","","","1475-9276","","http://dx.doi.org/10.1186/s12939-020-01354-6;https://www.ncbi.nlm.nih.gov/pubmed/33397390;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781424;https://equityhealthj.biomedcentral.com/articles/10.1186/s12939-020-01354-6","10.1186/s12939-020-01354-6","33397390","","","PMC7781424","Despite being the wealthiest and one of the most technologically advanced countries in the world, the United States has the greatest number of Covid-19 cases and deaths. What accounts for this failure? The dismantling of the country's public health infrastructure has crippled contact tracing and exacerbated inequality as a disproportionate number of poor people and people of color have fallen ill with Covid-19. Inadequate regulation of the private for-profit sector has adversely affected the efficiency and quality of testing for the virus, and the prescription of costly drugs whose benefit and safety in treating infected patients have not been established. More stringent regulation of the commercial sector has led to the development of efficacious vaccines in a remarkably short time. Still, questions remain about the vaccines' effectiveness in the real world, and their safety.","","","","Johns Hopkins School of Medicine (Emeritus), Baltimore, MD, USA. nholtzm1@jhu.edu.","en","Research Article","","","","","","",""
"Preprint Manuscript","Kang KY,Wang X","","Search, Infection, and Government Policy","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-05","2021-04-02","","","","https://papers.ssrn.com/abstract=3760485;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3760485;http://dx.doi.org/10.2139/ssrn.3760485;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3760485","10.2139/ssrn.3760485","","","","","We construct an economic model of epidemiology, in which agents' economic decisions affect epidemic dynamics and vice versa. Agents are randomly matched and trade goods in pairwise meetings. The meeting rate increases in agents' search efforts, and the pathogen can be transmitted from infected agents to susceptible agents in meetings. We calibrate the model to the COVID-19 pandemic in the U.S. The model shows that unless an instrument, such as a vaccine, entirely stops new infections, the pandemic can be persistent due to the non-lasting property of immunity. Output drops because agents search less due to the threat of infections, and trade volumes in each meeting fall. We also conduct counterfactual analyses to study the effects of monetary policy, preventive measures, persistence of immunity, and lockdowns on epidemic dynamics and economic performance.","COVID-19, epidemics, lockdown, persistency of immunity, monetary policy, preventive measures","","","","","","","","","","","","Infection, and Government Policy (January 5"
"Journal Article","Karmakar M,Lantz PM,Tipirneni R","","Association of Social and Demographic Factors With COVID-19 Incidence and Death Rates in the US","JAMA Netw Open","JAMA network open","2021","4","1","e2036462","COVID Tracking Project","","","","jamanetwork.com","","","","","2021-01-04","","","2574-3805","","http://dx.doi.org/10.1001/jamanetworkopen.2020.36462;https://www.ncbi.nlm.nih.gov/pubmed/33512520;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846939;https://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2020.36462;https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2775732;https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2775732/karmakar_2021_oi_201090_1611286825.95028.pdf","10.1001/jamanetworkopen.2020.36462","33512520","","","PMC7846939","Importance: Descriptive data have revealed significant racial/ethnic disparities in coronavirus disease 2019 (COVID-19) cases in the US, but underlying mechanisms of disparities remain unknown. Objective: To examine the association between county-level sociodemographic risk factors and US COVID-19 incidence and mortality. Design, Setting, and Participants: This cross-sectional study analyzed the association between US county-level sociodemographic risk factors and COVID-19 incidence using mixed-effects negative binomial regression, and COVID-19 mortality using zero-inflated negative binomial regression. Data on COVID-19 incidence and mortality were collected from January 20 to July 29, 2020. The association of social risk factors with weekly cumulative incidence and mortality was also examined by interacting time with the index measures, using a random intercept to account for repeated measures. Main Outcomes and Measures: Sociodemographic data from publicly available data sets, including the US Centers for Disease Control and Prevention's Social Vulnerability Index (SVI), which includes subindices of socioeconomic status, household composition and disability, racial/ethnic minority and English language proficiency status, and housing and transportation. Results: As of July 29, 2020, there were a total of 4 289 283 COVID-19 cases and 147 074 COVID-19 deaths in the US. An increase of 0.1 point in SVI score was associated with a 14.3% increase in incidence rate (incidence rate ratio [IRR], 1.14; 95% CI, 1.13-1.16; P < .001) and 13.7% increase in mortality rate (IRR, 1.14; 95% CI, 1.12-1.16; P < .001), or an excess of 87 COVID-19 cases and 3 COVID-19 deaths per 100 000 population for a SVI score change from 0.5 to 0.6 in a midsize metropolitan county; subindices were also associated with both outcomes. A 0.1-point increase in the overall SVI was associated with a 0.9% increase in weekly cumulative increase in incidence rate (IRR, 1.01; 95% CI, 1.01-1.01; P < .001) and 0.5% increase in mortality rate (IRR, 1.01; 95% CI, 1.01-1.01; P < .001). Conclusions and Relevance: In this cross-sectional study, a wide range of sociodemographic risk factors, including socioeconomic status, racial/ethnic minority status, household composition, and environmental factors, were significantly associated with COVID-19 incidence and mortality. To address inequities in the burden of the COVID-19 pandemic, these social vulnerabilities and their root causes must be addressed.","","","","Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor. Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor. Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor. Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor.","en","Research Article","","","","","","",""
"Journal Article","Jones BL,Phillips F,Shanor D,VanDiest H,Chen Q,Currin-McCulloch J,Franklin C,Sparks D,Corral C,Ortega J","","Social work leadership in a medical school: A coordinated, compassionate COVID-19 response","Soc. Work Health Care","Social work in health care","2021","60","1","49-61","COVID Tracking Project","","","","Taylor & Francis","","","","","2021-02-09","","","0098-1389","1541-034X","http://dx.doi.org/10.1080/00981389.2021.1885567;https://www.ncbi.nlm.nih.gov/pubmed/33557718;https://www.tandfonline.com/doi/full/10.1080/00981389.2021.1885567;https://www.tandfonline.com/doi/abs/10.1080/00981389.2021.1885567?casa_token=9pVZ-ELldH8AAAAA:7ed1okT-mPkeBBeLe8W7vsdhQdy82finot13jLaOr94vDgoLizNn_PhU4Ir7AmjD0U9S_dcmkdad2A;https://www.tandfonline.com/doi/pdf/10.1080/00981389.2021.1885567?casa_token=P-OaBRw8VJgAAAAA:5nNNIGVhnyu2nKxLuS5frQ2TC9ri5VxpQVSgggiTE36UlCqL9oQqkJsmYijfXkn3Q8m30aLEwLNXoQ","10.1080/00981389.2021.1885567","33557718","","","","The COVID-19 pandemic has exposed the systemic inequities in our health care system and society has called for actions to meet the clinical, psychosocial and educational needs in health care settings and communities. In this paper we describe how an organized Department of Health Social Work in a medical school played a unique role in responding to the challenges of a pandemic with community, clinical, and educational initiatives that were integral to our community's health.","COVID 19 pandemic; advocacy; health disparities; social work; social work leadership","","","Department of Health Social Work, Dell Medical School, Steve Hicks School of Social Work, the University of Texas at Austin, USA. Steve Hicks School of Social Work, The University of Texas at Austin, Austin, TX, USA. School of Social Work, Colorado State University, Fort Collins, CO, USA. Steve Hicks School of Social Work, Department of Psychiatry, Dell Medical School, the University of Texas at Austin, Austin, TX, USA. Department of Health Social Work, Dell Medical School, Steve Hicks School of Social Work, The University of Texas at Austin, Austin, TX, USA. Department of Medical Education, Dell Medical School, Steve Hicks School of Social Work, The University of Texas at Austin, Austin, TX, USA.","en","Research Article","","","","","","",""
"Journal Article","Nossal KR","","Canada and COVID-19: the longer-term geopolitical implications","Round Table","The Round table","2021","110","1","31-45","COVID Tracking Project","","","","Routledge","","","","","2021-01-02","","","0035-8533","","https://doi.org/10.1080/00358533.2021.1875684;http://dx.doi.org/10.1080/00358533.2021.1875684;https://www.tandfonline.com/doi/abs/10.1080/00358533.2021.1875684?casa_token=AYMlCTUfp9wAAAAA:9wZNen0VDgSHPx3MQ5AcrSrQlpag8QPLT1z4fT5qKZReICcgnR51JckCk3vl--wWceV15Xkb8nlu9w;https://www.tandfonline.com/doi/pdf/10.1080/00358533.2021.1875684?casa_token=R3B5GmE4DrEAAAAA:NUe6gzCP3Uf1kumANElvrO41u3pCSqHLDsHvW4iGgRzTQRh4aXVCg45RacqfeI7nAeIwBqGn2Z5nZw","10.1080/00358533.2021.1875684","","","","","ABSTRACT This article explores the geopolitical implications of COVID-19 for Canada. It argues that the pandemic accelerated changes that were already underway as a result of the Trump presidency. It traces the spread of the virus in Canada and measures taken to control it. Canada?s response was markedly different to that of the US and the geostrategic fissures have deepened. The pandemic has played a major role in transforming thinking about U.S./Canada relations. Canada is now much more alone in North America and the world.","","","","","","","","","","","","",""
"Journal Article","Adjodah D,Dinakar K,Chinazzi M,Fraiberger SP,et al.","","Association between COVID-19 Outcomes and Mask Mandates, Adherence, and Attitudes","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.19.21250132v3.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/03/05/2021.01.19.21250132.full.pdf","","","","","","Page 1. Working Paper Association between COVID-19 Outcomes and Mask Mandates, Adherence, and Attitudes Dhaval Adjodah1,6, Karthik Dinakar2, Matteo Chinazzi4, Samuel P. Fraiberger1,3,5, Alex Pentland2, Samantha …","","","","","","","","","","","","",""
"Journal Article","Wu F,Xiao A,Zhang J,Moniz K,Endo N,Armas F,Bushman M,Chai PR,Duvallet C,Erickson TB,Foppe K,Ghaeli N,Gu X,Hanage WP,Huang KH,Lee WL,Matus M,McElroy KA,Rhode SF,Wuertz S,Thompson J,Alm EJ","","Wastewater Surveillance of SARS-CoV-2 across 40 U.S. states","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-03-14","","","","","http://dx.doi.org/10.1101/2021.03.10.21253235;https://www.ncbi.nlm.nih.gov/pubmed/33758888;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987047;https://doi.org/10.1101/2021.03.10.21253235;https://www.medrxiv.org/content/10.1101/2021.03.10.21253235v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/03/14/2021.03.10.21253235.full.pdf","10.1101/2021.03.10.21253235","33758888","","","PMC7987047","Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.","","","","","en","Research Article","","","","","","",""
"Journal Article","Salichos L,Warrell J,Cevasco H,Chung A,Gerstein M","","Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for improved mitigation strategies","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.30.21250785v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/02/2021.01.30.21250785.full.pdf","","","","","","… individuals have been retrieved from Worldometer 'worldometers.info/coronavirus/'. Maximum 196 reproduction rates have been retrieved from The Covid Tracking Project 197 “https://covidtracking.com/” and 'https://rt.live/us/' 29. For the first wave of Covid-19 outbreak …","","","","","","","","","","","","",""
"Journal Article","Kraay ANM,Gallagher ME,Ge Y,Han P,Baker JM,et al.","","Modeling the use of SARS-CoV-2 vaccination to safely relax non-pharmaceutical interventions","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.03.12.21253481v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/03/13/2021.03.12.21253481.full.pdf","","","","","","Page 1. Modeling the use of SARS-CoV-2 vaccination to safely relax 1 non-pharmaceutical interventions 2 Alicia NM Kraay, Molly E. Gallagher, Yang Ge, Peichun Han, Julia M. Baker, 3 Katia Koelle, Andreas Handel*, Benjamin A Lopman* 4 March 12, 2021 5 1 …","","","","","","","","","","","","",""
"Journal Article","Bi C,Mendoza R,Cheng HT,Pagaspas G,Gabutan EC,et al.","","Pooled Surveillance Testing Program for Asymptomatic SARS-CoV-2 Infections in K-12 Schools and Universities","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.09.21251464v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/12/2021.02.09.21251464.full.pdf","","","","","","… https://doi.org/10.1101/2021.02.09.21251464 doi: medRxiv preprint Page 16. 16 References: 1 The COVID Tracking Project . The Atlantic Monthly Group. 2 Nikolai, LA, Meyer, CG, Kremsner, PG & Velavan, TP Asymptomatic SARS Coronavirus 2 infection: Invisible yet …","","","","","","","","","","","","",""
"Journal Article","Vardavas R,de Lima PN,Baker L","","Modeling COVID-19 Nonpharmaceutical Interventions: Exploring periodic NPI strategies","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-03-02","","","","","http://dx.doi.org/10.1101/2021.02.28.21252642;https://www.ncbi.nlm.nih.gov/pubmed/33688672;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941649;https://doi.org/10.1101/2021.02.28.21252642;https://www.medrxiv.org/content/10.1101/2021.02.28.21252642v2.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/03/02/2021.02.28.21252642.full.pdf","10.1101/2021.02.28.21252642","33688672","","","PMC7941649","In April 2020, we developed a COVID-19 transmission model used as part of RAND's web-based COVID-19 decision support tool that compares the effects of different nonphar-maceutical public health interventions (NPIs) on health and economic outcomes. An interdis-ciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). We previously published a description of our approach as a RAND report describing how the epidemiological model, the economic model, and a systematic assessment of NPIs informed the web-tool. This paper provides further details of our model, describes extensions that we made to our model since April, presents sensitivity analyses, and analyzes periodic NPIs. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies. We consider strategies that periodically switch between a base NPI level and a higher NPI level as our working example.","","","","","en","Research Article","","","","","","",""
"Journal Article","Bhattacharjee S,Liao S,Paul D,Chaudhuri S","","Inference on the dynamics of the COVID pandemic from observational data","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.01.21250936v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/03/2021.02.01.21250936.full.pdf","","","","","","Page 1. Inference on the dynamics of the COVID pandemic from observational data Satarupa Bhattacharjeea, Shuting Liaob, Debashis Paula, Sanjay Chaudhuric* a Department of Statistics, University of California, Davis b Graduate …","","","","","","","","","","","","",""
"Journal Article","Gibson DG,Agarwal S,Meghani A,Limaye R,et al.","","COVID-19 vaccine acceptability and inequity in the United States: Results from a nationally representative survey","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.29.21250784v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/02/2021.01.29.21250784.full.pdf","","","","","","Page 1. Title: COVID-19 Vaccine Acceptability and Inequity in the United States: Results from a nationally representative survey Authors: Dustin Gibson,1* PhD; Smisha Agarwal1, MBBS MBA PhD; Ankita Meghani,1 PhD; Rupali …","","","","","","","","","","","","",""
"Journal Article","Wang Y,Peng HM,Sha L,Liu Z,Hong P","","State-level COVID-19 Trend Forecasting Using Mobility and Policy Data","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.04.21249218v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/01/06/2021.01.04.21249218.full.pdf","","","","","","… 125 • State-level demographic information: This data includes the state-level population den- 126 sity information published by the US Census Bureau4, the race structure information (frac- 127 tion of 7 different race categories) collected by the COVID Tracking Project …","","","","","","","","","","","","",""
"Journal Article","Bhowmik T,Eluru N","","A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.19.21252117v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/25/2021.02.19.21252117.full.pdf","","","","","","Page 1. 1 A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand Tanmoy Bhowmik* Post-Doctoral Scholar Department of Civil, Environmental & Construction …","","","","","","","","","","","","",""
"Journal Article","Faust J,Du C,Li SX,Lin Z,Krumholz H","","Correcting excess mortality for pandemic-associated population decreases","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.10.21251461v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/12/2021.02.10.21251461.full.pdf","","","","","","… Accessed January 4, 2021. https://www.cdc.gov/coronavirus/2019-ncov/covid- data/forecasting-us.html 13. Totals for the US. The COVID Tracking Project . Accessed February 8, 2021. https://covidtracking.com/data/national Supplemental Appendix: See attached pdf …","","","","","","","","","","","","",""
"Journal Article","Li Y,Li M,Rice M,Su Y,Yang C","","Phased implementation of COVID-19 vaccination: rapid assessment of policy adoption, reach and effectiveness to protect the most vulnerable in the US","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.19.21252118v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/23/2021.02.19.21252118.full.pdf","","","","","","Page 1. Phased implementation of COVID-19 vaccination: rapid assessment of policy adoption, reach and effectiveness to protect the most vulnerable in the US Yun Li1,2, Moming Li3, Megan Rice4, Yanfang Su5*, Chaowei Yang1,2 …","","","","","","","","","","","","",""
"Journal Article","Gray JD,Harris CR,Wylezinski LS,Spurlock CF","","Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-02-19","","","","","http://dx.doi.org/10.1101/2021.02.09.21251106;https://www.ncbi.nlm.nih.gov/pubmed/33619499;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899464;https://doi.org/10.1101/2021.02.09.21251106;https://www.medrxiv.org/content/10.1101/2021.02.09.21251106v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/19/2021.02.09.21251106.full.pdf","10.1101/2021.02.09.21251106","33619499","","","PMC7899464","The COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future. Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R 2 ) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.","","","","","en","Research Article","","","","","","",""
"Journal Article","Connolly M,Jacobs B,Notzon FC","","COVID-19 among American Indians and Alaska Natives in the United States: An early look","Stat. J. IAOS","Statistical journal of the IAOS","2021","37","1","25-36","COVID Tracking Project","","","","IOS Press","","","","","2021-03-22","","","1874-7655","1875-9254","https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/SJI-210790;http://dx.doi.org/10.3233/sji-210790;https://content.iospress.com/articles/statistical-journal-of-the-iaos/sji210790","10.3233/sji-210790","","","","","To date the US has experienced the greatest number of cases and deaths due to COVID-19 in the world, but the impact has been even greater for American Indians and Alaska Natives (AIAN). Despite numerous disadvantages related to poor socioeconomic status and preexisting health conditions, Tribal sovereignty, community strength and resiliency have been important factors in limiting the burden of disease on Indigenous Americans. AIAN Tribes have repeatedly chosen to protect lives over Tribal income, choosing to close businesses that are the economic lifeblood of the reservations.","","","","International Group for Indigenous Health Measurement, Special Populations Editor, Statistical Journal of the IAOS, Columbia, Maryland; O’Neill Institute for Global Health Law, Georgetown University, Washington, DC, USA; International Group for Indigenous Health Measurement. Kensington, Maryland","","","","","","","","",""
"Journal Article","Hussein MR,Morsi I,Awad EA,Fayed DA,et al.","","The differential impact of the COVID-19 epidemic on Medicaid expansion and non-expansion states","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.23.21252296v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/25/2021.02.23.21252296.full.pdf","","","","","","… diseases. Heal Aff Blog https//www Heal org/do/101377/hblog20170817. 2019;61561. 5. The Atlantic. The COVID Tracking Project . https://covidtracking.com/race. 6. Schwartz K, Tolbert J. Limitations of the Program for Uninsured COVID-19 Patients Raise …","","","","","","","","","","","","",""
"Journal Article","Alsharef A,Banerjee S,Uddin SMJ,Albert A,Jaselskis E","","Early Impacts of the COVID-19 Pandemic on the United States Construction Industry","Int. J. Environ. Res. Public Health","International journal of environmental research and public health","2021","18","4","","COVID Tracking Project","","","","mdpi.com","","","","","2021-02-06","","","1661-7827","1660-4601","http://dx.doi.org/10.3390/ijerph18041559;https://www.ncbi.nlm.nih.gov/pubmed/33562127;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915481;https://www.mdpi.com/resolver?pii=ijerph18041559;https://www.mdpi.com/989638;https://www.mdpi.com/1660-4601/18/4/1559/pdf","10.3390/ijerph18041559","33562127","","","PMC7915481","The COVID-19 pandemic has been the largest global health crisis in decades. Apart from the unprecedented number of deaths and hospitalizations, the pandemic has resulted in economic slowdowns, widespread business disruptions, and significant hardships. This study focused on investigating the early impacts of the COVID-19 pandemic on the U.S. construction industry since the declaration of the national emergency on 13 March 2020. The study objectives were achieved through 34 telephone interviews with project managers, engineers, designers, and superintendents that represented different states and distinct industry sectors in the United States (U.S.). The interviewees offered information on their experience with the pandemic, including the general and adverse effects experienced, new opportunities created, and risk management efforts being undertaken. The reported adverse effects included significant delays on projects, inability to secure materials on time, reduction in productivity rates, material price escalations, and others. The new opportunities that were created included projects involving the fast-track construction of medical facilities, construction of residential buildings, transportation-related work, and opportunities to recruit skilled workers. The risk management measures that were widely adopted included measures to enhance safety and reduce other project risks. The safety measures adopted included requiring employees to wear cloth face masks, adoption of social distancing protocols, staggering of construction operations, offering COVID-19-related training, administering temperature checks prior to entry into the workplace, and others. Measures to manage other project risks included the formation of a task force team to review the evolving pandemic and offer recommendations, advocating that construction businesses be deemed essential to combat delays and taking advantage of government relief programs. The study findings will be useful to industry stakeholders interested in understanding the early impacts of the pandemic on the construction industry. Industry stakeholders may also build upon the reported findings and establish best practices for continued safe and productive operations.","COVID-19; COVID-19 risk; construction delays; construction productivity; construction safety; lessons learned; mitigation strategies; occupational safety; safety risk; worker safety","","","Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA. Civil Engineering Department, College of Engineering, King Saud University, P.O. Box 22452, Riyadh 11451, Saudi Arabia.","en","Research Article","","","","","","",""
"Journal Article","Evers NF,Greenfield PM,Evers GW","","COVID ‐19 shifts mortality salience, activities, and values in the United States: Big data analysis of online adaptation","Human Behav and Emerg Tech","Human Behavior and Emerging Technologies","2021","3","1","107-126","COVID Tracking Project","","","","Wiley","","","","","2021-01","","","2578-1863","2578-1863","https://onlinelibrary.wiley.com/doi/10.1002/hbe2.251;http://dx.doi.org/10.1002/hbe2.251;https://onlinelibrary.wiley.com/doi/abs/10.1002/hbe2.251;https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbe2.251","10.1002/hbe2.251","","","","","Abstract What is the effect of a life‐threatening pandemic at the societal level? An expanded Theory of Social Change, Cultural Evolution, and Human Development predicts that, during a period of in...","","","http://creativecommons.org/licenses/by-nc-nd/4.0/","Department of Psychology Harvard College Cambridge Massachusetts USA; Department of Psychology University of California Los Angeles California USA; Mulgrave School Vancouver Canada","en","","","","","","","",""
"Journal Article","Barclay RA,Akhrymuk I,Patnaik A,Callahan V,Lehman C,Andersen P,Barbero R,Barksdale S,Dunlap R,Goldfarb D,Jones-Roe T,Kelly R,Kim B,Miao S,Munns A,Munns D,Patel S,Porter E,Ramsey R,Sahoo S,Swahn O,Warsh J,Kehn-Hall K,Lepene B","","Hydrogel particles improve detection of SARS-CoV-2 RNA from multiple sample types","Sci. Rep.","Scientific reports","2020","10","1","22425","COVID Tracking Project","","","","nature.com","","","","","2020-12-30","","","2045-2322","","http://dx.doi.org/10.1038/s41598-020-78771-8;https://www.ncbi.nlm.nih.gov/pubmed/33380736;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773739;https://doi.org/10.1038/s41598-020-78771-8;https://www.nature.com/articles/s41598-020-78771-8","10.1038/s41598-020-78771-8","33380736","","","PMC7773739","Here we present a rapid and versatile method for capturing and concentrating SARS-CoV-2 from contrived transport medium and saliva samples using affinity-capture magnetic hydrogel particles. We demonstrate that the method concentrates virus from 1 mL samples prior to RNA extraction, substantially improving detection of virus using real-time RT-PCR across a range of viral titers (100-1,000,000 viral copies/mL) and enabling detection of virus using the 2019 nCoV CDC EUA Kit down to 100 viral copies/mL. This method is compatible with commercially available nucleic acid extraction kits (i.e., from Qiagen) and a simple heat and detergent method that extracts viral RNA directly off the particle, allowing a sample processing time of 10 min. We furthermore tested our method in transport medium diagnostic remnant samples that previously had been tested for SARS-CoV-2, showing that our method not only correctly identified all positive samples but also substantially improved detection of the virus in low viral load samples. The average improvement in cycle threshold value across all viral titers tested was 3.1. Finally, we illustrate that our method could potentially be used to enable pooled testing, as we observed considerable improvement in the detection of SARS-CoV-2 RNA from sample volumes of up to 10 mL.","","","","Ceres Nanosciences, Inc., Manassas, VA, 20110, USA. National Center for Biodefense and Infectious Diseases, School of Systems Biology, George Mason University, Manassas, VA, 20110, USA. Ceres Nanosciences, Inc., Manassas, VA, 20110, USA. blepene@ceresnano.com.","en","Research Article","","","","","","",""
"Journal Article","Ames AD","","Safety-Critical Control of Compartmental Epidemiological Models With Measurement Delays","www-personal.umich.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","http://www-personal.umich.edu/~orosz/articles/2021_LCSS_Tamas_Andrew_Aaron.pdf","","","","","","Page 1. IEEE CONTROL SYSTEMS LETTERS, VOL. 5, NO. 5, NOVEMBER 2021 1537 Safety-Critical Control of Compartmental Epidemiological Models With Measurement Delays TamásG.Molnár ,AndrewW.Singletary ,GáborOrosz , Member, IEEE …","","","","","","","","","","","","",""
"Journal Article","Chen X,Chock S,Chong P,Donaldson T,Gu WW,et al.","","History does not have to repeat itself (again): How to prepare for future pandemics","pioneeracademics.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://pioneeracademics.com/wp-content/uploads/2020/10/DV-Super-Starkars.pdf","","","","","","Page 1. History does not have to repeat itself (again): How to prepare for future pandemics By Xuanyi Chen, Sam Chock, Peter Chong, Trent Donaldson, WenWen Gu, Michelle Tang, Madeleine Wing, Devon Shao, Iris Zhou, Ivan …","","","","","","","","","","","","",""
"Journal Article","Huanca-Arohuanca JW,et al.","","Estimaciones y contrastes de la pandemia en Perú y en el contexto mundial",": Revista de investigación …","","2020","","","","COVID Tracking Project","","","","revistas.usat.edu.pe","","","","","2020","","","","","http://revistas.usat.edu.pe/index.php/educare/article/view/440;http://revistas.usat.edu.pe/index.php/educare/article/download/440/1105","","","","","","… Fuente: Gráfico elaborado por Stheven Bernard (@sdbernard). Financial Times analysis of ECDC an Covid Tracking Project data.Los datos resultan del promedio móvil de 7 días. EDUCARE ET COMUNICARE VOL. 8 Nº 2 (Agosto-Diciembre, 2020): 10-20 Page 5 …","","","","","","","","","","","","",""
"Journal Article","Adhikari B,Prakash BA","","Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal","","","2021","","","","COVID Tracking Project","","","","cc.gatech.edu","","","","","2021","","","","","https://www.cc.gatech.edu/~acastillo41/iaai-deepcovid-appendix.pdf","","","","","","… 2020. Measuring movement and social con- tact with smartphone data: a real-time application to COVID-19. Technical report, National Bureau of Economic Research. COVID-Tracking. 2020. The COVID Tracking Project . URL https: //covidtracking.com. Google …","","","","","","","","","","","","",""
"Journal Article","Jones R","","Multidisciplinary insights into health care financial risk and hospital surge capacity, Part 4: What size does a health insurer or health authority need to be to …","J. Health Care Finance","Journal of health care finance","2021","","","","COVID Tracking Project","","","","academia.edu","","","","","2021","","","1078-6767","","https://www.academia.edu/download/65590374/241_490_1_SM.pdf","","","","","","… unrelated illnesses. For COVID-19 analysis of data from US states reveals that at the peak there were approximately 20 to 45 persons occupying a hospital bed per COVID-19 death (data from The COVID Tracking Project 2020). This …","","","","","","","","","","","","",""
"Commentary","Karim F,van Laar J,van Hagen M","","Spectrum of Fibrotic Lung Diseases","N. Engl. J. Med.","The New England journal of medicine","2020","383","25","2485","COVID Tracking Project","","","","bookcafe.yuntsg.com","","","","","2020-12-17","","","0028-4793","1533-4406","http://dx.doi.org/10.1056/NEJMc2031135;https://www.ncbi.nlm.nih.gov/pubmed/33326732;https://www.nejm.org/doi/10.1056/NEJMc2031135?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://bookcafe.yuntsg.com/ueditor/jsp/upload/file/20210116/1610761574656069054.pdf","10.1056/NEJMc2031135","33326732","","","","… the SIMPLE trials (49%) with representative US populations from the Corona- virus Disease 2019 (COVID-19)–Associated Hospi- talization Surveillance Network (COVID-NET) of the Centers for Disease Control and Prevention4 and from the COVID Tracking Project …","","","","Groene Hart Ziekenhuis, Gouda, the Netherlands faiz.karim@ghz.nl. Erasmus Medical Center, Rotterdam, the Netherlands.","en","Commentary","","","","","","",""
"Journal Article","Molock SD,Parchem B","","The impact of COVID-19 on college students from communities of color","J Am. Coll. Health","Journal of American college health: J of ACH","2021","","","1-7","COVID Tracking Project","","","","Taylor & Francis","","","","","2021-01-27","","","0744-8481","1940-3208","http://dx.doi.org/10.1080/07448481.2020.1865380;https://www.ncbi.nlm.nih.gov/pubmed/33502970;https://www.tandfonline.com/doi/full/10.1080/07448481.2020.1865380;https://www.tandfonline.com/doi/abs/10.1080/07448481.2020.1865380?casa_token=iDK401RZSQkAAAAA:U32kfN6zxn3SDMZsptd4b42hc4qq35752gmrTbT0S9LM8QKI7JY1AjnG32vWSBEz0AQQ1vXI9u9P2A;https://www.tandfonline.com/doi/pdf/10.1080/07448481.2020.1865380?casa_token=eBcSKEFWw1UAAAAA:G4arwPlMkc3iN9mfyPsHyOzT_x6g0Zd81tpIdYcDsns0uDhIEqjzBwVD6f0qsffcmIPqADdee1hg9g","10.1080/07448481.2020.1865380","33502970","","","","OBJECTIVE: To assess the impact of the COVID-19 pandemic on daily living, mental well-being, and experiences of racial discrimination among college students from communities of color. Participants: Sample comprised 193 ethnically diverse college students, aged 18 to 25 years (M = 20.5 years), who were participating in virtual internships due to the COVID-19 pandemic. Methods: A cross-sectional 16-item survey was developed as a partnership between two nonprofit organizations. The survey included both close-ended and open-ended questions assessing the impact of COVID-19. Results: The students of color reported disruptive changes in finances (54%), living situation (35%), academic performance (46%), educational plans (49%), and career goals (36%). Primary mental health challenges included stress (41%), anxiety (33%), and depression (18%). Students also noted challenges managing racial injustice during the COVID-19 pandemic. Conclusions: Higher education institutions will benefit from financially and emotionally supporting students of color during the COVID-19 pandemic and growing visibility of systemic racism.","COVID-19; college students; communities of color; mental health","","","Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA.","en","Research Article","","","","","","",""
"Journal Article","Zimba R,Kulkarni S,Berry A,You W,Mirzayi C,Westmoreland D,Parcesepe A,Waldron L,Rane M,Kochhar S,Robertson M,Maroko A,Grov C,Nash D","","SARS-CoV-2 Testing Service Preferences of Adults in the United States: Discrete Choice Experiment","JMIR Public Health Surveill","JMIR public health and surveillance","2020","6","4","e25546","COVID Tracking Project","","","","publichealth.jmir.org","","","","","2020-12-31","","","2369-2960","","http://dx.doi.org/10.2196/25546;https://www.ncbi.nlm.nih.gov/pubmed/33315584;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781587;https://publichealth.jmir.org/2020/4/e25546/;https://publichealth.jmir.org/2020/4/e25546/?fbclid=IwAR0ZrctDuwQNcHD60D97es_sLeo5RQr1se7wrHnQCEohrnMHVR-q-E8MT7M&utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_0","10.2196/25546","33315584","","","PMC7781587","BACKGROUND: Ascertaining preferences for SARS-CoV-2 testing and incorporating findings into the design and implementation of strategies for delivering testing services may enhance testing uptake and engagement, a prerequisite to reducing onward transmission. OBJECTIVE: This study aims to determine important drivers of decisions to obtain a SARS-CoV-2 test in the context of increasing community transmission. METHODS: We used a discrete choice experiment to assess preferences for SARS-CoV-2 test type, specimen type, testing venue, and results turnaround time. Participants (n=4793) from the US national longitudinal Communities, Households and SARS-CoV-2 Epidemiology (CHASING) COVID Cohort Study completed our online survey from July 30 to September 8, 2020. We estimated the relative importance of testing method attributes and part-worth utilities of attribute levels, and simulated the uptake of an optimized testing scenario relative to the current typical testing scenario of polymerase chain reaction (PCR) via nasopharyngeal swab in a provider's office or urgent care clinic with results in >5 days. RESULTS: Test result turnaround time had the highest relative importance (30.4%), followed by test type (28.3%), specimen type (26.2%), and venue (15.0%). In simulations, immediate or same-day test results, both PCR and serology, or oral specimens substantially increased testing uptake over the current typical testing option. Simulated uptake of a hypothetical testing scenario of PCR and serology via a saliva sample at a pharmacy with same-day results was 97.7%, compared to 0.6% for the current typical testing scenario, with 1.8% opting for no test. CONCLUSIONS: Testing strategies that offer both PCR and serology with noninvasive methods and rapid turnaround time would likely have the most uptake and engagement among residents in communities with increasing community transmission of SARS-CoV-2.","COVID-19; SARS-CoV-2; cohort study; discrete choice experiment; engagement; implementation science; pandemic; stated preference study; testing","","","Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States. The Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States. Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States. Department of Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States.","en","Research Article","","","","","","",""
"Journal Article","Chen S,Chen Q,Yang J,Lin L,Li L,Jiao L,Geldsetzer P,Wang C,Wilder-Smith A,Bärnighausen T","","Curbing the COVID-19 pandemic with facility-based isolation of mild cases: a mathematical modeling study","J. Travel Med.","Journal of travel medicine","2021","28","2","","COVID Tracking Project","","","","academic.oup.com","","","","","2021-02-23","","","1195-1982","1708-8305","http://dx.doi.org/10.1093/jtm/taaa226;https://www.ncbi.nlm.nih.gov/pubmed/33274387;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799023;https://academic.oup.com/jtm/article-lookup/doi/10.1093/jtm/taaa226;https://academic.oup.com/jtm/article-abstract/28/2/taaa226/6018220;https://academic.oup.com/jtm/article/28/2/taaa226/6018220?casa_token=hCzG4FORLDEAAAAA:WOVNouXDhllB5yS_9QaCq3f9WInmt92svi1AfIkLq7T6k0alPqB7Q0j3mwFsRGZJDHeY5XTyFXO2dw","10.1093/jtm/taaa226","33274387","","","PMC7799023","BACKGROUND: In many countries, patients with mild coronavirus disease 2019 (COVID-19) are told to self-isolate at home, but imperfect compliance and shared living space with uninfected people limit the effectiveness of home-based isolation. We examine the impact of facility-based isolation compared to self-isolation at home on the continuing epidemic in the USA. METHODS: We developed a compartment model to simulate the dynamic transmission of COVID-19 and calibrated it to key epidemic measures in the USA from March to September 2020. We simulated facility-based isolation strategies with various capacities and starting times under different diagnosis rates. Our primary model outcomes are new infections and deaths over 2 months from October 2020 onwards. In addition to national-level estimations, we explored the effects of facility-based isolation under different epidemic burdens in major US Census Regions. We performed sensitivity analyses by varying key model assumptions and parameters. RESULTS: We find that facility-based isolation with moderate capacity of 5 beds per 10 000 total population could avert 4.17 (95% credible interval 1.65-7.11) million new infections and 16 000 (8000-23 000) deaths in 2 months compared with home-based isolation. These results are equivalent to relative reductions of 57% (44-61%) in new infections and 37% (27-40%) in deaths. Facility-based isolation with high capacity of 10 beds per 10 000 population could achieve reductions of 76% (62-84%) in new infections and 52% (37-64%) in deaths when supported by expanded testing with an additional 20% daily diagnosis rate. Delays in implementation would substantially reduce the impact of facility-based isolation. The effective capacity and the impact of facility-based isolation varied by epidemic stage across regions. CONCLUSION: Timely facility-based isolation for mild COVID-19 cases could substantially reduce the number of new infections and effectively curb the continuing epidemic in the USA. Local epidemic burdens should determine the scale of facility-based isolation strategies.","","","","Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany, 69120. Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 100730. The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA, 16802. State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 100005. Department of Statistics, The Pennsylvania State University, University Park, PA, USA, 16802. Chinese Academy of Social Sciences, Beijing, China, 100732. Reed College, Portland, OR, USA, 97202. Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA, 94305. National Center for Respiratory Medicine, Beijing, China, 100029. Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China, 100029. Department of Disease Control, London School of Hygiene and Tropical Medicine, United Kingdom, WC1E 7HT.","en","Research Article","","","","","","",""
"Journal Article","Bouk D","","Materializing COVID","Isis","Isis; an international review devoted to the history of science and its cultural influences","2020","111","4","783-786","COVID Tracking Project","","","","The University of Chicago Press","","","","","2020-12-02","","","0021-1753","","https://doi.org/10.1086/712337;http://dx.doi.org/10.1086/712337;https://www.journals.uchicago.edu/doi/full/10.1086/712337","10.1086/712337","","","","","… The COVID Tracking Project , hosted by the Atlantic, graded states on the basis of whether they provided machine-readable totals, whether they reported negative testing results as well as positive ones, and other factors that made such data more useful in understanding …","","","","","","","","","","","","",""
"Thesis","Serio F","","Geopolitica del conflitto razziale e del cambiamento generazionale negli odierni Stati Uniti","","","2020","","","","COVID Tracking Project","","","","Luiss Guido Carli","","","","","2020-11-18","2021-04-02","","","","http://tesi.luiss.it/28571/;http://tesi.luiss.it/28571/1/639552_SERIO_FRANCESCO.pdf","","","","","","Evoluzione etnica. La crescente diversità americana. 50 Stati, tre regioni. Browning America. Evoluzione generazionale. Geografia dell'invecchiamento. La diversificazione e l'invecchiamento. Graying America. Contrapposizione etnica e generazionale. Razzismo sistemico. Wealth GAP. Effetti della pandemia. Contrapposizione politica generazionale ed etnica. Le elezioni del 2016 d il sogno americano. Le politiche di Trump. Prospettive democratiche e repubblicane.","","","","","it","","","","","","169","Luiss Guido Carli",""
"Journal Article","Manno M,Dukes D,Cerna O,Hill C","","Pushing toward progress: Early implementation findings from a study of the Male Student Success Initiative","MDRC","MDRC","2020","","","","COVID Tracking Project","","","","MDRC. 16 East 34th Street 19th Floor, New York, NY 10016-4326. Tel: 212-532-3200; Fax: 212-684-0832; e-mail: publications@mdrc.org; Web site: http://www.mdrc.org","","","","","2020-11","2021-04-02","","","","http://files.eric.ed.gov/fulltext/ED609564.pdf;https://eric.ed.gov/?id=ED609564;https://files.eric.ed.gov/fulltext/ED609564.pdf","","","","","","National college completion rates for men of color at open- and broad-access postsecondary institutions (including community colleges) lag behind completion rates for White students and for female students of any race or ethnicity. Research points to several broad factors to explain these unequal outcomes, including precollege environments that do not sufficiently prepare men of color for college, nonacademic barriers that compete for students' time and attention, and inadequate college campus support. Other scholarship challenges postsecondary education professionals to think critically about how discriminatory policies and practices and structural racism perpetuate this inequality nationwide. Since the early 2000s, many colleges have tailored campus programs to provide academic and social support specific to the interests and needs of male students of color to overcome gaps in success rates. The Male Student Success Initiative (MSSI) at the Community College of Baltimore County","College Students; Males; Success; Academic Achievement; Program Effectiveness; Program Implementation; Minority Group Students; African American Students; College Programs; Equal Education; Racial Differences; Program Evaluation; Program Design","","","","en","","","","","","","",""
"Journal Article","Comas-Herrera A,Zalakaín J,Lemmon E,et al.","","Mortality associated with COVID-19 in care homes: international evidence",". org, International Long …","","2020","","","","COVID Tracking Project","","","","ltccovid.org","","","","","2020","","","","","https://ltccovid.org/wp-content/uploads/2020/10/Mortality-associated-with-COVID-among-people-living-in-care-homes-14-October-2020-5.pdf","","","","","","Page 1. ltccovid.org | Mortality associated with COVID-19 outbreaks in care homes 1 Mortality associated with COVID-19 in care homes: international evidence Adelina Comas-Herrera, Joseba Zalakaín, Elizabeth Lemmon, David …","","","","","","","","","","","","",""
"Book","Krohn C,Farmer T","","Bombarded: How to Fight Back Against the Online Assault on Democracy","","","2020","","","","COVID Tracking Project","","","","Made For Success Publishing","","","","","2020-10-10","","9781641465311","","","https://play.google.com/store/books/details?id=29UPEAAAQBAJ;https://books.google.com/books?hl=en&lr=&id=29UPEAAAQBAJ&oi=fnd&pg=PT6&dq=%22covid+tracking+project%22&ots=twH0kPNjaN&sig=nIN2ePSA8EJXVj6YyRRXJH1OnMw","","","","","","Imagine an imminent America where citizens are bombarded with personalized political messages from every smart device – yet information is so suspect, nobody can tell what the truth is. It means oceans of disinformation engineered to sow false beliefs or simply disorient. The coronavirus pandemic provided a foretaste of an infuriating, dystopian future. From the start Americans fought over the most basic facts of the crisis, from death tolls to quack cures to the wisdom of stay-at-home orders. The splintered digital infosphere bred confusion and delusion, some of it fatal. Now think of our campaigns and elections. The digital information age means more than hyper-targeted, just-for-you messages from insurance companies and presidential candidates alike. Big Data is on the way to fueling information environments so fine-tuned, no two of us hold the same view of reality, and no two voters hear the same pitch. Already, citizens don’t know who to trust or what to believe – about COVID-19 or anything else. If we ask nothing more of tech providers or digital citizens, the fog will continue to thicken. Irritation will merge into despair and then numbness... and democracy teeters.Digital pioneer Cyrus Krohn knows the territory, and in Bombarded: How to Fight Back Against the Online Assault on Democracy, Krohn locates the roots of our blooming political chaos in the earliest days of the World Wide Web. But he goes beyond recounting 25 years of destabilizing Internet shock waves and his own role in building digital culture. Krohn rolls out a provocative action plan for rescuing the American system of campaigns and elections while there is still time.“Trying to shield yourself from disinformation and deep fakes? Cyrus Krohn offers a ‘five-step program’ to fight back. This book rings true.\"—Jill Dougherty, Former CNN Moscow Bureau Chief","","","","","en","","","","","","185","",""
"Journal Article","Long CD","","Essentially There: Higher Education Returns to Serve","Planning for Higher Education","Planning for Higher Education","2020","49","","1+","COVID Tracking Project","","","","go.gale.com","","","","","2020","","","0736-0983","","https://go.gale.com/ps/i.do?id=GALE%7CA652688300&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=07360983&p=AONE&sw=w;https://search.proquest.com/openview/4a0ebaf6ad56d764f43ef3b1056a3aae/1.pdf?pq-origsite=gscholar&cbl=47536&casa_token=HqZ5yQiLlzUAAAAA:NZ_6PX6tc0OX_CypMBFHPAZeaf0Xt_rD1EvJL_igdEfDveZSHj9n-JMi2jlXzEpqV2Jh_hPrxgM","","","","","","… According to the COVID-19 Tracking Project website: \"Less than 1 percent of America's population lives in long-term care facilities, but as of August 27, 2020, this tiny fraction of the country accounts for 43 percent of US COVID-19 deaths\" (The COVID Tracking Project …","Epidemics; Education; COVID-19; Educational facilities","","","","","","","","","","","",""
"Journal Article","Sauerheber R","","Characteristics of the Covid-19 Pandemic in the United States, 2020","Archives of Preventive Medicine","","2020","","","","COVID Tracking Project","","","","peertechzpublications.com","","","","","2020","","","","","https://www.peertechzpublications.com/articles/APM-5-121.php","","","","","","… Front Immunol 11: 1451. Link: https://bit.ly/2Et9Dpa; The COVID Tracking Project , The Atlantic, US Daily Cases. Link: https://bit.ly/2FVFz6p. Copyright. © 2020 Sauerheber R. This is an open-access article distributed under the …","","","","","","","","","","","","",""
"Review","Ioannidis JPA","","Reconciling estimates of global spread and infection fatality rates of COVID-19: an overview of systematic evaluations","Eur. J. Clin. Invest.","European journal of clinical investigation","2021","","","e13554","COVID Tracking Project","","","","Wiley","","","","","2021-03-26","","","0014-2972","1365-2362","https://onlinelibrary.wiley.com/doi/10.1111/eci.13554;http://dx.doi.org/10.1111/eci.13554;https://www.ncbi.nlm.nih.gov/pubmed/33768536;https://onlinelibrary.wiley.com/doi/abs/10.1111/eci.13554;https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/eci.13554","10.1111/eci.13554","33768536","","","","BACKGROUND: Estimates of community spread and infection fatality rate (IFR) of COVID-19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions. METHODS: Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates, and global representativeness. RESULTS: Six systematic evaluations were eligible. Each combined data from 10-338 studies (9-50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria, and biased eligibility criteria (e.g. excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78-100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only 1 study from other continents. Allowing for these caveats, 4 evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases. CONCLUSIONS: All systematic evaluations of seroprevalence data converge that SARS-CoV-2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5-2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries, and locations.","COVID-19; bias; global health; infection fatality rate; meta-analysis; seroprevalence","","http://onlinelibrary.wiley.com/termsAndConditions#vor","Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.","en","Review","","","","","","",""
"Journal Article","Frederick C,Girard C,Wong G,Lemire M,Langwieder A,Martin MC,Legagneux P","","Communicating with Northerners on the absence of SARS-CoV-2 in migratory snow geese","Écoscience","Écoscience","2021","","","1-7","COVID Tracking Project","","","","Taylor & Francis","","","","","2021-03-25","","","1195-6860","","https://doi.org/10.1080/11956860.2021.1885803;http://dx.doi.org/10.1080/11956860.2021.1885803;https://www.tandfonline.com/doi/abs/10.1080/11956860.2021.1885803?casa_token=8aSUVW_3IdMAAAAA:spCgXw-3f_p5EK3VZyDZSfEAH9ZRoANvJbyuQwwbTb3wG2gHwjHSDN0Bz2KNWZ9uj8j0wiX8DQkn1g;https://www.tandfonline.com/doi/pdf/10.1080/11956860.2021.1885803?casa_token=C8YmSl9VvqYAAAAA:kr5CDXfH-ZjErkTMN2O__GeMACOQWvg2l1ImZAlaSl0hmFKMx8oDV7dlcf43qhIFlGWJbfqCIndvIw","10.1080/11956860.2021.1885803","","","","","ABSTRACTThe COVID-19 pandemic has raised many concerns among Indigenous communities about virus transmission risks from wild food, particularly migratory birds. Snow geese contribute significantly to food security in Indigenous contexts, which is precarious in many communities. The risk to goose hunters is very unlikely as coronaviruses found in birds are from different genera than that of SARS-CoV-2, the etiologic agent responsible for COVID-19. Nevertheless, little is currently known about the host tropism range of SARS-CoV-2. To address the concerns raised by Northern communities, we captured 500 snow geese in May 2020 at their stopover along the St Lawrence estuary. We took oropharyngeal and cloacal samples before releasing the birds. All samples were tested for SARS-CoV-2 within one week and were found to be PCR-negative, allowing us to communicate rapidly with Northern communities. The current pandemic has shown that the importance of understanding animals as potential viral reservoirs, and that a better understanding of these viruses will better prepare us for future spillover events. This project demonstrates that researchers can be quickly and efficiently mobilized to respond to concerns from Indigenous communities.","","","","","","","","","","","","",""
"Report","Cui Z,Heal G,Kunreuther H,Liu L","","The Political Economy of Responses to COVID-19 in the U.S.A","","","2021","","","","COVID Tracking Project","","National Bureau of Economic Research","w28578","nber.org","","","","","2021-03-22","2021-04-02","","","","https://www.nber.org/system/files/working_papers/w28578/w28578.pdf;https://www.nber.org/papers/w28578;http://dx.doi.org/10.3386/w28578","10.3386/w28578","","","","","Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.","","","","","","","","","","","","",""
"Journal Article","Johnson-Agbakwu CE,Eakin CM,Bailey CV,Sood S,Ali N,Doehrman P,Bhattarai B,Chambliss L,Coonrod DV","","SARS-CoV-2: A Canary in the Coal Mine for Public Safety Net Hospitals","AJOG Global Reports","","2021","","","100009","COVID Tracking Project","","","","Elsevier","","","","","2021-03-21","","","2666-5778","","https://www.sciencedirect.com/science/article/pii/S2666577821000071;http://dx.doi.org/10.1016/j.xagr.2021.100009","10.1016/j.xagr.2021.100009","","","","","ABSTRACT Background The COVID-19 pandemic has exposed disproportionate health inequities among underserved populations, including refugees. Public safety net health care systems play a critical role in facilitating access to care for refugees, and informing coordinated public health prevention and mitigation efforts during a pandemic crisis. Objective To evaluate the prevalence of SARS-CoV-2 among refugee women admitted for delivery relative to non-refugee parturient patients. We suspect the burden of infection is disproportionately distributed across refugee communities which may act as sentinels for community outbreaks. Study Design A cross-sectional study was performed examining parturient women admitted to the maternity unit between May 6 and July 22, 2020, when universal testing for SARS-CoV-2 was first employed. Risk factors for SARS-CoV-2 positivity were ascertained, disaggregated by refugee status, and other clinical and socio-demographic variables examined. Prevalence ratios (PR) were calculated and comparisons made to county level community prevalence over the same time period. Results The percent positive at the County level during this study period was 21.6%. Of 350 women admitted for delivery, 33 (9.4%) screened positive for SARS-CoV-2. When disaggregated by refugee status, 45 (12.8%) were refugees, of whom 8 (17.8%) tested positive, compared to 25 (8.19%) non-refugee patients testing positive, PR 2.16 (95%CI 1.04-4.51). Seven of the SARS-CoV-2 positive tests were among refugees from Central Africa. Conclusion The SARS-CoV-2 outbreak has disproportionately affected refugee populations. This study highlights the utility of universal screening in mounting a rapid response to an evolving pandemic and how we can better serve the refugee community. Focused response may help achieve more equitable care related to SARS-CoV-2 among vulnerable communities. Identification of such populations may help mitigate spread and facilitate a timely, culturally and linguistically enhanced public health response.","COVID-19; SARS-CoV-2; universal testing; refugees; health disparities; health equity; public safety net; maternity care; Labor and Delivery","","","","","","","","","","","",""
"Journal Article","Pham AV,Adrian C,Garg M,Phang SY,Truong C","","State-level COVID-19 Outbreak and Stock Returns","Finance Research Letters","","2021","","","102002","COVID Tracking Project","","","","Elsevier","","","","","2021-03-19","","","1544-6123","","https://www.sciencedirect.com/science/article/pii/S1544612321000830;http://dx.doi.org/10.1016/j.frl.2021.102002;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7973052/;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7973052","10.1016/j.frl.2021.102002","","","","pmc7973052","We use state-level data to evaluate the connection between outbreaks of COVID-19 and stock returns over the period January-June 2020. We show that daily increases in the number of infected cases, hospitalized cases, and deaths are negatively associated with next day stock returns of firms headquartered in the same state. The relationship is weaker among states with high levels of medical resources and states that are likely to get support from the federal government. In addition, we find that the negative effect is reduced for firms that report an expectation that an outbreak will increase revenues and for firms with a strong corporate social responsibility practice. We believe our study is the first paper to assess cross-sectional stock price reactions to COVID-19 as a function of the state-level impact of the pandemic outbreak.","COVID-19; Stock Returns; Corporate Social Responsibility; State-Level Analysis","","","","","","","","","","","",""
"Preprint Manuscript","Long J,Khaliq A,Furati K","","Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-17","","","","","http://arxiv.org/abs/2103.09949","","","2103.09949","","","Data-driven deep learning provides efficient algorithms for parameter identification of epidemiology models. Unlike the constant parameters, the complexity of identifying time-varying parameters is largely increased. In this paper, a variant of physics-informed neural network (PINN) is adopted to identify the time-varying parameters of the Susceptible-Infectious-Recovered-Deceased model for the spread of COVID-19 by fitting daily reported cases. The learned parameters are verified by utilizing an ordinary differential equation solver to compute the corresponding solutions of this compartmental model. The effective reproduction number based on these parameters is calculated. Long Short-Term Memory (LSTM) neural network is employed to predict the future weekly time-varying parameters. The numerical simulations demonstrate that PINN combined with LSTM yields accurate and effective results.","","","","","","","","arXiv","2103.09949","math.DS","","","arXiv [math.DS]"
"Journal Article","Allchin D","","The facts of science & the values of social justice","Am. Biol. Teach.","The American biology teacher","2021","83","3","199-201","COVID Tracking Project","","","","University of California Press","","","","","2021-03-01","2021-04-02","","0002-7685","1938-4211","https://online.ucpress.edu/abt/article-abstract/83/3/199/116454;https://online.ucpress.edu/abt/article/83/3/199/116454/The-Facts-of-Science-amp-the-Values-of-Social;http://dx.doi.org/10.1525/abt.2021.83.3.199","10.1525/abt.2021.83.3.199","","","","","The theme of social justice has regained cultural urgency recently. Does science have any role to play? Certainly, when one thinks of addressing the disparities of power, profit, and privilege, one typically thinks of charities, social workers, political activists, or courtroom lawsuits. Not science. The world of facts is profoundly different from the realm of values. Reasoning from ­empirical evidence is unlike reasoning from ethical principles. So, no (most might contend), objective science seems to transcend social issues, with all their subjectivity.Here, however, I wish to challenge this view (this month’s Sacred Bovine) and show how, in some cases, science is most decidedly relevant to social justice (see also Yacoubian & Hansson, 2020; Shmaefsky, 2020). Further, this connection can be an effective tool to engage students who might otherwise regard abstract science as aloof from human concerns.First, it may be helpful to review just how facts and values are related. No amount of observation or measurement, alone, will reveal or justify an ethical principle. Facts cannot be converted into values or vice versa – however much some people try to conflate them or blur the distinction. Facts describe what is, values set norms of what should be. Their modes of justification differ. Still, scientific facts can valuably inform our reasoning about values. For example, science can help document cases of injustice. Once the basic values have been established (independently!), science can help establish context, illuminate causes, elucidate consequences, or gauge the likely effectiveness of prospective solutions. Science can vitally inform – as illustrated in the following cases.Consider the popular biology topic of DNA-based identification. Teachers often allude to the forensic use of DNA to find or confirm the culprit of a crime. But consider the converse. DNA evidence can also help determine who is innocent. Or who has been wrongly convicted. For over two decades, the Innocence Project (2020), a legal initiative, has used DNA testing to help exonerate persons imprisoned for crimes they did not commit. Since 1989, over 375 victims of injustice have been freed.But the ethical context of science does not end there. The Innocence Project also analyzes the cases as an ensemble, looking for patterns. What can these cases collectively tell us about the root causes of injustice? While the DNA evidence helped clear the victims, what caused the wrongful conviction varies. Factors include (sadly) misused forensic science and lack of access to postconviction DNA testing. Our system needs more rigorous standards and more disciplined forensic practices to avoid scientific errors – and unjust verdicts.Eyewitness identification is often regarded as the most reliable form of evidence. “What could be more trustworthy than direct observation?,” one might suppose. Yet 69% of the cases resolved by the Innocence Project involved mistaken reports by witnesses; 84% of those cases involved misidentification by a surviving victim. The National Registry of Exonerations (2018), in their own analysis, found that this is the most important factor in cases of sexual assault. For many decades, psychologist Elizabeth Loftus has sounded the alarm about the vagaries of human memory and the pitfalls of eyewitness testimony (Loftus et al., 2019). The documented cases of injustice bring further weight to her claims and to the importance of heeding reliable science in securing criminal justice.Finally, the data on wrongful convictions reveal other, deeper patterns. Of the 375 DNA exonerees to date, 60% were African American. Of the cases of flawed eyewitness testimony, 42% have involved a cross-racial misidentification. Likewise, the National Registry of Exonerations (2018) documents that when groups of individuals are exonerated “as a result of a large-scale pattern of police perjury and corruption” (involving over 2500 exonerees across two decades) they are “overwhelmingly Black.” In other words, criminal injustice exhibits a strong racial bias. Jaythan Kendrick, freed on November 19, 2020, after serving 25 years for a murder he did not commit, fits the pattern well. He was misidentified by two witnesses, each originally coaxed into their testimony. Thus, people who want to pretend that there is no racial bias in the system – and thus that no remedial action is needed – are mistaken. Science does not determine the value of justice. But it does inform us how to achieve it. And lawyers are now pursuing systemic reforms based on the findings above.Using a similar style of reasoning, science can also inform us about the distribution of environmental risks and harms across diverse segments of the populace. Ethically, of course, the burdens should be borne fairly and evenly. But scientific analysis indicates that they are not, and how they are not.In 1984, in one of history’s worst environmental disasters, a chemical plant in Bhopal, India, leaked over 30 tons of methyl isocyanate gas into the surrounding residential community. Some 15,000 persons died. Over a half-million were injured. But the harm was not distributed evenly. The neighborhood was a shantytown. (What person of means would have chosen to live next to such an industry?) The suffering thus fell disproportionately on the poor.Bhopal may seem like an exceptional incident – a rare “accident.” But evidence is plentiful for equally dramatic “slow-motion Bhopals.” Exposure to pollution or toxic emissions may occur gradually, but with no less overall impact. For many years in the mid-20th century, hazardous waste disposal sites in the United States were more likely to be placed near communities of color (Commission for Racial Justice, 1987). The pattern continued. For example, in 2008, four million cubic yards of waste coal ash laced with mercury, lead, and arsenic was moved from a flooded plant in Tennessee to Uniontown, Alabama. A cleanup of the toxic sludge was needed – “of course.” But why was it deposited in a small community with a median income of $14,000 and a population that was 90% Black? (Earthjustice, 2014; Milman, 2018). In 2014–2016 (in a case that students may still recall), city leaders in Flint, Michigan, allowed aging lead pipes to contaminate the public water supply, affecting the mostly African American community where 45% were living below the poverty line. Again, poverty and race featured prominently.Similarly, the risks of climate change are not borne equally. Those who contribute least to the problem are generally those most likely to suffer the consequences. Some nations have prospered through industrial production, as they exported the long-term costs of their fossil fuel emissions to the rest of the world. Meat diets, with the associated production of methane by cattle, are primarily a prerogative of the affluent. When climate hardship comes, however, it will be the poor who are least able to afford or accommodate the changes. With increased flooding from superstorms and coastal surges from hurricanes (and probably rises in sea level in the future), those living in flood plains or along seacoasts will be more severely affected. Those areas, not surprisingly perhaps, are inhabited disproportionately by the poor. Scientists can see clearly that the effects of climate change will not be distributed fairly (Lahn, 2018; California Office of Environmental Health Hazard Assessment, 2020).Science has helped document and clarify these injustices. Often enough, decisions about where to locate industries that pose environmental risks are based on minimizing economic cost or reducing overall harm. The criteria generally do not include local environmental history. Thus, although a decision may seem neutral and “reasonable,” if it is layered on (and functions within) an existing injustice, it merely compounds the original injustice (Shue, 1992). Ultimately, poverty itself begets further injustice – ironically, under a deceptive rationale of apparent fairness. “Reduction of risk” overall does not mean that individuals are equally protected. Scientific analysis can importantly expose how inequities result, and thus how this very form of reasoning is flawed.Other studies have shown that poverty is not the only factor in environmental disparities. For example, a 2016 study found that most toxic emissions nationwide come from just a handful of polluters and that, even when one controls for poverty as a factor, the sources are disproportionately situated near communities of color (Collins et al., 2016). Another study in 2018 examined exposure to fine particulate pollution, or soot, whether from automobile exhaust, smog, coal furnaces, oil smoke, ash, or construction dust. All lead to respiratory problems. Nationwide, African American communities – regardless of their urban, suburban, or rural setting – are more highly exposed to particulates (Newkirk, 2018). That is, there is evidence of racism. Not necessarily attributable to particular individuals, but deeply embedded in the socioeconomic system. Again, the science helps document the injustice and make it irrefutably and inescapably visible.Remedies may then ensue. In 2016, the U.S. Commission on Civil Rights heeded the evidence about Uniontown in concluding that when the Environmental Protection Agency (EPA) approved the transfer of all that coal ash waste there, it had violated the civil rights of residents. In 2019, a local court also acknowledged evidence of harm and directed the landfill operator to institute new safeguards (Walters, 2019). Based on this and other cases, national standards for the disposal of coal ash have now been adopted. However, the deeper systemic injustice will require broader changes in legislation and enforcement to fix. And while the circumstances are complex, science is disentangling the most significant causes and informing efforts at restoring justice (Diaz, 2017).The EPA formally instituted a program for environmental justice back in 1994. The values are clearly stated: “Fair treatment means no group of people should bear a disproportionate share of the negative environmental consequences resulting from industrial, governmental and commercial operations or policies” (https://www.epa.gov/environmentaljustice). However, the science is essential in characterizing the inequity and in determining how best to solve it. The EPA now awards small grants for local projects. Recently, they have spent over $7 million annually. Over a period of 25 years, at least 1400 communities have benefited.Finally, one may consider the recent effects of the SARS-CoV-2 coronavirus. From a strictly biological perspective, one might contend that viruses are blind to race, ethnicity, and social class. The privileged and the impoverished would seem equally susceptible. Yet statistics gathered as the 2020 pandemic unfolded clearly indicated otherwise.Data inform us that some groups have experienced COVID-19’s adversity disproportionately. For example, Blacks are more than five times more likely to test positive for COVID-19. In four states, the comparative rate for Native Americans is over fivefold. Prisons and meat-processing facilities – both high-density – have been among the top hot spots. Of those infected, the poor are nearly four times more likely to need intensive care. In addition, Blacks and Hispanics are more likely to have underlying conditions (such as diabetes, heart disease, or asthma) that worsen the health effects of an infection (a concrete downstream effect, no doubt, of the environmental injustice noted above). In some states, Blacks are dying at a rate more than 2.5 times their share of the population – and not just because of genetics (Ogedegbe et al., 2020). Finally, Blacks are more likely to be exposed to infection risk: through service-industry jobs (with no work-at-home option), through crowded workplaces or housing, through greater reliance on public transport, and so on (­Turrentine, 2020; Van Beusekom, 2020; Wood, 2020).The statistics are just abstract numbers. But numbers, appropriately interpreted, tell a story. In this case, they are not really observations about the virus or the disease. Rather, they are indirect measures of the context: the social injustice in health and health care in the United States. As noted by the Centers for Disease Control and Prevention, the evidence of disparities, when coupled with underlying social values about fairness, can ideally inform our future practices on COVID testing and prevention. It may also inform our understanding of long-term health care policy in general.For students who may regard science as cold and remote from human affairs, the link between science and social justice can potentially be a revelation. It can be a gateway into learning science. The examples above offer ready connections to the standard topics of molecular genetics, human physiology, and human ecology. They offer compelling cases of the relevance of biology to social values.Curricular goals for science inevitably appeal to the importance of science in public and personal decision making. Yet it is remarkable, I think, that most curricular content ironically avoids such concrete engagement. Concepts are typically presented without cultural context. Even activities in “scientific practices” or “scientific inquiry” tend to drift to black-box exercises or investigations on simple or trivial topics. Perhaps those common lessons answer to what is perceived as a more pressing aim? – namely, what is manageable in a classroom. But do these alternatives help reach the targeted understanding about science in society? Usually not.Biology teachers are generally not trained in ethical discourse or the dynamics of political negotiations. But this does not mean that they are without resources for teaching about social justice. Good old-fashioned science – collecting evidence and reasoning toward reliable conclusions – is relevant to achieving social justice in our culture (Allchin, 2020). And perhaps the time is ripe to engage this more fully in the classroom?","","","","","en","","","","","","","",""
"Preprint Manuscript","Stoeckel LE","","One dad’s COVID-19 diary 1 year later","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03","","","","","psyarxiv.com/hcv46;http://dx.doi.org/10.31234/osf.io/hcv46;https://psyarxiv.com/hcv46/;https://psyarxiv.com/hcv46/download?format=pdf","10.31234/osf.io/hcv46","","","","","A dad sat down in March 2020 and started a Google doc to keep a record of resources for friends and family, track the progress of life and the response to the COVID-19 pandemic, and tried to stay sane. This continued for a year.","COVID-19; dad; resource","","","","","","","","","","","",""
"Website","Nandy S","","COVID emergency declaration and fintech digital payment companies' performance","","","2022","","","","COVID Tracking Project","","","","buscompress.com","","","","","2022","2021-04-02","","","","https://www.buscompress.com/uploads/3/4/9/8/34980536/riber_11-1_03_m20-902_51-62.pdf","","","","","","… The tracking website includes information from 50 states, 5 territories, and District of Columbia (https:// covidtracking . com /data/charts/us-daily-positive/). Data of Covid confirmed cases were then correlated with the abnormal returns of the five digital payment firms …","","","","","","","","","","","","",""
"Journal Article","Babino A,Magnasco MO","","Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions","Sci. Rep.","Scientific reports","2021","11","1","5183","COVID Tracking Project","","","","nature.com","","","","","2021-03-04","","","2045-2322","","http://dx.doi.org/10.1038/s41598-021-84679-8;https://www.ncbi.nlm.nih.gov/pubmed/33664380;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970858;https://doi.org/10.1038/s41598-021-84679-8;https://www.nature.com/articles/s41598-021-84679-8","10.1038/s41598-021-84679-8","33664380","","","PMC7970858","During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the [Formula: see text] parameter. Then, we postulate each jump in [Formula: see text] as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing [Formula: see text] to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ([Formula: see text] CI 190,000-210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.","","","","Laboratory of Integrative Neuroscience, Rockefeller University, New York, 10065, USA. ababino@rockefeller.edu. Laboratory of Integrative Neuroscience, Rockefeller University, New York, 10065, USA.","en","Research Article","","","","","","",""
"Journal Article","So MKP,Chu AMY,Tiwari A,Chan JNL","","On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics","Sci. Rep.","Scientific reports","2021","11","1","5112","COVID Tracking Project","","","","nature.com","","","","","2021-03-04","","","2045-2322","","http://dx.doi.org/10.1038/s41598-021-84094-z;https://www.ncbi.nlm.nih.gov/pubmed/33664280;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7933279;https://doi.org/10.1038/s41598-021-84094-z;https://www.nature.com/articles/s41598-021-84094-z","10.1038/s41598-021-84094-z","33664280","","","PMC7933279","The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of 'co-movement' of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pandemic control. The preparedness risk score contributed by countries in Asia is between 25% and 50% most of the time after February and America contributes around 40% in July 2020. The high preparedness risk contribution implies the importance of travel restrictions between those countries. The severity risk score of America and Europe contribute around 90% in December 2020, signifying that the control of COVID-19 is still worrying in America and Europe. We can keep track of the pandemic situation in each country using an online dashboard to update the pandemic risk scores and contributions.","","","","Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, China. immkpso@ust.hk. Department of Social Sciences, The Education University of Hong Kong, Hong Kong, China. LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China. School of Nursing, Hong Kong Sanatorium & Hospital, Hong Kong, China. Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, China.","en","Research Article","","","","","","",""
"Journal Article","Singh S,Shaikh M,Hauck K,Miraldo M","","Impacts of introducing and lifting nonpharmaceutical interventions on COVID-19 daily growth rate and compliance in the United States","Proc. Natl. Acad. Sci. U. S. A.","Proceedings of the National Academy of Sciences of the United States of America","2021","118","12","","COVID Tracking Project","","","","National Acad Sciences","","","","","2021-03-23","","","0027-8424","1091-6490","http://dx.doi.org/10.1073/pnas.2021359118;https://www.ncbi.nlm.nih.gov/pubmed/33658331;http://www.pnas.org/cgi/pmidlookup?view=long&pmid=33658331;https://www.pnas.org/content/118/12/e2021359118.short;https://www.pnas.org/content/pnas/118/12/e2021359118.full.pdf","10.1073/pnas.2021359118","33658331","","","","We evaluate the impacts of implementing and lifting nonpharmaceutical interventions (NPIs) in US counties on the daily growth rate of COVID-19 cases and compliance, measured through the percentage of devices staying home, and evaluate whether introducing and lifting NPIs protecting selective populations is an effective strategy. We use difference-in-differences methods, leveraging on daily county-level data and exploit the staggered introduction and lifting of policies across counties over time. We also assess heterogenous impacts due to counties' population characteristics, namely ethnicity and household income. Results show that introducing NPIs led to a reduction in cases through the percentage of devices staying home. When counties lifted NPIs, they benefited from reduced mobility outside of the home during the lockdown, but only for a short period. In the long term, counties experienced diminished health and mobility gains accrued from previously implemented policies. Notably, we find heterogenous impacts due to population characteristics implying that measures can mitigate the disproportionate burden of COVID-19 on marginalized populations and find that selectively targeting populations may not be effective.","COVID-19; compliance; lockdown measures; nonpharmaceutical interventions; public policy","","","Health Economics Research Center, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom. Political Economy Cluster, Hertie School, 10117 Berlin, Germany. Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, United Kingdom. Department of Economics and Public Policy, Business School, Imperial College London, London SW7 2AZ, United Kingdom; m.miraldo@imperial.ac.uk. Center for Health Economics and Policy Innovation, Business School, Imperial College London, London SW7 2AZ, United Kingdom.","en","Research Article","","","","","","",""
"Preprint Manuscript","Ng S","","COVID-19 and Estimation of Macroeconomic Factors","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-03-03","","","","","http://arxiv.org/abs/2103.02732","","","2103.02732","","","Covid-19 is a large shock to the economic system and just a few months of data changed the mean of many time series in significant ways. But while Covid-19 has economic consequences, it is not an economic shock. This creates a problem for the estimation of economic factors from the data. The persistence of its effects also suggests a need for unconventional predictors in factor-augmented regressions. This note uses covid indicators to adjust the post-covid data prior to factor estimation. The adjustment preserves the pre-covid variations in the factors estimates. The real activity factor in FRED-MD is estimated to be down nearly four standard deviations in March/April 2020. Using economic and covid factors to generate $h$ step ahead prediction errors, the JLN measure of uncertainty finds covid-19 to be the third most uncertain episode recorded since 1960.","","","","","","","","arXiv","2103.02732","econ.EM","","","arXiv [econ.EM]"
"Preprint Manuscript","Hunziker P","","Personalized-Dose COVID-19 Vaccination in a Wave of Virus Variants of Concern: Trading Individual Efficacy for Societal Benefit","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-27","2021-04-02","","","","https://papers.ssrn.com/abstract=3794249;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3794249;http://dx.doi.org/10.2139/ssrn.3794249;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3794249","10.2139/ssrn.3794249","","","","","Limited vaccine supplies and virus variants of concern threaten prolonging the COVID-19 pandemic. The elderly die more frequently but the younger drive disease transmission. Here, we explore strategies that trade individual vaccine efficacy for increased numbers of vaccinations by personalized vaccine dosing during the onset of a wave of virus variants of concern. The model incorporates U.S. demographic and epidemic data and vaccine characteristics. We find that broad, personalized-dose vaccination, trading individual efficacy for vaccination speed and societal benefit, mitigates an infection wave of highly infectious variants of concern better and overcomes the pandemic faster than conventional “elderly first” strategies.","COVID-19, vaccination, U.S.A., coronavirus, pandemic, strategy, modeling, variant of concern, public health, epidemiology, personalized medicine","","","","","","","","","","","","Available at SSRN 3794249"
"Preprint Manuscript","Bhandari S,Raju R","","Social Networks Analysis to Retrieve Critical Comments on Online Platforms","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-21","","","","","http://arxiv.org/abs/2102.10495","","","2102.10495","","","Social networks are rich source of data to analyze user habits in all aspects of life. User's behavior is decisive component of a health system in various countries. Promoting good behavior can improve the public health significantly. In this work, we develop a new model for social network analysis by using text analysis approach. We define each user reaction to global pandemic with analyzing his online behavior. Clustering a group of online users with similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social media have significant effect on public health and reducing the effect of global pandemic. In this work, we introduce a new approach to clustering habits based on user activities on social media in the time of pandemic and recommend a machine learning model to promote health in the online platforms.","","","","","","","","arXiv","2102.10495","cs.SI","","","arXiv [cs.SI]"
"Journal Article","Okut H","","Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory","Artificial Neural Networks and Deep Learning","","2021","","","","COVID Tracking Project","","","","intechopen.com","","","","","2021","","","","","https://www.intechopen.com/online-first/deep-learning-for-subtyping-and-prediction-of-diseases-long-short-term-memory","","","","","","The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated …","","","","","","","","","","","","",""
"Journal Article","Center W","","Early introductions and community transmission of SARS-CoV-2 variant B. 1.1. 7 in the United States","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2021.02.10.21251540v3.full.pdf","","","","","","… we downloaded (1) all SARS-CoV-2 genomes available on GISAID (gisaid.org; accessed on March 4, 2021) with “USA” listed as a location and (2) the total number of new COVID-19 cases for each state from December 2020 to February 2021 ( covidtracking . com ; …","","","","","","","","","","","","",""
"Journal Article","Ray KS","","Going Beyond the Data: Using Testimonies to Humanize Pedagogy on Black Health","J. Med. Humanit.","The Journal of medical humanities","2021","","","","COVID Tracking Project","","","","Springer","","","","","2021-02-12","","","1041-3545","1573-3645","http://dx.doi.org/10.1007/s10912-021-09681-7;https://www.ncbi.nlm.nih.gov/pubmed/33576930;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879396;https://doi.org/10.1007/s10912-021-09681-7;https://link.springer.com/article/10.1007/s10912-021-09681-7","10.1007/s10912-021-09681-7","33576930","","","PMC7879396","When health professions learners' primary pedagogical experience of Black people and how they become patients is through statistics, it becomes very easy for learners to think of Black people as data points rather than as individuals whose health is often at the mercy of racist institutions. When the human dimension of Black people's health is ignored, specifically the ways that poor health affects individual wellbeing, one of the barriers to proper health for Black patients is how to be seen and considered as a part of a larger problem of systemic racism and institutional injustices as well as individuals whose personal lives are affected by such larger problems. I propose an approach to health professions pedagogy-the experiential race testimonies (ERT) approach-that can change the way health professions learners understand and treat Black patients, thus changing the future of Black health. The ERT approach pairs population data analysis with analysis of personal testimonies and the experiences they convey.","Black health; Health education; Pedagogy; Racial disparities; Racial inequities; Racism","","","University of Texas Health Science Center at Houston, McGovern Medical School, McGovern Center for Humanities and Ethics, 6431 Fannin Street, JJL 450, Houston, TX, 77030, USA. keisha.s.ray@uth.tmc.edu.","en","Research Article","","","","","","",""
"Preprint Manuscript","Wang Q,Zhou Y,Chen X","","A Vector Autoregression Prediction Model for COVID-19 Outbreak","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-06","","","","","http://arxiv.org/abs/2102.04843","","","2102.04843","","","Since two people came down a county of north Seattle with positive COVID-19 (coronavirus-19) in 2019, the current total cases in the United States (U.S.) are over 12 million. Predicting the pandemic trend under effective variables is crucial to help find a way to control the epidemic. Based on available literature, we propose a validated Vector Autoregression (VAR) time series model to predict the positive COVID-19 cases. A real data prediction for U.S. is provided based on the U.S. coronavirus data. The key message from our study is that the situation of the pandemic will getting worse if there is no effective control.","","","","","","","","arXiv","2102.04843","stat.AP","","","arXiv [stat.AP]"
"Journal Article","Boyarsky BJ,Werbel WA,Durand CM,Avery RK,et al.","","Article type: Brief Communication","escholarship.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://escholarship.org/content/qt0973n7jc/qt0973n7jc_noSplash_ddfbdc4cc0e09f5c76e04e2372515072.pdf","","","","","","… the state level (including the District of Columbia and Puerto Rico) on March 25, 2020, using data from http:// covidtracking . com / . States were classified by COVID-19 burden as \"low\" (<50 cases PMP: 13 states); \"medium\" (50 …","","","","","","","","","","","","",""
"Journal Article","Tunzi P","","Building the Beloved Community: Christian Ethical Reflections on Race, Gender, and Family During COVID-19","New Horiz.","New horizons ","2021","","","","COVID Tracking Project","","","","scholarcommons.scu.edu","","","","","2021","","","1063-7389","","https://scholarcommons.scu.edu/cgi/viewcontent.cgi?article=1000&context=newhorizons#page=51","","","","","","Page 51. TUNZI: BUILDING THE BELOVED COMMUNITY 49 Building the Beloved Community: Christian Ethical Reflections on Race, Gender, and Family During COVID-19 Porsia Tunzi Jesuit School of Theology of Santa Clara …","","","","","","","","","","","","",""
"Preprint Manuscript","Ioannidis JP","","Benefit of COVID-19 Vaccination Accounting for Potential Risk Compensation","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-27","2021-04-02","","","","https://papers.ssrn.com/abstract=3773950;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3773950;http://dx.doi.org/10.2139/ssrn.3773950;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3773950","10.2139/ssrn.3773950","","","","","Newly developed vaccines have tremendous potential in the fight against the COVID-19 pandemic. Risk compensation is the phenomenon where people receiving vaccines or other preventive measures may subsequently increase their previously suppressed exposure risk. Here a parsimonious mathematical model is presented that aims to evaluate the benefit of vaccination to the vaccinated (index) person and others exposed to that person; and calculate the amount of risk compensation required to eliminate all the benefit or to halve the benefit. As shown, 2.5-fold increase in exposure may eliminate the benefit of a vaccine of moderate efficacy (E=0.6) unless the probability of infection in the population of interest is very high. With very high vaccine efficacy (E=0.95), substantial benefit is maintained except in situations where there is very low probability of infection in the population. If the vaccine efficacy decreases to 0.8, the benefit may get eroded easily with modest risk compensation. Risk compensation may markedly affect the benefit of COVID-19 vaccination, especially if vaccine efficacy in real-life or specific high-risk populations (e.g. nursing home residents) is not very high.","risk compensation; behavior; vaccines; COVID-19","","","","","","","","","","","","Available at SSRN 3773950"
"Journal Article","Dias FA,Chance J","","COVID-19, Public Charge Rules, and Immigrant Employment in the United States","","","2021","","","","COVID Tracking Project","","","","escholarship.org","","","","","2021-02-02","2021-04-02","","","","https://escholarship.org/uc/item/37f8w4sf;https://escholarship.org/content/qt37f8w4sf/qt37f8w4sf.pdf","","","","","","Author(s): Dias, Felipe A; Chance, Joseph | Abstract: This article examines the impact of the COVID-19 pandemic on immigrant employment in the United States using data from the Current Population Survey. It also provides the first evidence about the impact of the new public charge rules on the employment behavior of immigrants during the post-outbreak recovery. The authors find that among immigrants with household earnings at levels that make them susceptible to inadmissibility under the new rules, noncitizen status is associated with a 3.7% increase in employment among immigrant men. This effect is robust to inclusion of controls for socioeconomic characteristics and various fixed effects, and it is concentrated for men in states with below average unemployment benefit take-up. Findings also show that the differential employment effect is stronger in state-months with higher COVID-19 rates, suggesting that impacted workers may be increasing their workplace exposure to COVID-19.","Social and Behavioral Sciences","","","","","","","","","","","",""
"Journal Article","Hunziker P","","Minimizing loss of life in Covid-19 in a 100 day period in the USA by personalized-dose vaccination","Available at SSRN 3780070","","2021","","","","COVID Tracking Project","","","","papers.ssrn.com","","","","","2021","","","","","https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3780070;https://www.medrxiv.org/content/10.1101/2021.01.30.21250834v3.full-text","","","","","","… 2020. Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus' on January 25,2021 24 https:// covidtracking . com / data/national/hospitalization 25 Voysey M, Costa Clemens SA, Madhi SA et al …","","","","","","","","","","","","",""
"Journal Article","Ashley S,Ford LL,Boyd C,Eldridge M,Hatry H,et al.","","Nurture, Sustain, Expand","","","2021","","","","COVID Tracking Project","","","","imls.gov","","","","","2021","","","","","https://www.imls.gov/sites/default/files/2021-01/2021-aahc-evaluation-report.pdf","","","","","","… American–centered arts and cultural organizations and institutions, including African American museums and HBCUs. 5 “The COVID Racial Data Tracker,” Centers for Disease Control and Prevention, accessed October 13, 2020, https:// covidtracking . com /race …","","","","","","","","","","","","",""
"Journal Article","Wilkerson M,Rivero E","","Sociocritical Literacies and Computing with Data as a Window on the World","Online Seminar Series on Programming in","","","","","","COVID Tracking Project","","","","mkn-rcm.ca","","","","","","","","","","http://mkn-rcm.ca/wp-content/uploads/2021/01/OSSPME-Proceedings-January-24.pdf#page=21","","","","","","… times more likely to die of complications related to COVID-19 (see the COVID Racial Data Tracker; https:// covidtracking . com /race). We illustrate our approach through an early unit we developed focused on food and nutrition …","","","","","","","","","","","","",""
"Journal Article","Bassolas A,Sousa S,Nicosia V","","Diffusion segregation and the disproportionate incidence of COVID-19 in African American communities","J. R. Soc. Interface","Journal of the Royal Society, Interface / the Royal Society","2021","18","174","20200961","COVID Tracking Project","","","","royalsocietypublishing.org","","","","","2021-01","","","1742-5689","1742-5662","http://dx.doi.org/10.1098/rsif.2020.0961;https://www.ncbi.nlm.nih.gov/pubmed/33499765;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879774;https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0961?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2020.0961;https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2020.0961","10.1098/rsif.2020.0961","33499765","","","PMC7879774","One of the most concerning aspects of the ongoing COVID-19 pandemic is that it disproportionately affects people from some specific ethnic and socio-economic minorities. In particular, since from the beginning of the pandemic it has been clear that people from Black and African American backgrounds seem to be hit especially hard by the virus, creating a substantial infection gap. The observed abnormal impact on these ethnic groups could probably be due to the co-occurrence of other known risk factors, including co-morbidity, poverty, level of education, access to healthcare, residential segregation and response to cures, although those factors do not seem able to explain fully and in depth the excess incidence of infections and deaths among African Americans. Here, we introduce the concept of diffusion segregation, that is the extent to which a given group of people is internally clustered or exposed to other groups, as a result of mobility and commuting habits. By analysing census and mobility data on major US cities, we found that the weekly excess COVID-19 incidence and mortality in African American communities at the beginning of the COVID-19 pandemic is significantly associated with their level of diffusion segregation. The results confirm that knowing where people commute to, rather than where they live, is potentially much more important to contain and curb the spreading of infectious diseases.","COVID-19; ethnic segregation; human mobility; random walks; urban systems","","","School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK.","en","Research Article","","","","","","",""
"Preprint Manuscript","Murphy J,Devereaux A,Goodman NP,Koppl R","","Expert Failure and Pandemics: On Adapting to Life With Pandemics","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-26","2021-04-02","","","","https://papers.ssrn.com/abstract=3773846;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3773846;http://dx.doi.org/10.2139/ssrn.3773846;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3773846","10.2139/ssrn.3773846","","","","","In a pandemic, citizens and policy makers must rely on expert opinion. What are the institutional arrangements that allow for the best advice to come forward? Using the framework established by Koppl (2018) on expert failure, we analyze the COVID-19 pandemic to see where missteps in expertise occurred and suggest institutional arrangements to improve expert advice in future pandemics.","COVID-19, Expert Failure, Market Failure, Information Economics, Pandemic, Knowledge Problem","","","","","","","","","","","","Available at SSRN"
"Journal Article","Kopel J,Tenner T,Brower G","","Modeling of COVID-19 total hospitalizations in the United States","Respiratory and Critical …","","2021","","","","COVID Tracking Project","","","","pulmonarychronicles.com","","","","","2021","","","","","https://pulmonarychronicles.com/index.php/pulmonarychronicles/article/download/811/1683","","","","","","… METHODS Data on total US COVID-19 hospitalizations, pos- itive COVID-19 tests, and COVID-19 mortality were obtained from the CDC and the COVID-19 Tracking Project at the Atlantic (https:// covidtracking . com /data/ national) …","","","","","","","","","","","","",""
"Preprint Manuscript","He Y,Wai HT","","Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-20","","","","","http://arxiv.org/abs/2101.07938","","","2101.07938","","","This paper is concerned with the blind identification of graph filters from graph signals. Our aim is to determine if the graph filter generating the graph signals is first-order lowpass without knowing the graph topology. Notice that lowpass graph filter is a common prerequisite for applying graph signal processing tools for sampling, denoising, and graph learning. Our method is inspired by the Perron Frobenius theorem, which observes that for first-order lowpass graph filter, the top eigenvector of output covariance would be the only eigenvector with elements of the same sign. Utilizing this observation, we develop a simple detector that answers if a given data set is produced by a first-order lowpass graph filter. We analyze the effects of finite-sample, graph size, observation noise, strength of lowpass filter, on the detector's performance. Numerical experiments on synthetic and real data support our findings.","","","","","","","","arXiv","2101.07938","eess.SP","","","arXiv [eess.SP]"
"Preprint Manuscript","DeVerna MR,Pierri F,Truong B,Bollenbacher J,Axelrod D,Loynes N,Torres-Lugo C,Yang KC,Menczer F,Bryden J","","CoVaxxy: A global collection of English-language Twitter posts about COVID-19 vaccines","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-01-19","","","","","http://arxiv.org/abs/2101.07694","","","2101.07694","","","With a large proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about the vaccines spreading on social media. In this paper, we present a dataset of English-language Twitter posts about COVID-19 vaccines. We show statistics for our dataset regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also demonstrate how we are able to perform analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. We have developed a live dashboard to allow people to track hashtag changes over time. The dataset can be used in studies about the impact of online information on COVID-19 vaccine uptake and health outcomes.","","","","","","","","arXiv","2101.07694","cs.SI","","","arXiv [cs.SI]"
"Journal Article","Pujara K","","A Hybrid Probabilistic Approach for Table Understanding","","","2021","","","","COVID Tracking Project","","","","jaypujara.org","","","","","2021","","","","","https://www.jaypujara.org/pubs/2021/sun-aaai21/sun-aaai21.pdf","","","","","","Page 1. A Hybrid Probabilistic Approach for Table Understanding Kexuan Sun Harsha Rayudu Jay Pujara University of Southern California Information Sciences Institute kexuansu@usc.edu hrayudu@usc.edu jpujara@isi.edu …","","","","","","","","","","","","",""
"Journal Article","Raju R","","Machine learning model to detect emergency in the global pandemic","","","2021","","","","COVID Tracking Project","","","","ebot.gmu.edu","","","","","2021-01-11","2021-04-02","","","","http://ebot.gmu.edu/handle/1920/11953;https://ebot.gmu.edu/bitstream/handle/1920/11953/raju_ml.pdf?sequence=1&isAllowed=y","","","","","","It is crucial to use advanced machine learning models to improve disaster and emergency response in critical events around the world. In this paper, we introduce a new model, which can highlight the essential help that people need in times of emergency. Based on the user comments, we choose the emergency response that can use the optimal resources to address the maximum needs. The new features in the model help to analyze each person's response from political, social, and health perspectives. This approach helps to recognize different types of users to improve emergency response in the time of the global pandemic. Also, collecting pandemic data from different online resources, makes this research more powerful in feature extraction to improve the model accuracy based on emergency data. This model can help health applications to improve disaster response time and services.","covid-19; machine learning; Technical Report","","","","","","","","","","","",""
"Website","Haile-Mariam T","","[No title]","","","","","","","COVID Tracking Project","","","","","","","","","","2021-04-02","","","","https://www.researchgate.net/profile/Yolanda_Haywood2/publication/341445018_For_us_COVID-19_is_personal/links/5fea7d7892851c13fecfd1b2/For-us-COVID-19-is-personal.pdf","","","","","","… 3. American University. COVID Racial Data Tracker [Internet]. [Cited 2020 May 3]. Accessed from: https:// covidtracking . com /race on May 2, 2020. 4. Chomilo N, Heard-Garris N, DeSilva M, Blackstock U. The Harm Of A Colorblind Allocation Of Scarce Resources [Internet …","","","","","","","","","","","","",""
"Journal Article","Mohamud SA","","Sentiment analysis methods to mitigate negative effect of the COVID-19 pandemic","","","2021","","","","COVID Tracking Project","","","","jbox.gmu.edu","","","","","2021-01-11","2021-04-02","","","","http://jbox.gmu.edu/handle/1920/11952;http://jbox.gmu.edu/bitstream/handle/1920/11952/mohamud_sentiment-analysis.pdf?sequence=1&isAllowed=y","","","","","","The goal of this research is to determine crucial factors that played a role in the number of confirmed COVID-19 infections within a given location. We hypothesize that political bias plays a significant role in the rise of COVID-19 cases globally and nationally; specifically, in overriding scientific reasoning for the delay or lack of deploying national policies to address the pandemic. Methods: To determine the validity of our hypothe- sis, we performed a literature review that identified statistical information on 1) the origins of the virus, 2) the lethality of the virus, and 3) potential parties responsible for the creation and release of the virus. In addition to the literature review, our team performed a behavioral analysis using information extracted from social media platforms to identify and determine behavior patterns associated with specific words related to the virus","data mining; sentiment analysis; covid-19; Technical Report","","","","","","","","","","","",""
"Journal Article","Monica Gandhi MD","","see manuscript DOI for details","pashev.me","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://pashev.me/files/adjodah-2020.10.21.20208728v1.full.pdf","","","","","","… technical- documentation/file-layouts.html; Delphi's COVID-19 Surveillance Streams (covidcast) API description: Available at https://cmu-delphi.github.io/delphi-epidata/api/covidcast. html; The COVID-19 Tracking Project: Available at: https:// covidtracking . com /about; …","","","","","","","","","","","","",""
"Journal Article","Lee A,Nyang DH,Mohaisen D","","An Analysis of Users Engagement on Twitter During the COVID-19 Pandemic: Topical Trends and Sentiments","2020, Dallas, TX, USA, December 11 …","","2020","","","","COVID Tracking Project","","","","books.google.com","","","","","2020","","","","","https://books.google.com/books?hl=en&lr=&id=o9QREAAAQBAJ&oi=fnd&pg=PA73&dq=%22covidtracking.com%22&ots=RVdF19gpwb&sig=zzwYE2N3Ezgu5z8qNbaZFDdVwBE","","","","","","Page 95. An Analysis of Users Engagement on Twitter During the COVID-19 Pandemic: Topical Trends and Sentiments Sultan Alshamrani1 (B), Ahmed Abusnaina1, Mohammed Abuhamad2, Anho Lee3, DaeHun Nyang4, and …","","","","","","","","","","","","",""
"Journal Article","Alpert T,Lasek-Nesselquist E,Brito AF,Valesano AL,Rothman J,MacKay MJ,Petrone ME,Breban MI,Watkins AE,Vogels CBF,Russell A,Kelly JP,Shudt M,Plitnick J,Schneider E,Fitzsimmons WJ,Khullar G,Metti J,Dudley JT,Nash M,Wang J,Liu C,Hui P,Muyombwe A,Downing R,Razeq J,Bart SM,Murphy S,Neal C,Laszlo E,Landry ML,Cook PW,Fauver JR,Mason CE,Lauring AS,St George K,MacCannell DR,Grubaugh ND","","Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-02-12","","","","","http://dx.doi.org/10.1101/2021.02.10.21251540;https://www.ncbi.nlm.nih.gov/pubmed/33594373;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885932;https://doi.org/10.1101/2021.02.10.21251540;https://www.medrxiv.org/content/10.1101/2021.02.10.21251540v3.full-text","10.1101/2021.02.10.21251540","33594373","","","PMC7885932","The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a national public health concern in the United States because of its increased transmissibility. Over 500 COVID-19 cases associated with this variant have been detected since December 2020, but its local establishment and pathways of spread are relatively unknown. Using travel, genomic, and diagnostic testing data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. New York, which receives the most international travel from the UK, is likely one of the key hubs for introductions and domestic spread. Finally, we provide evidence for increased community transmission in several states. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.","","","","","en","Research Article","","","","","","",""
"Journal Article","Adiga A,Wang L,Hurt B,Peddireddy A,Porebski P,Venkatramanan S,Lewis B,Marathe M","","All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-03-13","","","","","http://dx.doi.org/10.1101/2021.03.12.21253495;https://www.ncbi.nlm.nih.gov/pubmed/33758893;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987052;https://doi.org/10.1101/2021.03.12.21253495;https://www.medrxiv.org/content/10.1101/2021.03.12.21253495v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/03/13/2021.03.12.21253495.full.pdf","10.1101/2021.03.12.21253495","33758893","","","PMC7987052","Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response. ACM Reference Format: Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In Proceedings of ACM Conference (Conference'17) . ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn.","","","","","en","Research Article","","","","","","",""
"Journal Article","Yogurtcu ON,Rodriguez Messan M,Gerkin RC,Belov AA,Yang H,Forshee RA,Chow CC","","A Quantitative Evaluation of COVID-19 Epidemiological Models","medRxiv","medRxiv : the preprint server for health sciences","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021-02-08","","","","","http://dx.doi.org/10.1101/2021.02.06.21251276;https://www.ncbi.nlm.nih.gov/pubmed/33564783;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872378;https://doi.org/10.1101/2021.02.06.21251276;https://www.medrxiv.org/content/10.1101/2021.02.06.21251276v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/08/2021.02.06.21251276.full.pdf","10.1101/2021.02.06.21251276","33564783","","","PMC7872378","Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.","","","","","en","Research Article","","","","","","",""
"Journal Article","Hunziker P","","Impact of personalized-dose vaccination in Covid-19 with a limited vaccine supply in a 100 day period in the USA","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.30.21250834v6.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/28/2021.01.30.21250834.full.pdf","","","","","","Page 1. Impact of personalized-dose vaccination in Covid-19 with a limited vaccine supply in a 100 day period in the USA Patrick Hunziker University Hospital Basel; University of Basel, Switzerland; CLINAM Foundation Brief title: “Covid-19 vaccination strategies” Contact …","","","","","","","","","","","","",""
"Journal Article","Reeves DB,Bracis C,Swan DA,Moore M,Dimitrov D,et al.","","Rapid vaccination and early reactive partial lockdown will minimize deaths from emerging highly contagious SARS-CoV-2 variants","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.02.21250985v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/03/2021.02.02.21250985.full.pdf","","","","","","Page 1. 1 2 3 Rapid vaccination and early reactive partial lockdown will minimize deaths from emerging 4 highly contagious SARS-CoV-2 variants 5 6 Daniel B Reeves1†, Chloe Bracis2†, David A. Swan1, Mia Moore1, Dobromir …","","","","","","","","","","","","",""
"Journal Article","Lundberg B,McDonald K","","Mandatory public health measures for COVID-19 are associated with improved mortality, equity and economic outcomes","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.02.11.21251580v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/24/2021.02.11.21251580.full.pdf","","","","","","… https://cdn2.sph.harvard.edu/wp- content/uploads/sites/94/2017/01/Robinson- Hammitt-OKeeffe-VSL.2018.03.23.pdf [||] Non-white includes Hispanic, Asian non-Hispanic, Black non-Hispanic, Pacific Islander, Native American; https:// covidtracking . com /race/dashboard …","","","","","","","","","","","","",""
"Journal Article","Echeverría-Estrada C,Rodríguez JM,et al.","","The COVID-19 pandemic, immigrants, and minority communities in the United States","Pandemia and health","","2020","","","","COVID Tracking Project","","","","javiermrodriguez.org","","","","","2020","","","","","https://javiermrodriguez.org/wp-content/uploads/2020/11/COVID-19-immigrants.pdf","","","","","","Page 1. 1 The COVID-19 pandemic, immigrants, and minority communities in the United States Carlos Echeverría-Estrada Javier M. Rodríguez Claudia Cáceres Claremont Graduate University Introduction Pandemics are rare …","","","","","","","","","","","","",""
"Journal Article","Patil U,Kostareva U,Hadley M,Manganello JA,Okan O,Dadaczynski K,Massey PM,Agner J,Sentell T","","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","International journal of environmental research and public health","2021","18","6","3301","COVID Tracking Project","","","","MDPI AG","","","","","2021-03-23","2021-04-02","","1661-7827","1660-4601","https://www.mdpi.com/1044578;https://www.mdpi.com/1660-4601/18/6/3301;http://dx.doi.org/10.3390/ijerph18063301;https://www.mdpi.com/1660-4601/18/6/3301/pdf","10.3390/ijerph18063301","","","","","The COVID-19 pandemic has been accompanied by rapidly emerging evidence, changing guidance, and misinformation, which present new challenges for health literacy (HL) and digital health literacy (DHL) skills. This study explored whether COVID-19-related information access, attitudes, and behaviors were associated with health literacy and digital health literacy among college students in the United States. Self-reported measures of health literacy, along with items on pandemic-related attitudes, behaviors, information sources, and social networks, were collected online using a managed research panel. In July 2020, 256 responses were collected, which mirrored the racial/ethnic and gender diversity of U.S. colleges. Only 49% reported adequate HL, and 57% found DHL tasks easy overall. DHL did not vary by HL level. In multivariable models, both HL and DHL were independently associated with overall compliance with basic preventive practices. Higher DHL, but not HL, was significantly associated with greater willingness to get a COVID-19 vaccine and the belief that acquiring the disease would negatively impact their life. On average, respondents discussed health with 4–5 people, which did not vary by HL or DHL measures. The usage of online information sources varied by HL and DHL. The study findings can inform future student-focused interventions, including identifying the distinct roles of HL and DHL in pandemic information access, attitudes, and behaviors.","","","https://creativecommons.org/licenses/by/4.0/","","en","","","","","","","",""
"Journal Article","Atalla E,Zhang R,Shehadeh F,Mylona EK,Tsikala-Vafea M,Kalagara S,Henseler L,Chan PA,Mylonakis E","","Clinical Presentation, Course, and Risk Factors Associated with Mortality in a Severe Outbreak of COVID-19 in Rhode Island, USA, April–June 2020","Pathogens","Pathogens","2020","10","1","8","COVID Tracking Project","","","","Multidisciplinary Digital Publishing Institute","","","","","2020-12-24","2021-04-02","","","","https://www.mdpi.com/2076-0817/10/1/8;http://dx.doi.org/10.3390/pathogens10010008;https://www.mdpi.com/2076-0817/10/1/8/pdf","10.3390/pathogens10010008","","","","","Long-term care facilities (LTCFs) have had a disproportionally high mortality rate due to COVID-19. We describe a rapidly escalating COVID-19 outbreak among 116 LTCF residents in Rhode Island, USA. Overall, 111 (95.6%) residents tested positive and, of these, 48 (43.2%) died. The most common comorbidities were hypertension (84.7%) and cardiovascular disease (84.7%). A small percentage (9%) of residents were asymptomatic, while 33.3% of residents were pre-symptomatic, with progression to symptoms within a median of three days following the positive test. While typical symptoms of fever (80.2%) and cough (43.2%) were prevalent, shortness of breath (14.4%) was rarely found despite common hypoxemia (95.5%). The majority of patients demonstrated atypical symptoms with the most common being loss of appetite (61.3%), lethargy (42.3%), diarrhea (37.8%), and fatigue (32.4%). Many residents had increased agitation (38.7%) and anxiety (5.4%), potentially due to the restriction measures or the underlying mental illness. The fever curve was characterized by an intermittent low-grade fever, often the first presenting symptom. Mortality was associated with a disease course beginning with a loss of appetite and lethargy, as well as one more often involving fever greater than 38 °C, loss of appetite, altered mental status, diarrhea, and respiratory distress. Interestingly, no differences in age or comorbidities were noted between survivors and non-survivors. Taking demographic factors into account, treatment with anticoagulation was still associated with reduced mortality (adjusted OR 0.16; 95% C.I. 0.06–0.39; p < 0.001). Overall, the clinical features of the disease in this population can be subtle and the symptoms are commonly atypical. However, clinical decline among those who did not survive was often rapid with patients expiring within 10 days from disease detection. Further studies are needed to better explain the variability in clinical course of COVID-19 among LTCF residents, specifically the factors affecting mortality, the differences observed in symptom presentation, and rate of clinical decline.","","","","","en","","","","","","","",""
"Journal Article","Ghilardi A,Ruiz-Mercado I,Navarrete A,Sturdivant E,et al.","","Plataforma de información geográfica de la UNAM sobre COVID-19 en México","MESA","","2020","","","","COVID Tracking Project","","","","smbb.mx","","","","","2020","","","","","https://smbb.mx/wp-content/uploads/2020/12/2020_24_3.pdf#page=40","","","","","","Page 40. BioTecnología, Año 2020, Vol. 24 No. 3 39 Plataforma de información geográfica de la UNAM sobre COVID-19 en México Adrian Ghilardi1, 2*, Ilse Ruiz-Mercado3, 2, Antonio Navarrete1, Emily Sturdivant1, 2, Roberto …","","","","","","","","","","","","",""
"Conference Paper","Alshamrani S,Abusnaina A,Abuhamad M,Lee A,Nyang D,Mohaisen D","","An Analysis of Users Engagement on Twitter During the COVID-19 Pandemic: Topical Trends and Sentiments","","","2020","","","73-86","COVID Tracking Project","","","","Springer International Publishing","","","Computational Data and Social Networks","","2020","","","","","http://dx.doi.org/10.1007/978-3-030-66046-8_7;https://link.springer.com/chapter/10.1007/978-3-030-66046-8_7;https://www.researchgate.net/profile/David_Mohaisen/publication/348197405_An_Analysis_of_Users_Engagement_on_Twitter_During_the_COVID-19_Pandemic_Topical_Trends_and_Sentiments/links/5ffd215c92851c13fe06b1c6/An-Analysis-of-Users-Engagement-on-Twitter-During-the-COVID-19-Pandemic-Topical-Trends-and-Sentiments.pdf","10.1007/978-3-030-66046-8_7","","","","","The outbreak of COVID-19 pandemic raised health and economic concerns. With social distancing along with other measures that are enforced in an attempt to limit the spread of the virus, our life has dramatically changed. During this period, the web and social media platforms have become the main medium for communication, expression, and entertainment. Such platforms are a rich source of information, enabling researchers to better understand how the pandemic affected the users’ everyday life, including interaction with and perception of different topics. In this study, we focus on understanding the shift in the behavior of Twitter users, a major social media platform used by millions daily to share thoughts and discussions. In particular, we collected 26 million tweets for a period of seven months, three months before the pandemic outbreak, and four months after. Using topic modeling and state-of-the-art deep learning techniques, the trending topics within the tweets on monthly-bases, including their sentiment and user’s perception, were analyzed. This study highlights the change of the public behavior and concerns during the pandemic. Users expressed their concerns on health services, with an increase of 59.24% in engagement, and economical effects of the pandemic (34.43% increase). Topics such as online shopping have had a remarkable increase in popularity, perhaps due to the social distancing, while crime and sports topics witnessed a decrease. Overall, various topics related to COVID-19 have witnessed an improved sentiment, alluding to users adoption to the pandemic and associated topics of the public discourse.","","","","","","","","","","","","",""
"Preprint Manuscript","Cohen JD","","A Mitigation Score for COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-02","","","","","http://arxiv.org/abs/2012.02628","","","2012.02628","","","This note describes a simple score to indicate the effectiveness of mitigation against infections of COVID-19 as observed by new case counts. The score includes normalization, making comparisons across jurisdictions possible. The smoothing employed provides robustness in the face of reporting vagaries while retaining salient features of evolution, enabling a clearer picture for decision makers and the public.","","","","","","","","arXiv","2012.02628","q-bio.OT","","","arXiv [q-bio.OT]"
"Journal Article","Fu L,Shi R,Ke J","","Impact of\" Stay at Home\" Policy on Covid-19 in the United States","World Scientific Research Journal","","2020","","","","COVID Tracking Project","","","","airitilibrary.com","","","","","2020","","","","","https://www.airitilibrary.com/Publication/alDetailedMesh?docid=P20190709001-202012-202012040003-202012040003-207-221","","","","","","… home-order.html. “Our Data.” The COVID Tracking Project, 2020, covidtracking . com /data. E-mail ：. 文章公開取用時，將寄通知信至您填寫的信箱地址. E-mail ：. 購物車中已有多篇文章，請問是否要先清除，或一併加入購物車中購買 …","","","","","","","","","","","","",""
"Journal Article","Boserup B,McKenney M,Elkbuli A","","Disproportionate Impact of COVID-19 Pandemic on Racial and Ethnic Minorities","Am. Surg.","The American surgeon","2020","86","12","1615-1622","COVID Tracking Project","","","","journals.sagepub.com","","","","","2020-12","","","0003-1348","1555-9823","http://dx.doi.org/10.1177/0003134820973356;https://www.ncbi.nlm.nih.gov/pubmed/33231496;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691116;https://journals.sagepub.com/doi/10.1177/0003134820973356?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://journals.sagepub.com/doi/abs/10.1177/0003134820973356;https://journals.sagepub.com/doi/full/10.1177/0003134820973356","10.1177/0003134820973356","33231496","","","PMC7691116","BACKGROUND: Health disparities are prevalent in many areas of medicine. We aimed to investigate the impact of the COVID-19 pandemic on racial/ethnic groups in the United States (US) and to assess the effects of social distancing, social vulnerability metrics, and medical disparities. METHODS: A cross-sectional study was conducted utilizing data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC). Demographic data were obtained from the US Census Bureau, social vulnerability data were obtained from the CDC, social distancing data were obtained from Unacast, and medical disparities data from the Center for Medicare and Medicaid Services. A comparison of proportions by Fisher's exact test was used to evaluate differences between death rates stratified by age. Negative binomial regression analysis was used to predict COVID-19 deaths based on social distancing scores, social vulnerability metrics, and medical disparities. RESULTS: COVID-19 cumulative infection and death rates were higher among minority racial/ethnic groups than whites across many states. Older age was also associated with increased cumulative death rates across all racial/ethnic groups on a national level, and many minority racial/ethnic groups experienced significantly greater cumulative death rates than whites within age groups ≥ 35 years. All studied racial/ethnic groups experienced higher hospitalization rates than whites. Older persons (≥ 65 years) also experienced more COVID-19 deaths associated with comorbidities than younger individuals. Social distancing factors, several measures of social vulnerability, and select medical disparities were identified as being predictive of county-level COVID-19 deaths. CONCLUSION: COVID-19 has disproportionately impacted many racial/ethnic minority communities across the country, warranting further research and intervention.","COVID-19; health disparities; racial disparities; social determinants of health","","","Department of Surgery, Division of Trauma and Surgical Critical Care, 14506Kendall Regional Medical Center, Miami, FL, USA. Department of Surgery, University of South Florida, Tampa, FL, USA.","en","Research Article","","","","","","",""
"Conference Paper","Samyoun S,Mondol AS,Stankovic JA","","CoPED: a smartwatch based voice cognitive assistant for the pandemic and beyond: demo abstract","","","2020","","","605-606","COVID Tracking Project","","","","Association for Computing Machinery","New York, NY, USA","","Proceedings of the 18th Conference on Embedded Networked Sensor Systems","Virtual Event, Japan","2020-11-16","2021-04-02","9781450375900","","","https://doi.org/10.1145/3384419.3430431;http://dx.doi.org/10.1145/3384419.3430431;https://dl.acm.org/doi/abs/10.1145/3384419.3430431?casa_token=OFFKzT8A2kEAAAAA:lT0dFKPgwIfYIFbdwOlRl6iBfPpT1Pk7PvNbRwg28jnGE6WB7X-ckrozBMG2CMV4t590ocU2M-ojzw;https://dl.acm.org/doi/pdf/10.1145/3384419.3430431?casa_token=BYF_7qyxcxoAAAAA:dm6zSu92J_8D2PqyDRtl4LeDYGtrudDM_GSdwXPqGwFUobVaG-IRPhci1t49uGxwn-5qfcGL7EcpMA","10.1145/3384419.3430431","","","","","The COVID-19 pandemic has brought significant changes in daily activities, such as, washing hands and wearing masks regularly. During a pandemic, it is crucial to follow the recommendations from physicians and experts for mental and physical well-being. Also, it is important to know the latest information on the pandemic situation. Although smartwatches are very popular for monitoring and assisting daily life activities, existing systems are not directed towards coping up with the \"new normal\" life during pandemic. Towards achieving this goal, we present CoPED, a comprehensive voice cognitive assistant on a smartwatch that reminds and assists people for different daily activities during the pandemic and beyond.","pandemic, cognitive assistant, smartwatch, voice interaction","","","","","","SenSys '20","","","","","",""
"Preprint Manuscript","Tam KM,Walker N,Moreno J","","Influence of State Reopening Policies in COVID-19 Mortality","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-13","","","","","http://arxiv.org/abs/2011.09279","","","2011.09279","","","By the end of May 2020, all states in the US have eased their COVID-19 mitigation measures. Different states adopted markedly different policies and timing for reopening. An important question remains in how the relaxation of mitigation measures is related to the number of casualties. To address this question, we compare the actual data to a hypothetical case in which the mitigation measures are left intact using a projection of the data from before mitigation measures were eased. We find that different states have shown significant differences in the number of deaths, possibly due to their different policies and reopening schedules. Our study provides a gauge for the effectiveness of the approaches by different state governments and can serve as a guide for implementing best policies in the future. It also indicates that the face mask mandate seems to correlate with the change in the death count more dramatically than other measures.","","","","","","","","arXiv","2011.09279","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Nabben K,Poblet M,Gardner-Stephen P","","The Four Internets of COVID-19: the digital-political responses to COVID-19 and what this means for the post-crisis Internet","rukanda.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://rukanda.com/kuda/docs/publications/conferences/ghtc2020/papers/1570645124.pdf","","","","","","… 09, 2020). [26] 'The COVID Tracking Project | The COVID Tracking Project.' 2020. [Online]. Available: https:// covidtracking . com / (accessed Apr. 09, 2020). [27] F Pennic, 'White House, IBM Partner to Fight COVID-19 Using Supercomputers'. 2020. [Online] …","","","","","","","","","","","","",""
"Preprint Manuscript","Lin BC,Chen YJ,Hung YC,Chen CS,Wang HC,Chern JL","","The Data Forecast in COVID-19 Model with Applications to US, South Korea, Brazil, India, Russia and Italy","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-11-05","","","","","http://arxiv.org/abs/2011.04738","","","2011.04738","","","In this paper, we firstly propose SQIARD and SIARD models to investigate the transmission of COVID-19 with quarantine, infected and asymptomatic infected, and discuss the relation between the respective basic reproduction number $R_0, R_Q$ and the stability of the equilibrium points of model. Secondly, after training the related data parameters, in our numerical simulations, we respectively conduct the forecast of the data of US, South Korea, Brazil, India, Russia and Italy, and the effect of prediction of the epidemic situation in each country. Furthermore, we apply US data to compare SQIARD with SIARD, and display the effects of predictions.","","","","","","","","arXiv","2011.04738","q-bio.PE","","","arXiv [q-bio.PE]"
"Journal Article","Liu X,Xu X,Li G,Xu X,Sun Y,Wang F,Shi X,Li X,et al.","","Differential impact of non-pharmaceutical public health","assets.researchsquare.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://assets.researchsquare.com/files/rs-60056/v3_stamped.pdf","","","","","","… 14 Daily coronavirus cases and deaths in the US (open): https://www.worldometers. info/coronavirus/country/us/. The number of daily reported cases in the US (open): https:// covidtracking . com /api … JAMA. 2020. doi:10.1001/jama.2020.6130. 6. https:// covidtracking …","","","","","","","","","","","","",""
"Thesis","Gaille M,Terral P,Askenazy P,Aubry R,Bergeron H,Becerra S,Blanchon D,Borraz O,Bonnefoy L,Borst G,Bourdelais P,Brugères F,Cambois E,Castel P,Charmes É,Chlous F,Cochoy F,Coutellec L,Cretin E,Chavalarias D,Deboulet A,Delage A,Cyrille D,Demoraes F,Didry C,Doraï K,Duboz P,Dupuy A,Eyraud B,Fassin E,Gaglio G,Gautier C,Girel M,Gouëset V,Grasland C,Gravel N,Gueye L,Hennette-Vauchez S,Ibos C,Israel-Jost V,Julliard R,Keck F,Kelly-Irving M,Khlat M,Lacroix T,Lagrange F,Landy F,Laugier S,Leblanc G,Lefebvre M,Le Tourneau FM,Luchini S,Macia E,Mallard A,March F,Meslé F,Mennesson C,Milcent C,Noiville C,Watel PP,Pintus PA,Robert J,Robine JM,Rousseau M,Teschl M,Thébaud-Sorger MA,Thomann B,Torny D,Valls-Russell J,Wang S,Worms F,Gaudron CZ,Zouache A","","Les sciences humaines et sociales face à la première vague de la pandémie de Covid-19 -Enjeux et formes de la recherche","","","2020","","","","COVID Tracking Project","","","","Centre National de la Recherche Scientifique ; Université Toulouse III - Paul Sabatier","","","","","2020-11","2021-04-02","","","","https://halshs.archives-ouvertes.fr/halshs-03036192/;https://halshs.archives-ouvertes.fr/halshs-03036192/file/Les%20sciences%20humaines%20et%20sociales%20face%20a%CC%80%20la%20premie%CC%80re%20vague%20de%20la%20pande%CC%81mie%20de%20Covid-19%20Enjeux%20et%20formes%20de%20la%20recherche%20%202%2012%202020.pdf","","","","","","La recherche en sciences humaines et sociales (SHS), à qui l’on pose régulièrement la question de son « utilité », a été massivement mobilisée dans la première partie de l’année 2020, tant par les médias et les institutions. Elle s’est montrée d’une grande réactivité, en adaptant ses calendriers et ses objectifs, en modifiant ses formats d’interventions (wébinaires, cours en distanciel). Chercheuses et chercheurs, enseignant(e)s-chercheurs ont été présents, et ce malgré des inégalités générées par le confinement dans le travail de recherche, notamment en termes de genre. Le présent travail a pour ambition de proposer à son lecteur une analyse mobilisant les travaux des SHS dans leur ensemble. Sans prétendre à l’exhaustivité, il tisse les fils, à travers les questions qu’il aborde, d’une discipline à une autre, composant un ensemble dans lequel les SHS entrent en résonance les unes avec les autres, déploient leur complémentarité, et créent une analyse commune, qu’elles relèvent plutôt des sciences sociales ou des humanités. Il a pour objectif de rendre manifeste un capital scientifique des SHS en tant que telles, pour aborder les différents questionnements que suscite la pandémie de Covid-19. La recherche actuelle en SHS sur la pandémie, sa gestion politique, et ses enjeux, ne s’élabore pas ex nihilo. Tout en prenant la mesure de la spécificité des temps présents, elle s’appuie sur un ensemble de cadres théoriques, de méthodes, d’analyses élaborés dans d’autres contextes, remobilisés, réactualisés, enrichis à la lumière des problématiques associées à la pandémie de Covid 19. Par ailleurs, le parti-pris de ce travail a été de tenir compte d’emblée de la dimension mondiale de la pandémie, et de ne pas s’en tenir à la situation française. Ainsi, plusieurs contextes nationaux, voire continentaux sont explorés sur tel ou tel point et la dimension mondiale de la pandémie y est prise en compte en tant que telle. Enfin, ce document s’intéresse aussi à la manière même dont les sciences humaines et sociales se sont mobilisées, en France, dans le contexte de la pandémie de Covid 19, aux formes collaboratives, aux pratiques pluridisciplinaires particulièrement adoptées face à cette pandémie. Il se structure en cinq parties : la première porte sur la manière dont les SHS font de la crise une question et un objet de connaissance (A – Du cadrage de la crise dans l’espace public à la crise comme objet de connaissance - l’exemple de la France). La seconde aborde un point saillant des analyses élaborées au cours des derniers mois, qui envisagent la pandémie comme un révélateur, voire un amplificateur d’enjeux pré-existants (B). Puis, la troisième partie s’intéresse aux sociétés et aux gouvernements confrontés à la pandémie (C), autrement dit aux formes de la gestion de la crise par le pouvoir politique, à la mobilisation des sciences et à l’exercice du pouvoir, ainsi qu’aux mesures prises et aux attitudes des populations au regard de ces mesures. La quatrième partie présente la façon dont le temps de la pandémie a été traversé de questionnements pour le futur, questionnements qui à leur tour impriment des orientations pour la recherche en SHS (D. Se réinventer en temps de pandémie). Enfin, la cinquième et dernière partie invite le lecteur à découvrir comment les SHS se sont mobilisées en temps de pandémie, comment elles ont collaboré et entrepris de documenter à chaud la crise sanitaire tout en acceptant de voir se renouveler questions, objets, méthodes sous l’effet de cette crise (E. Quand la crise invite aux collaborations et à une réflexion sur le « transfert » des connaissances).","","","","","fr","","","","","","","Centre National de la Recherche Scientifique ; Université Toulouse III - Paul Sabatier",""
"Preprint Manuscript","Wang L,Adiga A,Venkatramanan S,Chen J,Lewis B,Marathe M","","Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-27","","","","","http://arxiv.org/abs/2010.14491","","","2010.14491","","","The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-temporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.","","","","","","","","arXiv","2010.14491","cs.LG","","","arXiv [cs.LG]"
"Preprint Manuscript","Cooper M","","The Political Economy of Pandemic Response: 3rd Quarter (Update)","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-26","2021-04-02","","","","https://papers.ssrn.com/abstract=3719322;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3719322;http://dx.doi.org/10.2139/ssrn.3719322;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3719322","10.2139/ssrn.3719322","","","","","This paper provides the 3rd quarter update to an earlier analysis of the record on the first six months of the U.S. public policy response to the COVID-19 epidemic, The conclusion remains crystal clear:The U.S. had one of the worst policy responses to the pandemic in the world, certainly among large, high-income democracies, including Asian (e.g., South Korea, Japan, Taiwan), European (e.g., Germany, Denmark, Finland, Norway), and other nations (e.g., Australia, New Zealand).The update has stronger findings based on new sources of data and analysis.Three months of recent data, a period that was particularly harsh in the U.S;More than doubling the number of data sources to over 350, which include reports of multinational and national public health and economic institutions, academic papers, trade association analyses, detailed accounts from investigative journalism, and dozens of publications in the field of public administration.Evaluating and incorporating the recent Woodward book (Rage).Cross-national Comparisons: Comparative cross-national studies show the heavy price of bad policies in the U.S, compared to the results achieved by others in three areas:Public Health: 200,000 avoidable deaths, six million avoidable infections and hundreds of thousands of hospitalizations.Economic: trillions of dollars of lost output and budget deficit, as well as millions of lost jobs.Political: a severely net negative perception of Trump’s handling of pandemic policy at home and abroad followed by a precipitous decline in his overall job approval and deterioration of his standing in the head-to-head match-up with Biden.Misrepresenting the Research on Good Policy to Defend the U.S. Failure: The single study (the Imperial College epidemiological report to the World Health Organization) on which the Trump administration relies for its claim of 2.2 million lives saved is misread and misrepresented. The analysis focuses how good things could have been under good policy.Under the “best” policy and the spread of the virus that results from it, the study projects the number of U.S. deaths between 6,600 and 24,000 over two years, not the 230,000 that will have perished in the U.S. in less than 10 months before election day.The projected results for good policy are close to the results achieved by other nations.These results are consistent with other epidemiological studies published shortly thereafter, which were dismissed by the Trump administration and its supporters as “political hit jobs.”The Flu: The effort to excuse the poor U.S. outcome by claiming COVID is like the flu fares no better under close scrutiny.In the U.S., COVID has already killed 4 times as many Americans as the most severe flu season in the past decade and 20 times as many as in the least severe flu season in that decade. COVID has killed over twice as many as the worst flu season in the last 50 years.Globally, COVID has killed only one-twentieth as many people who died in the worst flu season ever (1918-1919). In the U.S. it has already killed about one-third as many and that number of deaths continues to rise. The world appears to have learned something the Trump administration has not.The Complete Breakdown of Public Administration – Four dozen academic papers identify the principles for sound public administration during a crisis, demonstrating the cause and effect of the complete breakdown of public administration under Trump.Woodward’s book gives the “backstory” on the Trump administration’s response, recounting the president’s private thoughts and actions (or lack thereof),” Cooper said. “The data and policy analysis in the update give the publicly available “front-story.” They strongly agree.The Bottom Line for PolicyThe data and studies support the conclusions of others.Larry Hogan, the Republican governor of Maryland, that ‘Trump is his own worst enemy.”Bob Woodward, “Trump is the wrong man for the Job.”The New England Journal of Medicine, in a rare election editorial. “When it comes to the response to the largest public health crisis of our time, our current political leaders have demonstrated that they are dangerously incompetent.’”The tragic irony of the research is that the Trump administration misinterpreted and the advice of its own experts that it disregarded, is that there never was a conflict between good policy (known as non-pharmaceutical interventions) and the development of a vaccine. We could have had both, but we got neither in an avoidable year of suffering.There never was a tradeoff between public health and economic performance. Controlling the virus first was the key to minimizing its economic impact. The only conflict was between the lifecycle of the virus as dramatically altered by good policy, and the political spin cycle of the of Trump administration that undermined an effective U.S. response.Learning the lessons about how not to react to a pandemic in the biosphere is urgent, because we face an ongoing pandemic in the atmosphere, climate change, in which the Trump made exactly the same policy mistakes two years earlier.”","Source of COVID","","","","","","","","","","","","Available at SSRN 3719322"
"Journal Article","Gandhi M","","Decrease in Hospitalizations for COVID-19 after Mask Mandates in 1083 US Counties","medrxiv.org","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.medrxiv.org/content/10.1101/2020.10.21.20208728v1.full.pdf","","","","","","… technical- documentation/file-layouts.html; Delphi's COVID-19 Surveillance Streams (covidcast) API description: Available at https://cmu-delphi.github.io/delphi-epidata/api/covidcast. html; The COVID-19 Tracking Project: Available at: https:// covidtracking . com /about; …","","","","","","","","","","","","",""
"Journal Article","Redlener I,Sachs JD,Hansen S,et al.","","130,000-210,000 Avoidable Covid-19 Deaths—And Counting—In the US","New York: National","","2020","","","","COVID Tracking Project","","","","ncdp.columbia.edu","","","","","2020","","","","","http://ncdp.columbia.edu/custom-content/uploads/2020/10/Avoidable-COVID-19-Deaths-US-NCDP.pdf","","","","","","… 10 https://bera.house.gov/media-center/press-releases/bera-urges-administration- to-develop-national-testing-strategy 11 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7172645/ 12 https:// covidtracking . com /data/national 13 https://www.nejm.org/doi/full/10.1056/nejmp2014836 …","","","","","","","","","","","","",""
"Miscellaneous","Harvey AC","","Time series modeling of epidemics: Leading indicators, control groups and policy assessment","","","2021","","","","COVID Tracking Project","","","","Apollo - University of Cambridge Repository","","","","","2021-02-22","2021-04-02","","","","https://www.repository.cam.ac.uk/handle/1810/318300;http://dx.doi.org/10.17863/CAM.65417;https://www.repository.cam.ac.uk/bitstream/handle/1810/318300/cwpe2114.pdf?sequence=1","10.17863/CAM.65417","","","","","This article shows how new time series models can used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020) is extended to model the relationship between two or more series, and the role of common trends is discussed. Data on daily deaths from Covid-19 in Italy and the UK provides an example of leading indicators when there is balanced growth. When growth is not balanced, the model can be extended by including a nonstationary component in the leading series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy.","Balanced growth; Co-integration; Covid-19; Gompertz curve; Kalman filter; Stochastic trend; Working Paper","","","","","","","","","","","",""
"Preprint Manuscript","Nagafuchi Y,Lin Y,Mamgain K,Asudeh A,Jagadish HV,You,Wu,Yu C","","MithraDetective: A System for Cherry-picked Trendlines Detection","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-17","","","","","http://arxiv.org/abs/2010.08807","","","2010.08807","","","Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values over time. Our goal is to evaluate whether the 'trends' described by the extracted samples are representative of the true situation represented in the data. We demonstrate MithraDetective, a system to compute a support score to indicate how cherry-picked a statement is; that is, whether the reported trend is well-supported by the data. The system can also be used to discover more supported alternatives. MithraDetective provides an interactive visual interface for both tasks.","","","","","","","","arXiv","2010.08807","cs.DB","","","arXiv [cs.DB]"
"Preprint Manuscript","Ghosh S,Senapati A,Chattopadhyay J,Hens C,Ghosh D","","Optimal test-kit based intervention strategy of epidemic spreading in heterogeneous complex networks","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-10-15","","","","","http://arxiv.org/abs/2010.07649","","","2010.07649","","","We propose a deterministic compartmental model of infectious disease which considers the test-kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with analytical argument) is provided to reveal the effective reduction of final outbreak size and peak of infection as a function of basic reproduction number in a single patch. Further, to study the impact of long and short-distance human migration among the patches, we have considered heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test-kits in the fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as final outbreak size) significantly. A next-generation matrix based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: global airport networks and transportation networks of Kolkata, India.","","","","","","","","arXiv","2010.07649","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Journal Article","Serag H","","Covid-19 pandemic and the social determinants of health Social determinants of health and lessons for a fairer and more sustainable post-COVID world","","","2020","","","","COVID Tracking Project","","","","bmj.com","","","","","2020","","","","","https://www.bmj.com/sites/default/files/attachments/bmj-article/pre-pub-history/first_revised_article_30.10.20.pdf","","","","","","Page 1. Confidential: For Review Only Covid-19 pandemic and the social determinants of health Social determinants of health and lessons for a fairer and more sustainable post-COVID world Journal: BMJ Manuscript ID BMJ-2020-061105.R1 Article Type: Analysis …","","","","","","","","","","","","",""
"Journal Article","Zheng H,Bonasera A","","Chaos, percolation and the coronavirus spread: a two-step model","Eur Phys J Plus","European physical journal plus","2020","135","10","799","COVID Tracking Project","","","","Springer","","","","","2020-10-09","","","2190-5444","","http://dx.doi.org/10.1140/epjp/s13360-020-00811-z;https://www.ncbi.nlm.nih.gov/pubmed/33052299;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544563;https://link.springer.com/article/10.1140/epjp/s13360-020-00811-z/figures/10;https://link.springer.com/content/pdf/10.1140/epjp/s13360-020-00811-z.pdf","10.1140/epjp/s13360-020-00811-z","33052299","","","PMC7544563","We discuss a two-step model for the rise and decay of a new coronavirus (Severe Acute Respiratory Syndrome-CoV-2) first reported in December 2019, COVID-19. The first stage is well described by the same equation for turbulent flows, population growth and chaotic maps: a small number of infected, d 0 , grows exponentially to a saturation value, d ∞ . The typical growth time (aggressive spreading of the virus) is given by τ = 1 λ where λ is the Lyapunov exponent. After a time t crit determined by social distancing and/or other measures, the spread decreases exponentially analogous to nuclear decays and non-chaotic maps. Some countries, like China, S. Korea and Italy, are in this second stage while others including the USA are near the end of the growth stage. The model predicted 15,000 (±2250) casualties for the Lombardy region (Italy) at the end of the spreading around May 10, 2020. Without the quarantine, the casualties would have been more than 50,000, one hundred days after the start of the pandemic. The data from the 50 US states are of very poor quality because of an extremely late and confused response to the pandemic, resulting unfortunately in a large number of casualties, more than 70,000 on May 6, 2020, and more than 170,000 on August 21, 2020. S. Korea, notwithstanding the high population density ( 511 / km 2 ) and the closeness to China, responded best to the pandemic with 255 deceased as of May 6, 2020, and 301 on August 21, 2020.","","","","School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710119 China. Cyclotron Institute, Texas A&M University, College Station, TX 77843 USA. Laboratori Nazionali del Sud, INFN, 95123 Catania, Italy.","en","Research Article","","","","","","",""
"Journal Article","Gehrke M","","As fontes acionadas no Jornalismo Guiado por Dados durante a cobertura da Covid-19 The news sources used in Data-driven Journalism during the Covid-19 …","abraji-bucket-001.s3.sa-east-1","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://abraji-bucket-001.s3.sa-east-1.amazonaws.com/uploads/publication_info/details_file/37612c0f-16f3-4ca9-afed-acfbce9e172e/Marilia_Gehrke_As_fontes_acionadas_no_Jornalismo_Guiado_por_Dados_durante_a_cobertura_da_Covid-19_pdf.pdf","","","","","","… resultados fazem refletir, entre outras questões, sobre o tipo de discussão que o jornalismo pretende impulsionar em se tratando da pandemia. 14 Disponível em: https:// covidtracking . com /. Trata-se de uma iniciativa vinculada ao veículo de comunicação The …","","","","","","","","","","","","",""
"Journal Article","Tao R,Downs J,Beckie TM,Chen Y,McNelley W","","Examining spatial accessibility to COVID-19 testing sites in Florida","Ann. GIS","Annals of GIS","2020","26","4","319-327","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-10-01","","","1947-5683","","https://doi.org/10.1080/19475683.2020.1833365;http://dx.doi.org/10.1080/19475683.2020.1833365;https://www.tandfonline.com/doi/abs/10.1080/19475683.2020.1833365;https://www.tandfonline.com/doi/pdf/10.1080/19475683.2020.1833365","10.1080/19475683.2020.1833365","","","","","ABSTRACTMassive and rapid testing is crucial for containing the spread of COVID-19. Health and policy planners must ensure that access to and uptake of SARS-CoV-2 testing is adequate and equitable. This study measures the spatial accessibility to testing sites in Florida at the census tract level at the end of May 2020, using the 2-step floating catchment area method that integrates both driving and walking modes. Accessibility scores were found to be heterogeneous across geographic regions and among different groups of people. In particular, many rural areas were in a testing desert. While people in larger cities tended to have better accessibility to testing, many did not have adequate accessibility at that time due to both capacity limitations and spatial factors. In particular, people without access to private vehicles and the elderly faced disadvantages in accessibility to testing sites even in urban areas. However, Black and low-income groups were disproportionally concentrated in neighbourhoods with above-average accessibility due to their closer proximity to testing sites. These results suggest that increased efforts are needed to reach vulnerable populations, including the elderly and those without private vehicles.","","","","","","","","","","","","",""
"Journal Article","Huang L,Lu Z,Rajagopal P","","Numbers not Lives: AI Dehumanization Undermines COVID-19 Preventive Intentions","Journal of the Association for Consumer Research","Journal of the Association for Consumer Research","2020","","","","COVID Tracking Project","","","","The University of Chicago Press","","","","","2020-09-25","","","2378-1815","","https://doi.org/10.1086/711839;http://dx.doi.org/10.1086/711839;https://www.journals.uchicago.edu/doi/pdf/10.1086/711839?casa_token=iNFI5TnowbEAAAAA:lys7paibcsMmY_Trj0jh12a67l901-hnByb-NliD_rUcs8oYjaiUGLWb90DQo4x05gTuXRKL2Ba3","10.1086/711839","","","","","… publish data in a fully or partially manual manner (eg, Johns Hopkins Covid-19 dashboard, covidtracking . com ), other websites use artificial intelligence (AI) to do web scraping, data aggregation, and updates on their websites (eg, HealthMap). While the actual data generated …","","","","","","","","","","","","",""
"Journal Article","Borillo GA,Kagan RM,Baumann RE,et al.","","Pooling of Upper Respiratory Specimens Using a SARS-CoV-2 Real-time RT-PCR Assay Authorized for Emergency Use in Low-Prevalence Populations for High …","Open forum","","2020","","","","COVID Tracking Project","","","","academic.oup.com","","","","","2020","","","","","https://academic.oup.com/ofid/article-abstract/7/11/ofaa466/5913172;https://academic.oup.com/ofid/article/7/11/ofaa466/5913172","","","","","","… The average number of daily tests for SARS-CoV-2 performed in the United States has reached >700 000 (https:// covidtracking . com /data/us-daily); however, this increased demand for testing has put pressure on the laboratory supply chain, resulting in shortages of …","","","","","","","","","","","","",""
"Journal Article","Duque RB","","Black health matters too… especially in the era of Covid-19: how poverty and race converge to reduce access to quality housing, safe neighborhoods, and …","Journal of racial and ethnic health disparities","","2020","","","","COVID Tracking Project","","","","Springer","","","","","2020","","","","","https://link.springer.com/article/10.1007/s40615-020-00857-w","","","","","","… pulmonary conditions. Then, what surprised many was how African Americans around the nation seemed to be the most at risk. As of June 2020, 23,253 Black lives have been lost to Covid-19 ( Covidtracking . com ). That is about …","","","","","","","","","","","","",""
"Journal Article","Cot C,Cacciapaglia G,Sannino F","","Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing","Sci. Rep.","Scientific reports","2021","11","1","4150","COVID Tracking Project","","","","nature.com","","","","","2021-02-18","","","2045-2322","","http://dx.doi.org/10.1038/s41598-021-83441-4;https://www.ncbi.nlm.nih.gov/pubmed/33602967;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892828;https://doi.org/10.1038/s41598-021-83441-4;https://www.nature.com/articles/s41598-021-83441-4","10.1038/s41598-021-83441-4","33602967","","","PMC7892828","We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20-40% in the infection rate in Europe and 30-70% in the US.","","","","Institut de Physique des 2 Infinis (IP2I), CNRS/IN2P3, UMR5822, 69622, Villeurbanne, France. Université de Lyon, Université Claude Bernard Lyon 1, 69001, Lyon, France. Institut de Physique des 2 Infinis (IP2I), CNRS/IN2P3, UMR5822, 69622, Villeurbanne, France. g.cacciapaglia@ipnl.in2p3.fr. Université de Lyon, Université Claude Bernard Lyon 1, 69001, Lyon, France. g.cacciapaglia@ipnl.in2p3.fr. CP3-Origins & the Danish Institute for Advanced Study, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark. sannino@cp3.sdu.dk. Dipartimento di Fisica E. Pancini, Università di Napoli Federico II & INFN sezione di Napoli, Complesso Universitario di Monte S. Angelo Edificio 6, via Cintia, 80126, Napoli, Italy. sannino@cp3.sdu.dk.","en","Research Article","","","","","","",""
"Journal Article","McNamara KR,Newman AL","","The Big Reveal: COVID-19 and Globalization's Great Transformations","Int. Organ.","International organization","2020","74","S1","E59-E77","COVID Tracking Project","","","","Cambridge University Press","","","","","2020-12","2021-04-02","","0020-8183","1531-5088","https://www.cambridge.org/core/journals/international-organization/article/big-reveal-covid19-and-globalizations-great-transformations/56E7E235EE971A9E393CDFA4484CE561;http://dx.doi.org/10.1017/S0020818320000387","10.1017/S0020818320000387","","","","","Analysis of the post-COVID world tends to gravitate to one of two poles. For some, the pandemic is a crisis that will reshuffle the decks, producing a fundamental reordering of global politics. For others, the basic principles of the international order are likely to remain much the same, driven largely by the emerging bipolar system between the US and China. We find both narratives dissatisfying, as the former overinterprets the causal role of the pandemic itself, while the latter underappreciates the critical ways in which global politics have been transformed beyond the state-centered system of the Cold War. We argue instead that the pandemic exposes underlying trends already at work and forces scholars to open the aperture on how we study globalization. Most centrally, we contend that globalization needs to be seen not just as a distributional game of winners and losers but rather a more profoundly transformational game that reshapes identities, redefines channels of power and authority, and generates new sites for contentious politics. We draw on emerging work to sketch out a theoretical frame for thinking about the politics of globalization, and assess some of the key policy arenas where COVID-19 is accelerating the transformative effects of globalization. In so doing, we suggest a roadmap to a post-pandemic research agenda for studying global markets that more fully captures these transformations and their implications for world politics.","globalization; International Political Economy; political authority; identity; digital technology; climate change; economic statecraft; inequality; COVID-19; pandemic","","","","","","","","","","","",""
"Journal Article","de Oliveira PM","","Epidemics a la Stauffer","Physica A: Statistical Mechanics and its Applications","","2021","561","","125287","COVID Tracking Project","","","","Elsevier","","","","","2021-01-01","","","0378-4371","","https://www.sciencedirect.com/science/article/pii/S0378437120306798;http://dx.doi.org/10.1016/j.physa.2020.125287;https://www.sciencedirect.com/science/article/pii/S0378437120306798?casa_token=ZbcpDsYGafkAAAAA:0_wWUGGnMTZYOwQsS_xcFAuO7Q7zwYLX5TKQldSrevevMHFZLH17eRAVEQeXvTPxvX0JVg2aKbg","10.1016/j.physa.2020.125287","","","","","Simulations of a SIR-like model for epidemics with spatial distribution and movements of agents lead to dynamic phase transitions when part of the whole population stay confined, isolated from others. The control parameter is the fraction C of confined individuals, the isolation index. The order parameters P are diverse probabilities related to the possible transfer of the infection from one group to the other.","Dynamic critical phase transitions","","","","","","","","","","","",""
"Preprint Manuscript","Umar Z,Jareño F,Escribano AM","","Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era","","","2020","","","","COVID Tracking Project","","Research Square","","","","","","","2020-09-11","2021-04-02","","","","https://www.researchsquare.com/article/rs-75766/latest.pdf;https://www.researchsquare.com/article/rs-75766/v1;http://dx.doi.org/10.21203/rs.3.rs-75766/v1","10.21203/rs.3.rs-75766/v1","","","","","Abstract This paper explores the dynamic return and volatility connectedness for the three most relevant agricultural commodity markets (Soft, Grain and Livestock) and the Coronavirus Media Coverage Index (MCI) extracted from RavenPack. In concrete, we apply the fresh TVP-VAR methodology proposed by Antonakakis and Gabauer (2017) during the sample period between January 22, 2020 and July 31, 2020, that is in the context of the COVID-19 pandemic crisis. Interesting results are found in this research. First, dynamic total return and volatility connectedness fluctuates over time, reaching a peak during the heart of the global pandemic crisis, initially in the dynamic total return connectedness and later in the volatility one. Second, in the dynamic connectedness TO the system, we observe significant differences between the agricultural commodity markets in the level of the return connectedness measure. However, in the dynamic volatility connectedness TO, there are very few differences between some elements of the system, highlighting the Coronavirus MCI as one of the major transmitters TO the system at some points in the sample. This Coronavirus MCI appears as the less relevant receiver FROM the system, not only in terms of dynamic return connectedness, but also in volatility. Finally, regarding the net dynamic total connectedness, the Coronavirus MCI shows the highest values in return and volatility. Next, in order of highest to lowest net dynamic return and volatility connectedness we find the Grain commodity market, then the Soft market and finally the Livestock market. This last commodity market shows a negative net dynamic connectedness throughout the entire sample period analysed, exhibiting negative peaks in return at the beginning of the pandemic’s epicentre and in volatility in the middle of the COVID-19 crisis.","","","","Zayed University; University of Castilla-La Mancha","","","","","","","","","Research Square"
"Preprint Manuscript","Aparicio Fenoll A,Grossbard SA","","Are Covid Fatalities in the Us Higher than in the EU, and If so, Why?","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-15","2021-04-02","","","","https://papers.ssrn.com/abstract=3691396;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3691396;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3691396;https://www.econstor.eu/bitstream/10419/227210/1/dp13683.pdf","","","","","","The COVID crisis has severely hit both the United States and the European Union. Even though they are the wealthiest regions in the world, they differ substantially in economic performance, demographic characteristics, type of government, health systems, and measures undertaken to counteract COVID. We construct comparable measures of the incidence of the COVID crisis and find that US states had more COVID-related deaths than EU countries. When taking account of demographic, economic, and political factors (but not health-policy related factors) we find that fatalities at 100 days since onset are 1.3 % higher in a US state than in an EU country. The US/EU gap disappears when we take account of health-policy related factors. Differences in number of beds per capita, number of tests, and early lockdown measures help explain the higher impact of COVID on US fatalities measured either 50 or 100 days after the epidemic started in a nation/state.","COVID-19, mortality, Europe, US, health policy","","","","","","","","","","","",""
"Journal Article","Kost GJ","","Designing and Interpreting Coronavirus Disease 2019 (COVID-19) Diagnostics: Mathematics, Visual Logistics, and Low Prevalence","Arch. Pathol. Lab. Med.","Archives of pathology & laboratory medicine","2021","145","3","291-307","COVID Tracking Project","","","","meridian.allenpress.com","","","","","2021-03-01","","","0003-9985","1543-2165","http://dx.doi.org/10.5858/arpa.2020-0443-SA;https://www.ncbi.nlm.nih.gov/pubmed/32906146;https://meridian.allenpress.com/aplm/article-lookup/doi/10.5858/arpa.2020-0443-SA;https://meridian.allenpress.com/aplm/article-abstract/145/3/291/443500;https://search.proquest.com/openview/918d88502ee81deb50819f2bcf7365ae/1.pdf?pq-origsite=gscholar&cbl=42082&casa_token=ZCqymW51ekgAAAAA:aGoYOlUEjYaIK6Hj5Q1-mVnYRNgJkW03KHa00vmvhCK0g4KtivoqlwGpyOfeo8fCH139ps7N600","10.5858/arpa.2020-0443-SA","32906146","","","","CONTEXT.—: Coronavirus infectious disease-19 (COVID-19) diagnostics require understanding of how predictive values depend on sensitivity, specificity, and especially, low prevalence. Clear expectations, high sensitivity and specificity, and manufacturer disclosure will facilitate excellence of tests. OBJECTIVES.—: To derive mathematical equations for designing and interpreting COVID-19 tests, assess US Food and Drug Administration (FDA) Emergency Use Authorization and Health Canada minimum requirements, establish sensitivity and specificity tiers, and enhance clinical performance in low prevalence settings. DESIGN.—: PubMed and other sources generated articles on COVID-19 testing and prevalence. EndNote X9.1 consolidated references. Mathematica and open access software helped prove equations, perform recursive calculations, graph multivariate relationships, and visualize patterns, including a new relationship, predictive value geometric mean-squared. RESULTS.—: Derived equations were used to illustrate shortcomings of COVID-19 diagnostics in low prevalence. Visual logistics helped establish sensitivity/specificity tiers. FDA/Canada's 90% sensitivity, 95% specificity minimum requirements generate excessive false positives at low prevalence. False positives exceed true positives at prevalence lower than 5.3%, or if sensitivity is improved to 100% and specificity to 98%, at prevalence lower than 2%. Recursive testing improves predictive value. Three tiers emerged from these results. With 100% sensitivity, physicians can select desired predictive values, then input local prevalence, to determine suitable specificity. CONCLUSIONS.—: Understanding low prevalence impact will help health care providers meet COVID-19 needs for effective testing. Laypersons should receive clinical performance disclosure when submitting specimens. Home testing needs to meet the same high standards as other tests. In the long run, it will be more cost-effective to improve COVID-19 point-of-care tests rather than repeat testing multiple times.","","","","From Pathology and Laboratory Medicine; POCT•CTR, School of Medicine, University of California, Davis. Knowledge Optimization; Davis, California.","en","Research Article","","","","","","",""
"Preprint Manuscript","Taskinsoy J","","The Great Pandemic of the 21st Century: The Stolen Lives","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-09-09","2021-04-02","","","","https://papers.ssrn.com/abstract=3689993;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3689993;http://dx.doi.org/10.2139/ssrn.3689993;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3689993;https://www.researchgate.net/profile/John_Taskinsoy3/publication/344178561_The_Great_Pandemic_of_the_21st_Century_The_Stolen_Lives/links/5f5941f2299bf1d43cf9000d/The-Great-Pandemic-of-the-21st-Century-The-Stolen-Lives.pdf","10.2139/ssrn.3689993","","","","","One microscopic coronavirus has done what US sanctions, tariffs, embargoes, trade war, and the use of dollar as a weapon of economic destruction have failed to accomplish. The COVID-19 pandemic shock has caused unconceivable damage; 200,000 stolen lives in the U.S. (and close to 1 million in the world) and trillions of dollars globally. The farfetched impacts of coronavirus pandemic, the costliest in history (i.e. Great Lockdown), put many economies including the world’s biggest economy on a ventilator. Important signs provided by the coronavirus health crisis must not be ignored as previous signs were in the past. Tens of thousands of lives could have been saved if the White House (US President Donald Trump in particular) did not choose to downplay significant impacts of COVID-19; moreover, despite clear warnings by senior officials in late January 2020, not only Mr. Trump delayed taking aggressive actions to curb the spread of the virus to the United States (i.e. closing schools, locking down cities/states, imposing a travel ban, enforcing face masks, and social distancing), but he focused instead on protecting his re-election campaign; he also took the easy way out and blamed Beijing for misleading governments and not sharing the genome sequence of the coronavirus.","COVID-19; Coronavirus; Pandemic; Cost Implications; Credit Expansion","","","","","","","","","","","","Available at SSRN 3689993"
"Journal Article","Goods IP","","US ECONOMIC UPDATE AUGUST 2020","Policy","","2020","","","","COVID Tracking Project","","","","business.nab.com.au","","","","","2020","","","","","https://business.nab.com.au/wp-content/uploads/2020/08/us-economic-update-august-2020.pdf","","","","","","… 2000 2500 0 10000 20000 30000 40000 50000 60000 70000 80000 25-Feb-20 25-Apr-20 25-Jun-20 25-Aug-20 Daily reportedCOVID-19 new positive tests & deaths (number, 7 day ma) Source: Covidtracking . com , NAB new cases (LHS) deaths (RHS) NAB Group …","","","","","","","","","","","","",""
"Journal Article","Breitzman A","","A Data Scientist Looks at Covid19 Part II: Mythbusting","","","2020","","","","COVID Tracking Project","","","","rdw.rowan.edu","","","","","2020","","","","","https://rdw.rowan.edu/cgi/viewcontent.cgi?article=1178&context=csm_facpub","","","","","","… 1. The US is in a second wave. False. 2. We have an increase in cases because we are doing more testing. False • Note all data comes from https:// covidtracking . com /data/api Page 4. • The case for a second wave relies on the idea that cases peaked at …","","","","","","","","","","","","",""
"Thesis","Snow J","","Examining the Intersection of Environmental Justice, Chronic Disease, and Pandemics; How a Mobile Health App Could Improve Health Outcomes and Inform Policy","","","2020","","","","COVID Tracking Project","","","","repository.usfca.edu","","","","","2020","2021-04-02","","","","https://repository.usfca.edu/capstone/1081/;https://repository.usfca.edu/cgi/viewcontent.cgi?article=2269&context=capstone","","","","","","The purpose of this paper is to analyze the intersection of environmental justice, chronic disease and illness, and pandemics. The inequitable distribution of polluting factories, landfills, and hazardous waste sites have been a long-standing concern in the field of environmental justice. Local zoning codes and land use policies have been tools for segregating people and concentrating pollution in low-income communities and communities of color. Many studies have found that pollution varies among racial and minority groups, and the burden of pollution is not one that is evenly shared. Communities of color and low income communities are disproportionately affected by air pollution and experience higher rates of illness associated with increased exposure. In addition to increased rates of chronic disease, people of color and low income communities are disproportionately affected by respiratory pandemics, including influenza and Coronavirus (COVID-19). COVID-19 has highlighted the inequities that these vulnerable communities face and the compounding effects that air pollution can have on human health. This paper argues that current public health infrastructure does not capture the necessary data to inform policy and improve outcomes for those that are most affected. It calls for the development of a mobile health application to gather community level data, inform and educate residents on the environmental issues in their area, and act as a resource for both individuals and government entities during a pandemic.","","","","","","","","","","","","The University of San Francisco",""
"Website","Gu Q","","Epidemic model guided machine learning for COVID-19 forecasts","","","2020","","","","COVID Tracking Project","","","","pdfs.semanticscholar.org","","","","","2020","2021-04-02","","","","https://pdfs.semanticscholar.org/ab22/0bae5f9f669ba64d0479e5518ae5beede389.pdf","","","","","","… ICU γi1 γi2 γh2 γh1 For state level and country level models, hospitalization data are provided in (https:// covidtracking . com /) For county level model, hospitalization data are provided in (https://data.ca.gov/dataset) Page 10. Our Model Is Used by CDC Forecasts (Deaths …","","","","","","","","","","","","",""
"Journal Article","Thaler J","","The Next Surges Are Here: What Can American Governments Lawfully Do In Response to the Ongoing COVID-19 Pandemic?","Mitchell Hamline Law Journal of Public Policy","","2021","","","","COVID Tracking Project","","","","open.mitchellhamline.edu","","","","","2021","","","","","https://open.mitchellhamline.edu/cgi/viewcontent.cgi?article=1049&context=policypractice","","","","","","… COVID-19 cases and deaths rose in many “reopened” states after Memorial Day weekend. Key Metrics by State, COVID TRACKING PROJECT, https:// covidtracking . com /data/charts/all-metrics-per-state (last updated Aug. 28, 2020) …","","","","","","","","","","","","",""
"Review","Moore JT,Pilkington W,Kumar D","","Diseases with health disparities as drivers of COVID-19 outcome","J. Cell. Mol. Med.","Journal of cellular and molecular medicine","2020","24","19","11038-11045","COVID Tracking Project","","","","Wiley","","","","","2020-10","","","1582-1838","1582-4934","https://onlinelibrary.wiley.com/doi/10.1111/jcmm.15599;http://dx.doi.org/10.1111/jcmm.15599;https://www.ncbi.nlm.nih.gov/pubmed/32816409;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461081;https://onlinelibrary.wiley.com/doi/abs/10.1111/jcmm.15599;https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcmm.15599","10.1111/jcmm.15599","32816409","","","PMC7461081","The COVID-19 pandemic has forced our society to come face to face with complex issues that were once theoretical but are now being played out in real time. As data from the pandemic accumulates, it is clear that COVID-19 is impacting some parts of society more than others. Unfortunately, there is an almost complete overlap between COVID-19 risk factors and conditions that are already represented as health disparities, such as hypertension, diabetes, heart disease, lung disease and immune disorders. In this review, we discuss our current understanding of the physiological and pathophysiological pathways that link these diseases to COVID-19 outcome. An increased awareness of the factors underlying this issue, both societal and medical, is needed to understand the long-term implications and possible solutions needed going forward.","COVID-19; health disparities; risk factors","","http://creativecommons.org/licenses/by/4.0/","Julius L. Chambers Biomedical/Biotechnology Research Institute (JLC-BBRI), North Carolina Central University, Durham, NC, USA.; HOPE Program, JLC-BBRI, North Carolina Research Campus (NCRC), Kannapolis, NC, USA.; Julius L. Chambers Biomedical/Biotechnology Research Institute (JLC-BBRI), North Carolina Central University, Durham, NC, USA.; HOPE Program, JLC-BBRI, North Carolina Research Campus (NCRC), Kannapolis, NC, USA.","en","Review","","","","","","",""
"Preprint Manuscript","Chandrasekhar AG,Goldsmith-Pinkham PS,Jackson MO,Thau S","","Interacting Regional Policies in Containing a Disease","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-24","2021-04-02","","","","https://papers.ssrn.com/abstract=3680231;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3680231;http://dx.doi.org/10.2139/ssrn.3680231;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3680231;https://arxiv.org/pdf/2008.10745","10.2139/ssrn.3680231","","","","","Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments -- such as cities, counties, states, and countries -- act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward-looking and proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments -- those that wait for nontrivial internal infection rates before quarantining -- impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.","","","","","","","","","","","","","Available at"
"Preprint Manuscript","Liu X,Xu X,Li G,Xu X,Sun Y,Wang F,Shi X,Li X,Xie G,Zhang L","","Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States","","","2020","","","","COVID Tracking Project","","Research Square","","","","","","","2020-10-27","2021-04-02","","","","https://www.researchsquare.com/article/rs-60056/latest.pdf;https://www.researchsquare.com/article/rs-60056/v2;http://dx.doi.org/10.21203/rs.3.rs-60056/v2","10.21203/rs.3.rs-60056/v2","","","","","Abstract Background: The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain.Methods: Based on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between R t and NPIs through a generalized linear model (GLM). Results: Different NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46%-57%), wearing (face) masks 29% (15%-42%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel restriction 11% (5%-16%), school closure 10% (7%-14%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 7% (2%-11%).Conclusions: This retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.","","","","Ping An Healthcare Technology; Tsinghua University; Cornell University","","","","","","","","","Research Square"
"Preprint Manuscript","Yang Z,Xu J,Pan Z,Jin F","","COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08-10","","","","","http://arxiv.org/abs/2008.04285","","","2008.04285","","","The Coronavirus Disease 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease spreading dynamics in China and showing how the epidemic is taken under control in China.","","","","","","","","arXiv","2008.04285","cs.CY","","","arXiv [cs.CY]"
"Journal Article","Hinojosa-Ojeda R,Robinson S,Zhang J,Pleitez M,et al.","","Essential but Disposable: Undocumented Workers and Their Mixed-Status Families","","","2020","","","","COVID Tracking Project","","","","naid.ucla.edu","","","","","2020","","","","","http://www.naid.ucla.edu/uploads/4/2/1/9/4219226/essential_undocumented_workers_final.pdf","","","","","","Page 1. 1 Essential but Disposable: Undocumented Workers and Their Mixed-Status Families Modeling COVID-19 Economic Impacts and Government Relief Policies by Race and Immigration Status in Los Angeles County, California, and the United States …","","","","","","","","","","","","",""
"Journal Article","Nalbantoglu OU,Gundogdu A","","COVID-19 Pandemic: Group Testing","Front. Med.","Frontiers of medicine","2020","7","","522","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2020-08-18","","","2095-0217","2296-858X","http://dx.doi.org/10.3389/fmed.2020.00522;https://www.ncbi.nlm.nih.gov/pubmed/32974372;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461804;https://doi.org/10.3389/fmed.2020.00522;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461804/","10.3389/fmed.2020.00522","32974372","","","PMC7461804","","COVID-19; group testing; infection control; molecular diagnostics; pandemia","","","Department of Computer Engineering, Erciyes University, Kayseri, Turkey. Genome and Stem Cell Center (GenKok), Erciyes University, Kayseri, Turkey. Department of Microbiology and Clinical Microbiology, Erciyes University, Kayseri, Turkey.","en","Research Article","","","","","","",""
"Journal Article","Pezzutto M,Bono Rosselló N,Schenato L,Garone E","","Smart testing and selective quarantine for the control of epidemics","Annu. Rev. Control","Annual reviews in control","2021","","","","COVID Tracking Project","","","","Elsevier","","","","","2021-03-26","","","1367-5788","","https://www.sciencedirect.com/science/article/pii/S1367578821000092;http://dx.doi.org/10.1016/j.arcontrol.2021.03.001;https://arxiv.org/pdf/2007.15412","10.1016/j.arcontrol.2021.03.001","","","","","This paper is based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures. This decision problem is closely related to the problem of optimal sensor selection, which is a very active research subject in control engineering. The goal of this paper is to propose a policy to smartly select the individuals to be tested. The main idea is to model the epidemics as a stochastic dynamic system and to select the individual to be tested accordingly to some optimality criteria, e.g. to minimize the probability of undetected asymptomatic cases. Every day, the probability of infection of the different individuals is updated making use of the stochastic model of the phenomenon and of the information collected in the previous days. Simulations for a closed community of 10’000 individuals show that the proposed technique, coupled with a selective confinement policy, can reduce the spread of the disease while limiting the number of individuals confined if compared to the simple contact tracing of positive and to an off-line test selection strategy based on the number of contacts.","","","","","","","","","","","","",""
"Journal Article","Haran SN,David GO,Nadav Y,et al.","","Efficient and Practical Sample Pooling for High-Throughput PCR Diagnosis of COVID-19","https://www. medrxiv. org","","2020","","","","COVID Tracking Project","","","","covid-19.conacyt.mx","","","","","2020","","","","","https://covid-19.conacyt.mx/jspui/handle/1000/2854;https://covid-19.conacyt.mx/jspui/bitstream/1000/2854/1/1102842.pdf","","","","","","Page 1. Efficient and Practical Sample Pooling for High- Throughput PCR Diagnosis of COVID-19 Haran Shani-Narkiss, Omri David Gilday, Nadav Yayon, Itamar Daniel Landau* Center for Brain Sciences, Hebrew University of Jerusalem * itamar.landau@mail.huji …","","","","","","","","","","","","",""
"Journal Article","Ojinnaka CO,Adepoju OE,Burgess AV,Woodard L","","Factors Associated with COVID-Related Mortality: the Case of Texas","J Racial Ethn Health Disparities","Journal of racial and ethnic health disparities","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020-11-09","","","2196-8837","","http://dx.doi.org/10.1007/s40615-020-00913-5;https://www.ncbi.nlm.nih.gov/pubmed/33169310;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651831;https://www.researchsquare.com/article/rs-48149/latest.pdf","10.1007/s40615-020-00913-5","33169310","","","PMC7651831","BACKGROUND: Texas ranks 2nd in the count of COVID cases. Pre-existing disparities in healthcare may be intersecting with COVID-19 outcomes. OBJECTIVES: To explore the relationship between county-level race/ethnic composition and COVID-19 mortality in the state of Texas and determine whether county-level health factors, healthcare access measures, and other demographic characteristics explain this relationship. METHODS: This retrospective study uses county-level case and fatality data obtained from the Texas Department of State Health Services and merged with the 2020 Robert Wood Johnson foundation (RWJF) county health rankings data. The outcome variables were fatalities per 100,000 population. A two-part/hurdle model examined (1) the probability of having a COVID-19 fatality and (2) fatalities per 100,000 population in counties with 1+ fatalities. For both parts of the hurdle model, we examined the impacts of racial and ethnic composition, adjusting for county characteristics and health factors. RESULTS: The odds of having a COVID-19 fatality decreased with a unit increase in the rate of primary care physicians in a county (OR = 0.93; 95% CI = 0.89, 0.99). In the second part of the model, there was a statistically significant increase in COVID-19 fatalities/100,000 population with every 1 % increase in the proportion of Hispanics (β = 5.41; p = 0.03) and African Americans (β = 5.08; p value = 0.04). CONCLUSION: Counties with higher rates of minorities, specifically Hispanics and African Americans, have a higher COVID-19 fatality burden. Targeted interventions are needed to raise awareness of preventive measures in these communities.","COVID-19 mortality; County-level factors; Healthcare access; Racial disparities","","","College of Health Solutions, Arizona State University, Phoenix, AZ, USA. Humana Integrated Health System Sciences Institute, University of Houston, Houston, TX, USA. oadepoju@uh.edu. Department of Health Systems and Population Health Sciences, University of Houston College of Medicine, 4849 Calhoun Road, Bldg 2, Houston, TX, 77204, USA. oadepoju@uh.edu. Rice University, Houston, TX, USA. Humana Integrated Health System Sciences Institute, University of Houston, Houston, TX, USA. Department of Health Systems and Population Health Sciences, University of Houston College of Medicine, 4849 Calhoun Road, Bldg 2, Houston, TX, 77204, USA.","en","Research Article","","","","","","",""
"Journal Article","Sharma R,Kuohn LR,Weinberger DM,Warren JL,Sansing LH,Jasne A,Falcone G,Dhand A,Sheth KN","","Excess Cerebrovascular Mortality in the United States During the COVID-19 Pandemic","Stroke","Stroke; a journal of cerebral circulation","2021","52","2","563-572","COVID Tracking Project","","","","Am Heart Assoc","","","","","2021-01","","","0039-2499","1524-4628","http://dx.doi.org/10.1161/STROKEAHA.120.031975;https://www.ncbi.nlm.nih.gov/pubmed/33430638;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834664;https://www.ahajournals.org/doi/10.1161/STROKEAHA.120.031975?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed;https://Insights.ovid.com/pubmed?pmid=33430638;https://www.ahajournals.org/doi/abs/10.1161/STROKEAHA.120.031975;https://www.ahajournals.org/doi/pdf/10.1161/STROKEAHA.120.031975","10.1161/STROKEAHA.120.031975","33430638","","","PMC7834664","BACKGROUND AND PURPOSE: The magnitude and drivers of excess cerebrovascular-specific mortality during the coronavirus disease 2019 (COVID-19) pandemic are unknown. We aim to quantify excess stroke-related deaths and characterize its association with social distancing behavior and COVID-19-related vascular pathology. METHODS: United States and state-level excess cerebrovascular deaths from January to May 2020 were quantified using National Center for Health Statistic data and Poisson regression models. Excess cerebrovascular deaths were analyzed as a function of time-varying stroke-related emergency medical service (EMS) calls and cumulative COVID-19 deaths using linear regression. A state-level regression analysis was performed to determine the association between excess cerebrovascular deaths and time spent in residences, measured by Google Community Mobility Reports, during the height of the pandemic after the first COVID-19 death (February 29). RESULTS: Forty states and New York City were included. Excess cerebrovascular mortality occurred nationally from the weeks ending March 28 to May 2, 2020, up to a 7.8% increase above expected levels during the week of April 18. Decreased stroke-related EMS calls were associated with excess stroke deaths one (70 deaths per 1000 fewer EMS calls [95% CI, 20-118]) and 2 weeks (85 deaths per 1000 fewer EMS calls [95% CI, 37-133]) later. Twenty-three states and New York City experienced excess cerebrovascular mortality during the pandemic height. A 10% increase in time spent at home was associated with a 4.3% increase in stroke deaths (incidence rate ratio, 1.043 [95% CI, 1.001-1.085]) after adjusting for COVID-19 deaths. CONCLUSIONS: Excess US cerebrovascular deaths during the COVID-19 pandemic were observed and associated with decreases in stroke-related EMS calls nationally and mobility at the state level. Public health measures are needed to identify and counter the reticence to seeking medical care for acute stroke during the COVID-19 pandemic.","COVID-19; mortality; pandemic; public health; stroke","","","Department of Neurology, Division of Vascular Neurology, Yale School of Medicine, New Haven, CT (R.S., LR.K., L.H.S., A.J.). Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit (D.M.W.), Yale School of Public Health, New Haven, CT. Department of Biostatistics (J.L.W.), Yale School of Public Health, New Haven, CT. Department of Neurology, Division of Neurocritical Care and Emergency Neurology, New Haven, CT (G.F., K.N.S.). Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (A.D.). Network Science Institute, Northeastern University, Boston, MA (A.D.).","en","Research Article","","","","","","",""
"Journal Article","Lin YT,Neumann J,Miller EF,Posner RG,et al.","","Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States","Emerg. Infect. Dis.","Emerging infectious diseases","2021","","","","COVID Tracking Project","","","","ncbi.nlm.nih.gov","","","","","2021","","","1080-6040","","https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7920670/;https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7920670","","","","","pmc7920670","","","","","","","","","","","","","",""
"Journal Article","Burnette D,Buckley TD,Fabelo HE,Yabar MP","","Foregrounding Context in the COVID-19 Pandemic: Learning from Older Adults in Puerto Rico","J. Gerontol. Soc. Work","Journal of gerontological social work","2020","63","6-7","709-712","COVID Tracking Project","","","","Taylor & Francis","","","","","2020-08","","","0163-4372","1540-4048","http://dx.doi.org/10.1080/01634372.2020.1793253;https://www.ncbi.nlm.nih.gov/pubmed/32672141;https://www.tandfonline.com/doi/full/10.1080/01634372.2020.1793253;https://www.tandfonline.com/doi/abs/10.1080/01634372.2020.1793253?casa_token=XrdtUQOcB0kAAAAA:K0klB4aMP1MiJ785oT53Id0ajgpF6_9ZJlr_iDilp5XTqjQ2erYRCi1flndI4MDIQq-mY6DqiuuOtw;https://www.tandfonline.com/doi/pdf/10.1080/01634372.2020.1793253?casa_token=e9PWLWvD_ocAAAAA:o0M_CRuYPvJOQN-exs0iI98wDM8kd7-WqIRzyhr20qvRw6R9S7_kemZ729r7aF9WMN_KqbOesNPFnQ","10.1080/01634372.2020.1793253","32672141","","","","… pathways; nursing home residency; and health disparities – hospital and mortality rates are two times higher for African Americans and Latinos than non-Latino Whites in the US (Laurencin & McClinton, 2020; see COVID-19 Racial Data Tracker https:// covidtracking …","","","","Virginia Commonwealth University School of Social Work, Academic Learning Commons , Richmond, VA, USA, jdburnette@vcu.edu.","en","Research Article","","","","","","",""
"Journal Article","Escobar JV","","A Hawkes process model for the propagation of COVID-19: Simple analytical results","EPL","EPL","2020","131","6","68005","COVID Tracking Project","","","","IOP Publishing","","","","","2020-11-03","2021-04-02","","0295-5075","","https://iopscience.iop.org/article/10.1209/0295-5075/131/68005/meta;https://iopscience.iop.org/article/10.1209/0295-5075/131/68005;https://iopscience.iop.org/article/10.1209/0295-5075/131/68005/pdf;http://dx.doi.org/10.1209/0295-5075/131/68005;https://iopscience.iop.org/article/10.1209/0295-5075/131/68005/meta?casa_token=mK8pp2L2BnwAAAAA:OWfcMpQFJAlNaFWGBCNlmQVffLi5kG5ot6kPuU3K9UbuvjVBcOud2GLHq0KqCUKHnfX7g7Jv-go;https://iopscience.iop.org/article/10.1209/0295-5075/131/68005/pdf?casa_token=Pq0B6R7IEQ4AAAAA:-5ybGoh-F5oXQf1b8E51s98T8HbwP1MYn0NOZvbllbxXPSaBe97qF7AbDwx7zIkz3cfXbltYs-8","10.1209/0295-5075/131/68005","","","","","We present a model for the COVID-19 epidemic that offers analytical expressions for the newly registered and latent cases. This model is based on an epidemic branching process with latency that is greatly simplified when the bare memory kernel is given by an exponential function as observed in this pandemic. We expose the futility of the concept of “bending the curve” of the epidemic as long as the number of latent cases is not depleted. Our model offers the possibility of laying out different scenarios for the evolution of the epidemic in different countries based on the most recent observations and in terms of only two constants obtained from clinical trials.","","","","","en","","","","","","","",""
"Journal Article","최하현","","21 세기 코로나 시대에 부활한 19 세기의 복음: 낸시 톰스, 이춘입 역,[세균의 복음-1870\ 1930 년 미국 공중보건의 역사](푸른역사, 2019)","미국사연구","","2020","51","","219-226","COVID Tracking Project","","","","dbpia.co.kr","","","","","2020","","","","","https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE09350956","","","","","","… 검색일: 2020년 5월 23일). 2) 백인거주지에 비해 흑인 거주지역의 감염자 발생과 사망률이 현저히 높다는 통계결과 가 발표되었다. https:// covidtracking . com /race (검색일: 2020년 5월 23일). 3) Nancy Thomes, The Gospel of Germ …","","","","","","","","","","","","",""
"Journal Article","Vahabi N,Salehi M,Duarte JD,Mollalo A,Michailidis G","","County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States","Sci. Rep.","Scientific reports","2021","11","1","3088","COVID Tracking Project","","","","nature.com","","","","","2021-02-04","","","2045-2322","","http://dx.doi.org/10.1038/s41598-021-82384-0;https://www.ncbi.nlm.nih.gov/pubmed/33542313;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862666;https://doi.org/10.1038/s41598-021-82384-0;https://www.nature.com/articles/s41598-021-82384-0","10.1038/s41598-021-82384-0","33542313","","","PMC7862666","As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify \"vulnerable\" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25-Jun3, 2020), followed by similar data for 1344 counties (in the \"sunbelt\" region of the country) during the 2nd wave (Jun4-Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3-Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies \"more vulnerable\" clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3-2.1-3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08-0.52% MIR↑). We identified \"more vulnerable\" county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.","","","","Informatics Institute, University of Florida, Gainesville, FL, USA. Department of Biostatistics, College of Public Health, Iran University of Medical Sciences, Tehran, Iran. Center for Pharmacogenomics, Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, USA. Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA. Informatics Institute, University of Florida, Gainesville, FL, USA. gmichail@ufl.edu.","en","Research Article","","","","","","",""
"Journal Article","Persico CL,Johnson KR","","The effects of increased pollution on COVID-19 cases and deaths","J. Environ. Econ. Manage.","Journal of environmental economics and management","2021","107","","102431","COVID Tracking Project","","","","Elsevier","","","","","2021-05","","","0095-0696","","http://dx.doi.org/10.1016/j.jeem.2021.102431;https://www.ncbi.nlm.nih.gov/pubmed/33642653;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899033;https://linkinghub.elsevier.com/retrieve/pii/S0095-0696(21)00014-0;https://www.sciencedirect.com/science/article/pii/S0095069621000140?casa_token=ZuslLTgEXOoAAAAA:ZzVrKQ7rIW3sEuoV17B3_D4THdcxmLuGUIF_4Ue_WCPUOd9t5jNoMUe7zJbl1U7g_7UINNXTIMA","10.1016/j.jeem.2021.102431","33642653","","","PMC7899033","The SARS-COV-2 virus, also known as the coronavirus, has spread around the world. A growing literature suggests that exposure to pollution can cause respiratory illness and increase deaths among the elderly. However, little is known about whether increases in pollution could cause additional or more severe infections from COVID-19, which typically manifests as a respiratory infection. During the pandemic, the Environmental Protection Agency (EPA) rolled back enforcement of environmental regulation, causing an increase in pollution in counties with more TRI sites. We use the variation in pollution and a difference in differences design to estimate the effects of increased pollution on county-level COVID-19 deaths and cases. We find that counties with more Toxic Release Inventory (TRI) sites saw a 11.8 percent increase in pollution on average following the EPA's rollback of enforcement, compared to counties with fewer TRI sites. We also find that these policy-induced increases in pollution are associated with a 53 percent increase in cases and a 10.6 percent increase in deaths from COVID-19.","COVID-19; Health; Pollution; Regulation","","","Department of Public Administration and Policy, School of Public Affairs, American University and IZA, 4400 Massachusetts Avenue, Washington, DC, 20016, USA. Department of Public Administration and Policy, School of Public Affairs, American University, 4400 Massachusetts Avenue, Washington, DC, 20016, USA.","en","Research Article","","","","","","",""
"Journal Article","Aruru M,Gurewitsch R,Das S,Ghosh P,Sen B,Mukhopadhyay I,Pyne S","","A data-driven approach to COVID-19: Resources, policies, and best practices","BLDE University Journal of Health Sciences","BLDE University Journal of Health Sciences","2020","5","2","226","COVID Tracking Project","","","","Medknow Publications and Media Pvt. Ltd.","","","","","2020-07-01","2021-04-02","","2468-838X","","https://www.bldeujournalhs.in/article.asp?issn=2468-838X;year=2020;volume=5;issue=2;spage=226;epage=231;aulast=Aruru;http://dx.doi.org/10.4103/bjhs.bjhs_37_20","10.4103/bjhs.bjhs_37_20","","","","","The grand scale of the COVID-19 pandemic has impacted all aspects of human life. It has revealed worldwide many systemic deficiencies in understanding, preparedness, and control of the disease. To improve the situation, a data-driven approach can guide the use of resources, aid policies, and benefit from the best practices of data acquisition, sharing, and dissemination. Public health decision-making and action depend critically on the timely availability of reliable data. In this study, we described the data types and principles that are useful for better understanding of the pandemic. We focused on public policies such as lockdown and social distancing. We observed a possible impact of change in mobility on different urban populations in the US. Finally, we discussed the potential of objective policies such as limited and local lockdown to balance the dual goals of preventing contagion while also maintaining economic stability with careful consideration for vulnerable populations.","","","","","en","","","","","","","",""
"Journal Article","Bennett M","","Tag: COVID-19","kiej.georgetown.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/tag/covid-19/page/2/","","","","","","Skip to content …","","","","","","","","","","","","",""
"Journal Article","Leea SY,Leia B,Ndeffo-Mbahb ML,Mallicka BK","","Impact of state reopening on COVID-19 transmission, United States, 2020","","","2020","","","","COVID Tracking Project","","","","fids.tamu.edu","","","","","2020","","","","","https://fids.tamu.edu/wp-content/uploads/2020/07/CoronaReport.pdf","","","","","","… encounter- June 26, 2020 Page 3. density from baseline [10]; and (4) number of COVID-19 tests ( covidtracking . com /-). Daily number of tests as a covariate to account for possible increase in cases from increase testing. To evaluate …","","","","","","","","","","","","",""
"Journal Article","Bennett M","","Tag","kiej.georgetown.edu","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://kiej.georgetown.edu/tag/special-issue/page/2/","","","","","","Skip to content …","","","","","","","","","","","","",""
"Journal Article","Winsberg E,Brennan J,Surprenant CW","","How Government Leaders Violated Their Epistemic Duties During the SARS-CoV-2 Crisis","Kennedy Inst. Ethics J.","Kennedy Institute of Ethics journal","2020","30","3","215-242","COVID Tracking Project","","","","Johns Hopkins University Press","","","","","2020","2021-04-02","","1054-6863","1086-3249","https://muse.jhu.edu/article/773103/summary?casa_token=jVJ-5XTLxQwAAAAA:cKYcr9Wfw_DlFf3TpAuI8AQY_9rg7v4lWVvJoi3nWSrGcRDagnz8F0Lb6gKOkOYESacqY41oI9o;http://dx.doi.org/10.1353/ken.2020.0013;https://muse.jhu.edu/article/773103/pdf?casa_token=C14ovy9f5icAAAAA:GpXWvjJdWdvg7tCsjVlvI_janaOdANAbBusYpNOO8nQeDyhcV8jIY_VaYDQN3QZXjtnf8WD4qSM","10.1353/ken.2020.0013","","","","","ARRAY(0x558f3e647988)","","","","","","","","","","","","",""
"Journal Article","Furceri D,Loungani P,Ostry JD,Pizzuto P","","13 Pandemics and inequality: Assessing the impact of COVID‑191","COVID-19 in Developing","","2020","","","","COVID Tracking Project","","","","voxeu.org","","","","","2020","","","","","https://voxeu.org/system/files/epublication/Covid-19_in_developing_economies.pdf#page=201","","","","","","Page 201. 200 13 Pandemics and inequality: Assessing the impact of COVID‑191 Davide Furceri, Prakash Loungani, Jonathan D. Ostry, Pietro Pizzuto IMF and University of Palermo; IMF and Johns Hopkins University; IMF and …","","","","","","","","","","","","",""
"Preprint Manuscript","Lanman RB,Lanman TH","","SARS-CoV-2 serology results in the first COVID-19 case in California: A case report and recommendations for serology testing and interpretation","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-16","","","","","https://www.researchsquare.com/article/rs-35358/latest.pdf;https://www.researchsquare.com/article/rs-35358/v1;http://dx.doi.org/10.21203/rs.3.rs-35358/v1","10.21203/rs.3.rs-35358/v1","","","","","Abstract Background: As countries in COVID-19 pandemic lockdown begin relaxation of shelter-in-place mitigation strategies, the role of serology testing escalates in importance. However, there are no clear guidelines as to when to use qualitative rapid diagnostic serology tests (RDTs) vs. SARS-CoV-2 viral RNA load (PCR) tests as an aid in acute diagnosis of patients presenting with flu-like symptoms, nor how to interpret serology test results in asymptomatic individuals or those with atypical COVID-19 symptomatology. Here we describe, in the context of the likely first case of COVID-19 in California, with an atypical presentation and not tested acutely, who nearly 3 months later was found to be IgM- and IgG+ positive for SARS-CoV-2 antibodies, highlighting the role of RDT-based serology testing and interpretation in retrospective diagnosis.Case Presentation: A 62-year-old male practicing neurosurgeon had onset of flu-like symptoms on January 20 with fatigue, slight cough only on deep inspiration, intermittent pleuritic chest pain unrelated to exertion, dyspnea, and night sweats but without fever, sore throat or rhinorrhea. He had recently traveled abroad but not to China. CT scan revealed right lower lobe infiltrate and effusion. Because of atypical symptoms, and low prevalence of COVID-19 in January, community acquired pneumonia was diagnosed and one week of doxycycline was prescribed without relief, followed by a second week of azithromycin with symptom remission. Three months later the physician-patient (author THL), tested positive for SARS-CoV-2 antibodies by a serology point-of-care rapid diagnostic test (RDT).Conclusions: Serology testing may be an aid in acute diagnosis of COVID-19, especially in patients with atypical presentations, as well as in assessment of asymptomatic higher-risk persons such as healthcare workers for prior infection. Recommendations for serology testing and interpretation are explicated.","","","","Forward Medical, Inc.; UCLA David Geffen School of Medicine","","","","","","","","","Research Square"
"Journal Article","Ваславский ЯИ","","Агрегирование общественного выбора: партнерство государства и бизнеса в структурировании посткоронавирусной реальности","Федерализм","","2020","","","","COVID Tracking Project","","","","federalizm.rea.ru","","","","","2020","","","","","https://federalizm.rea.ru/jour/article/view/154;https://federalizm.rea.ru/jour/article/viewFile/154/151","","","","","","… Page 9. удалось довести количество тестов на душу населения до уровня мировых лидеров – Италии и Южной Кореи. По данным сайта Covidtracking . com , 10 апреля в стране было сделано около 2,4 млн тестов, что составило 730 шт. на 100 тыс. американцев …","","","","","","","","","","","","",""
"Journal Article","Li Y,Li Q,Zhang N,Liu Z","","Sunlight and vitamin D in the prevention of coronavirus disease (COVID-19) infection and mortality in the United States","","","2020","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2020","","","","","https://www.researchsquare.com/article/rs-32499/latest.pdf","","","","","","… We searched the incidence and mortality data in the United States from three different resources: the COVIDTracking Project (COVIDTracking, https:// covidtracking . com ), Centers for Disease Control and Prevention (CDC, https://www.cdc.gov/coronavirus/2019-nCoV …","","","","","","","","","","","","",""
"Journal Article","Sehra ST,Salciccioli JD,Wiebe DJ,Fundin S,Baker JF","","Maximum Daily Temperature, Precipitation, Ultraviolet Light, and Rates of Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in the United States","Clin. Infect. Dis.","Clinical infectious diseases: an official publication of the Infectious Diseases Society of America","2020","71","9","2482-2487","COVID Tracking Project","","","","academic.oup.com","","","","","2020-12-03","","","1058-4838","1537-6591","http://dx.doi.org/10.1093/cid/ciaa681;https://www.ncbi.nlm.nih.gov/pubmed/32472936;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314246;https://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa681;https://academic.oup.com/cid/article-abstract/71/9/2482/5849063;https://academic.oup.com/cid/article/71/9/2482/5849063?casa_token=htB5KaabY2UAAAAA:4J7uBXV6Tu8fWCv-omnkDN-K5GMln1jdoyjuXFkmcm1GcChQ2tBlu9-R0uKMbZDeC1RQLYFcQTM9gA","10.1093/cid/ciaa681","32472936","","","PMC7314246","BACKGROUND: Previous reports have suggested that transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is reduced by higher temperatures and higher humidity. We analyzed case data from the United States to investigate the effects of temperature, precipitation, and ultraviolet (UV) light on community transmission of SARS-CoV-2. METHODS: Daily reported cases of SARS-CoV-2 across the United States from 22 January 2020 to 3 April 2020 were analyzed. We used negative binomial regression modeling to determine whether daily maximum temperature, precipitation, UV index, and the incidence 5 days later were related. RESULTS: A maximum temperature above 52°F on a given day was associated with a lower rate of new cases at 5 days (incidence rate ratio [IRR], 0.85 [0.76, 0.96]; P = .009). Among observations with daily temperatures below 52°F, there was a significant inverse association between the maximum daily temperature and the rate of cases at 5 days (IRR, 0.98 [0.97, 0.99]; P = .001). A 1-unit higher UV index was associated with a lower rate at 5 days (IRR, 0.97 [0.95, 0.99]; P = .004). Precipitation was not associated with a greater rate of cases at 5 days (IRR, 0.98 [0.89, 1.08]; P = .65). CONCLUSIONS: The incidence of disease declines with increasing temperature up to 52°F and is lower at warmer vs cooler temperatures. However, the association between temperature and transmission is small, and transmission is likely to remain high at warmer temperatures.","COVID-19; SARS-CoV-2; temperature; transmission rates","","","Division of Rheumatology, Department of Medicine, Mount Auburn Hospital, Cambridge, Massachusetts, USA. Assistant Professor of Medicine, Harvard Medical School, Boston, Massachusetts, USA. Department of Medicine, Mount Auburn Hospital, Cambridge, Massachusetts, USA. Clinical Fellow in Medicine, Harvard Medical School, Boston, Massachusetts, USA. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Department of Health Sciences, Northeastern University, Boston, Massachusetts, USA. Division of Rheumatology, Department of Medicine and Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Division of Rheumatology, Department of Medicine at Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.","en","Research Article","","","","","","",""
"Journal Article","Jain CL","","Forecasting Covid-19 Cases and Deaths with the\" S\" Curve Model","J. Bus. Forecast. Methods Syst.","Journal of Business Forecasting Methods and Systems","2020","39","2","","COVID Tracking Project","","","","search.ebscohost.com","","","","","2020","","","0278-6087","","http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=1930126X&AN=144819820&h=5B86apm3m3Vm3O2pUue0AoGSUz6WEUdBB%2B5a%2BsLQt4Lo0k20oquyfurWmrtjHeT6YDS%2BdWMC%2FT6CQOnBxWK0XQ%3D%3D&crl=c","","","","","","… Week of June 15 1.23% .03% Source: 1. https://www.worldometers.info/coronavirus/ country/us/ 2. https:/ covidtracking . com /data/state/new-york#historical … Source: 1. https://www. worldometers.info/coronavirus/country/us/ 2. https:/ covidtracking . com /data/state/new-york …","","","","","","","","","","","","",""
"Journal Article","James JJ","","Are we “Waiting for Godot”- A Metaphor for Covid-19","Disaster Med. Public Health Prep.","Disaster medicine and public health preparedness","2020","14","3","297-298","COVID Tracking Project","","","","Cambridge University Press","","","","","2020-06","2021-04-02","","1935-7893","1938-744X","https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/are-we-waiting-for-godot-a-metaphor-for-covid19/1B4897852A8FFCC27CF6A04A5F89BA13;http://dx.doi.org/10.1017/dmp.2020.280;https://www.cambridge.org/core/services/aop-cambridge-core/content/view/1B4897852A8FFCC27CF6A04A5F89BA13/S1935789320002803a.pdf/are-we-waiting-for-godot-a-metaphor-for-covid-19.pdf","10.1017/dmp.2020.280","","","","","//static.cambridge.org/content/id/urn%3Acambridge.org%3Aid%3Aarticle%3AS1935789320002803/resource/name/firstPage-S1935789320002803a.jpg","","","","","","","","","","","","",""
"Preprint Manuscript","Sapiezynski P,Pruessing J,Sekara V","","The Fallibility of Contact-Tracing Apps","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-22","","","","","http://arxiv.org/abs/2005.11297","","","2005.11297","","","Since the onset of the COVID-19's global spread we have been following the debate around contact tracing apps -- the tech-enabled response to the pandemic. As corporations, academics, governments, and civil society discuss the right way to implement these apps, we noticed recurring implicit assumptions. The proposed solutions are designed for a world where Internet access and smartphone ownership are a given, people are willing and able to install these apps, and those who receive notifications about potential exposure to the virus have access to testing and can isolate safely. In this work we challenge these assumptions. We not only show that there are not enough smartphones worldwide to reach required adoption thresholds but also highlight a broad lack of internet access, which affects certain groups more: the elderly, those with lower incomes, and those with limited ability to socially distance. Unfortunately, these are also the groups that are at the highest risks from COVID-19. We also report that the contact tracing apps that are already deployed on an opt-in basis show disappointing adoption levels. We warn about the potential consequences of over-extending the existing state and corporate surveillance powers. Finally, we describe a multitude of scenarios where contact tracing apps will not help regardless of access or policy. In this work we call for a comprehensive and equitable policy response that prioritizes the needs of the most vulnerable, protects human rights, and considers long term impact instead of focusing on technology-first fixes.","","","","","","","","arXiv","2005.11297","cs.CY","","","arXiv [cs.CY]"
"Preprint Manuscript","Brennan J,Surprenant C,Winsberg E","","How Government Leaders Violated Their Epistemic Duties During the SARS-CoV-2 Crisis","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-05-20","2021-04-02","","","","https://papers.ssrn.com/abstract=3605981;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3605981;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3605981","","","","","","In spring 2020, in response to the COVID-19 crisis, world leaders imposed severe restrictions on citizens’ civil, political, and economic liberties. These restrictions went beyond less controversial and less demanding social distancing measures seen in past epidemics. Many states and countries imposed universal lockdowns. In this paper, we argue that these restrictions have not been accompanied by the epistemic practices morally required for their adoption or continuation. While in theory, lockdowns can be justified, governments did not meet and have not yet met their justificatory burdens.","COVID-19 government legitimacy","","","","","","","","","","","","Kennedy Institute Journal"
"Preprint Manuscript","Straka J","","COVID-19 transmission estimates and forecasts: A model to test sample selection bias in a ‘low risk’ crisis","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-06-09","2021-04-02","","","","https://www.preprints.org/manuscript/202005.0326;https://www.preprints.org/manuscript/202005.0326/v3;http://dx.doi.org/10.20944/preprints202005.0326.v3;https://www.preprints.org/manuscript/202005.0326/download/final_file","10.20944/preprints202005.0326.v3","","","","","This paper surveys estimates of the transmission features of the novel coronavirus, and then proposes a model to address sample-selection bias in estimated determinants of infection. Containment assumptions of the infection forecasting models depend on assumed effects of policies and self-regulating behavior. In the commons dilemma of the pandemic, the perceived ‘low risks’ of unregulated marginal choices do not reflect the full social cost, implying non-pharmaceutical interventions (NPI) to reduce mortality can enhance social welfare. As more economic activity renews with liftings of restrictive NPI (RNPI), a critical question concerns the ability of milder NPI (MNPI) and voluntary precautions to mitigate the risk of greater infections and deaths while also limiting the pandemic’s economic damage and its social costs. Ineffective NPI could lead to continued COVID-19 waves and new types of crises, worsened expectations and delayed economic recoveries. From the central range of surveyed estimates of transmission and alternative herd-immunity-threshold estimates, a ‘worst-case’ virus guidepost suggests eventual deaths of around 25 to 41 million worldwide and 1.1 to 1.7 million in the U.S. needed to reach herd immunity with no vaccine or treatment. The most optimistic study surveyed (theoretical model from a non-reviewed preprint study) combined with the low end of the range of the estimated mortality rate suggests 6 to 9 million deaths worldwide and 250 to 370 thousand in the U.S. to reach herd immunity. Successes in the mix of NPI, treatments, and vaccine can limit the eventual global death toll of the virus. Improved estimation models for forecasting and decision making may assist in better targeting the local timings and mix of NPI. Diagnostic tests for the virus have been largely limited to symptomatic cases, causing possible sample selection bias. A recursive bivariate probit model of infection and testing is proposed along with several possible applications from cross-section or panel-data estimation. Multiple potential explanatory variables, data sources, and estimation needs are specified and discussed.","COVID-19; SARS-Cov-2; coronavirus; sample selection bias; bivariate probit; social distancing; public goods; macroeconomic","","","","en","","","","","","","","Preprints"
"Journal Article","Tariq A,Undurraga EA,Laborde CC,Vogt-Geisse K,Luo R,Rothenberg R,Chowell G","","Transmission dynamics and control of COVID-19 in Chile, March-October, 2020","PLoS Negl. Trop. Dis.","PLoS neglected tropical diseases","2021","15","1","e0009070","COVID Tracking Project","","","","journals.plos.org","","","","","2021-01","","","1935-2727","1935-2735","http://dx.doi.org/10.1371/journal.pntd.0009070;https://www.ncbi.nlm.nih.gov/pubmed/33481804;https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857594;https://dx.plos.org/10.1371/journal.pntd.0009070;https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0009070&rev=2","10.1371/journal.pntd.0009070","33481804","","","PMC7857594","Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.","","","","Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America. Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Region Metropolitana, Chile. Millennium Initiative for Collaborative Research in Bacterial Resistance (MICROB-R), Santiago, Region Metropolitana, Chile. Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Region Metropolitana, Chile. Centro de Epidemiología y Políticas de Salud, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Region Metropolitana, Chile. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Region Metropolitana, Chile.","en","Research Article","","","","","","",""
"Journal Article","Sucahya PK","","Barriers to Covid-19 RT-PCR testing in Indonesia: A health policy perspective","J. Indones. health policy adm.","Journal of Indonesian health policy and administration","2020","5","2","","COVID Tracking Project","","","","Universitas Indonesia, Directorate of Research and Public Service","","","","","2020-05-10","","","2460-1330","2477-3832","http://journal.fkm.ui.ac.id/ihpa/article/view/3888;http://dx.doi.org/10.7454/ihpa.v5i2.3888;https://journal.fkm.ui.ac.id/ihpa/article/download/3888/1000","10.7454/ihpa.v5i2.3888","","","","","Abstract. Indonesia has been facing a hard time accelerating the number of laboratory capacity. This study provides an overview of the data on daily tests, confirmed cases, and the challenge of Covid-19 control associated with government policy. This study utilizes data reported from 2 March to 29 April 2020 by online official sources and regulations. Initially, the government only trusted one lab for the Covid-19 test. As the number of cases increased, referral labs were increased, until 29 April 2020, there were 89 officially appointed laboratories. The daily number of testing results fluctuated and unstable, although the number of reference labs increases. This reflects implementation challenges from different factors: readiness and capacity between labs; availability of swab collection officers; availability of reagents in the lab; rules for lab officers and swab collection officers; and transportation for a specimen from health facility to the referral lab. This study recommends to ensure the lab readiness in terms of human resources, tools, and reagents when appointed; ensure the adequacy and quality of qualified laboratory staff & swab collectors; ensure adequate reagents for RT-PCR, and rearrangement of shift rules for lab & swab collection officers, and improve handling procedures and transport specimen delivery mechanisms. Abstrak. Indonesia kesulitan melakukan percepatan jumlah kapasitas laboratorium (Lab). Tujuan studi mereview hasil testing harian, kasus terkonfirmasi, dan tantangan imlementasi penanganan Covid-19 dikaitkan dengan kebijakan pemerintah. Analisis studi memanfaatkan data sekunder, periode 2 Maret sampai 29 April 2020, yang dilaporkan berbagai website resmi dan kajian regulasi. Awalnya pemerintah hanya percaya satu lab untuk testing Covid-19. Seiring bertambahnya kasus, jumlah lab rujukan ditambah, sampai tanggal 29 April 2020 sudah 89 lab yang ditunjuk resmi. Hasil testing harian memperlihatkan angka yang berfluktuasi. Ini mencerminkan tantangan implementasi dari berbagai faktor: kesiapan dan kapasitas antar laboratorium; ketersediaan & kemampuan petugas pengumpul swab; ketersediaan reagen di laboratorium; aturan untuk petugas lab dan petugas pengumpul swab; dan transportasi untuk spesimen dari fasilitas kesehatan ke laboratorium rujukan. Studi ini merekomendasikan untuk memastikan kesiapan laboratorium dalam hal sumber daya manusia, alat, dan reagen ketika ditunjuk; memastikan kecukupan dan kualitas petugas lab & pengambil swab mumpuni; memastikan kecukupan logistik reagen untuk RT-PCR; dan penataan ulang aturan shift untuk petugas lab & pengumpul swab, dan meningkatkan prosedur penanganan dan mekanisme transportasi pengiriman specimen.","","","","","","","","","","","","",""
"Website","Dutta H","","[No title]","","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","2021-04-02","","","","https://www.researchgate.net/profile/Hrishikesh_Dutta/publication/341089678_Neural_Network_Model_for_Prediction_of_Covid-19_Confirmed_Cases_and_Fatalities/links/5eb2eeb0299bf152d69eccce/Neural-Network-Model-for-Prediction-of-Covid-19-Confirmed-Cases-and-Fatalities.pdf","","","","","","… IEEE, 2017. [4] www.kaggle.com/datasets [5] www. covidtracking . com /api [6] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Trans. Neural Netw., vol. 5, no. 2, pp. 157–166, Mar. 1994 …","","","","","","","","","","","","",""
"Journal Article","Gaglioti A,Douglas M,Li C,Baltrus P,Blount M,Mack D","","County-Level Proportion of Non-Hispanic Black Population is Associated with Increased County Confirmed COVID-19 Case Rates After Accounting for Poverty, Insurance Status, and Population Density","White Paper-Morehouse School of Medicine","","2020","","","","COVID Tracking Project","","","","researchgate.net","","","","","2020","","","","","https://www.researchgate.net/profile/Peter_Baltrus/publication/341193983_County-Level_Proportion_of_Non-Hispanic_Black_Population_is_Associated_with_Increased_County_Confirmed_COVID-19_Case_Rates_After_Accounting_for_Poverty_Insurance_Status_and_Population_Density/links/5eb33582299bf152d6a1b785/County-Level-Proportion-of-Non-Hispanic-Black-Population-is-Associated-with-Increased-County-Confirmed-COVID-19-Case-Rates-After-Accounting-for-Poverty-Insurance-Status-and-Population-Density.pdf","","","","","","… MMWR Morb Mortal Wkly Rep 2020;69:458–464. DOI: http://dx.doi.org/10.15585/ mmwr. mm6915e3external icon. ii The COVID Tracking Project. https:// covidtracking . com /about- data. Accessed April 27, 2020. iii FIXGOV. Why are Blacks dying at higher rates from COVID …","","","","","","","","","","","","",""
"Journal Article","Chen S,Igan D,Pierri N,Presbitero A","","Tracking the economic impact of COVID-19 and mitigation policies in Europe and the United States","IMF Work. Pap.","IMF Working Papers","2020","20","125","","COVID Tracking Project","","","","International Monetary Fund (IMF)","","","","","2020-07-10","","","1018-5941","2227-8885","https://elibrary.imf.org/view/IMF001/29155-9781513549644/29155-9781513549644/29155-9781513549644.xml;http://dx.doi.org/10.5089/9781513549644.001;https://www.imf.org/~/media/Files/Publications/WP/2020/English/wpiea2020125-print-pdf.ashx","10.5089/9781513549644.001","","","","","We use high-frequency indicators to analyze the economic impact of COVID-19 in Europe and the United States during the early phase of the pandemic. We document that European countries and U.S. states that experienced larger outbreaks also suffered larger economic losses. We also find that the heterogeneous impact of COVID-19 is mostly captured by observed changes in people’s mobility, while, so far, there is no robust evidence supporting additional impact from the adoption of non-pharmaceutical interventions. The deterioration of economic conditions preceded the introduction of these policies and a gradual recovery also started before formal reopening, highlighting the importance of voluntary social distancing, communication, and trust-building measures.","","","","","en","","","","","","","",""
"Website","Matsuzawa K,Sabia JJ","","[No title]","","","2020","","","","COVID Tracking Project","","","","nber.org","","","","","2020","2021-04-02","","","","https://www.nber.org/system/files/working_papers/w27091/revisions/w27091.rev0.pdf","","","","","","… β7*TRAVELst + β8*EMERGst + β9*TEMPst + β10*PRECIPst + αs + γt + αs*t + εst) (2) 21 These data are available at: https:// covidtracking . com Page 18. 15 where COVIDDEATHst is the count of COVID-19 related deaths in state s on day t. We include …","","","","","","","","","","","","",""
"Journal Article","über die erneuerbaren Energien DW,Pusztai A,täglich Gift U,gehört das Wasser WW,des Wassers G","","Archiv der Kategorie: Mikro-Biom--„Immun-System “--„Darm-Flora “","keinblattvormmund13.wordpress","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://keinblattvormmund13.wordpress.com/category/mikro-biom-immun-system-darm-flora/","","","","","","Beiträge über Mikro-Biom – „Immun-System“ – „Darm-Flora“ von isodora13.","","","","","","","","","","","","",""
"Journal Article","Manual MSD","","HEALTH TOPICS","Infection","Infection","2021","","","","COVID Tracking Project","","","","msdmanuals.com","","","","","2021","","","0300-8126","","https://www.msdmanuals.com/home/resourcespages/select-covid-19-news?langselector=1","","","","","","MSD Manual. Please confirm that you are not located inside the Russian Federation. Yes No. Leave this Site? The link you have selected will take you to a third-party website. We do not control or have responsibility for the content of any third-party site. Continue Cancel","","","","","","","","","","","","",""
"Journal Article","Utych SM,Fowler L","","Age-based messaging strategies for communication about COVID-19","JBPA","Journal of Behavioral Public Administration","2020","3","1","","COVID Tracking Project","","","","journal-bpa.org","","","","","2020-04-06","2021-04-02","","2576-6465","2576-6465","http://www.journal-bpa.org/index.php/jbpa/article/view/151;http://dx.doi.org/10.30636/jbpa.31.151;https://www.journal-bpa.org/index.php/jbpa/article/download/151/70","10.30636/jbpa.31.151","","","","","Responding to the COVID-19 crisis across the world has required a massive and sudden shift in human behaviors, with an end goal of slowing the spread of the disease. Importantly, this type of behavioral change requires messaging from governmental agencies and officials. However, we are uncertain about what types of messages are most influential at inducing behavioral change. In this study, we find that messages highlighting the risk to older adults have little additive power in influencing attitudes and behaviors beyond the effect of a broad informational message. However, messages highlighting risks to younger adults, in addition to risks to older adults, make individuals perceive COVID-19 as a more serious threat, though this effect seems to be limited to areas where infection rates are high.","COVID-19; Messaging; Experimental methods; Age","","","","en","","","","","","","",""
"Journal Article","Williams E,Sanders C","","3 Principles for an Antiracist, Equitable State Response to COVID-19—and a Stronger Recovery","Center on Budget and Policy Priorities (May 21, 2020)","","2020","","","","COVID Tracking Project","","","","cbpp.org","","","","","2020","","","","","https://www.cbpp.org/sites/default/files/atoms/files/5-20-20sfp.pdf","","","","","","… Justice-System.pdf. 21 Alexis Madrigal, ”Tracking Race and Ethnicity in the COVID-19 Pandemic,” COVID Tracking Project, April 15, 2020, https:// covidtracking . com / blog/tracking-race-and-ethnicity. 22 Pavetti and Bailey. 23 CBPP …","","","","","","","","","","","","",""
"Preprint Manuscript","Lazer D,Ognyanova K,Quintana A,Baum M,Volpe JD,Druckman J,Perlis RH,Santillana M,Chwe H,Simonson M","","The COVID States Project #5: Approval of executive performance during COVID-19","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-08","","","","","http://dx.doi.org/10.31219/osf.io/pd39k;https://osf.io/preprints/pd39k/;https://osf.io/pd39k/download?format=pdf","10.31219/osf.io/pd39k","","","","","The initial response to a crisis typically depends on the executive branch of government, because they may act more rapidly than legislative and judicial branches. For COVID-19 in particular, the focal decision-makers have been the president and the governors of the 50 states. In the eyes of the public, how have the president and governors responded? We surveyed 22,501 individuals across all 50 states plus the District of Columbia. The survey was conducted on 12-28 June 2020 by PureSpectrum via an online, nonprobability sample, with state-level representative quotas for race/ethnicity, age, and gender (for methodological details on the other waves, see covidstates.org). In addition to balancing on these dimensions, we reweighted our data using demographic characteristics to match the U.S. population with respect to race/ethnicity, age, gender, and education. This was the fifth in a series of surveys we have been conducting since April 2020, examining attitudes and behaviors regarding COVID-19 in the United States.","COVID-19; executive approval; health communication; political communication; political science; politics; public health","","","","","","","","","","","",""
"Journal Article","Manuel DG,van Walraven C,Forster AJ","","The value of Hospital data for covid-19 pandemic surveillance and planning","Authorea Preprints","","2020","","","","COVID Tracking Project","","","","authorea.com","","","","","2020","","","","","https://www.authorea.com/doi/full/10.22541/au.159569198.82432694;https://www.authorea.com/doi/pdf/10.22541/au.159569198.82432694","","","","","","… (2020). [Journal Article]. Eurosurveillance, 25(8). The COVID Tracing Project. (2020). [Online Database]. https:// covidtracking . com /contact Covid-19 Hospitalizations in Belgium. (2020). [Online Database]. https://rpubs.com/JMBodart/Covid19- hosp-be …","","","","","","","","","","","","",""
"Preprint Manuscript","Perra N","","Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review","","","2020","","","","COVID Tracking Project","","","","","","","","","2020-12-30","","","","","http://arxiv.org/abs/2012.15230","","","2012.15230","","","Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I aim to review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 347 articles written by more than 2518 of authors in the last $12$ months. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities","","","","","","","","arXiv","2012.15230","physics.soc-ph","","","arXiv [physics.soc-ph]"
"Preprint Manuscript","Milad E,Bogg T","","Spring 2020 COVID-19 Surge: Prospective Relations between Psychosocial Factors and Guideline Adherence, Mask Wearing, and Symptoms in a U.S. Sample","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02","","","","","psyarxiv.com/tdheg;http://dx.doi.org/10.31234/osf.io/tdheg;https://psyarxiv.com/tdheg/;https://psyarxiv.com/tdheg/download?format=pdf","10.31234/osf.io/tdheg","","","","","Background: To date, much of the research on psychosocial correlates of coronavirus guideline adherence is cross-sectional, leaving prospective associations between these factors unaddressed. Additionally, investigations of prospective predictors of mask-wearing, COVID-19 symptoms, and viral testing remain wanting. Purpose: The present study examined prospective relations between psychosocial factors and guideline adherence, mask-wearing, symptoms, and viral testing in a U.S. sample (N = 500) during the initial surge of COVID deaths in the U.S. between late March and early May, 2020. Methods: Guided by a disposition-belief-motivation framework, correlational analyses and path models tested associations among baseline personality traits, guideline adherence social cognitions, health beliefs, guideline adherence and follow-up guideline adherence, mask-wearing, symptom counts, and 30-day viral testing. Results: Modeling results showed greater baseline agreeableness, conscientiousness, and extraversion were associated with more frequent baseline guideline adherence. More liberal political beliefs, greater guideline adherence intentions, and more frequent guideline adherence at baseline predicted more frequent mask-wearing at follow-up. Sex (female), lower perceived health, and greater neuroticism at baseline predicted greater symptom counts at follow-up. Reports of viral testing were quite low (1.80 %) yet were consistent with concurrent national reporting and limited availability of testing. Conclusions: Results show how inconsistencies and politization of health policy communication were concomitant with effects of individual-level political beliefs on mask-wearing during the initial surge. The results further clarify how personality traits related to social responsibility (i.e., agreeableness, conscientiousness) are associated with following new norms for prescribed behaviors and how symptom reporting can be as much a marker of perceived health as emotional stability.","adherence; behavior; coronavirus; COVID guidelines; COVID symptoms; guidelines; health; health behavior; mask-wearing; personality; U.S.; viral testing","","","","","","","","","","","",""
"Journal Article","Miller SC","","COVID-19 Case Growth vs. Outcomes: Comparing Regions in the United States","Journal of Private Enterprise","","2020","","","","COVID Tracking Project","","","","journal.apee.org","","","","","2020","","","","","http://journal.apee.org/index.php/ajax/GDMgetFile/Parte3_2020_Journal_of_Private_Enterprise_Vol_35_No_4_Winter.pdf","","","","","","… These definitions are explained on the COVID Tracking Project's website, which is the source used for the comparative analysis in section 4. All case, hospitalization, and death data through September 30 were downloaded from the COVID Tracking Project website …","","","","","","","","","","","","",""
"Journal Article","Choi YW,Tuel A,Eltahir EAB","","On the Environmental Determinants of COVID-19 Seasonality","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.03.01.21252243v1.full","","","","","","… JHU CCSE; https://data.humdata.org/). Daily COVID-19 data at the scale of different states within the United States are provided at the COVID Tracking Project (available at https://covidtracking.com/). A threshold of at least 10,000 …","","","","","","","","","","","","",""
"Journal Article","de Margerie E","","COVID-19 spread and Weather in US states: a cross-correlative study on summer-autumn 2020","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2021.01.29.21250793v1.abstract;https://www.medrxiv.org/content/medrxiv/early/2021/02/01/2021.01.29.21250793.full.pdf","","","","","","… quality specialists). Methods Covid-19 data. I used daily new covid-19 cases counts (new cases, NC) collected for the 50 states (+ DC) by the Covid Tracking Project [3] (ie positiveIncrease variable in original dataset). Raw new …","","","","","","","","","","","","",""
"Journal Article","Adolph C,Amano K,Bang-Jensen B,Fullman N,et al.","","Governor partisanship explains the adoption of statewide mask mandates in response to COVID-19","medRxiv","","2021","","","","COVID Tracking Project","","","","medrxiv.org","","","","","2021","","","","","https://www.medrxiv.org/content/10.1101/2020.08.31.20185371v2.full-text","","","","","","medRxiv - The Preprint Server for Health Sciences.","","","","","","","","","","","","",""
"Preprint Manuscript","Hershbein B,Holzer HJ","","The COVID-19 Pandemic's Evolving Impacts on the Labor Market: Who's Been Hurt and What We Should Do","","","2021","","","","COVID Tracking Project","","","","","","","","","2021-02-15","2021-04-02","","","","https://papers.ssrn.com/abstract=3788395;https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3788395;http://dx.doi.org/10.2139/ssrn.3788395;https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3788395;https://research.upjohn.org/cgi/viewcontent.cgi?article=1360&context=up_workingpapers","10.2139/ssrn.3788395","","","","","In this paper, we shed light on the impacts of the COVID-19 pandemic on the labor market, and how they have evolved over most of the year 2020. Relying primarily on microdata from the CPS and state-level data on virus caseloads, mortality, and policy restrictions, we consider a range of employment outcomes—including permanent layoffs, which generate large and lasting costs—and how these outcomes vary across demographic groups, occupations, and industries over time. We also examine how these employment patterns vary across different states, according to the timing and severity of virus caseloads, deaths, and closure measures. We find that the labor market recovery of the summer and early fall stagnated in late fall and early winter. As noted by others, we find low-wage and minority workers are hardest hit initially, but that recoveries have varied, and not always consistently, between Blacks and Hispanics. Statewide business closures and other restrictions on economic activity reduce employment rates concurrently but do not seem to have lingering effects once relaxed. In contrast, virus deaths—but not caseloads—not only depress current employment but produce accumulating harm. We conclude with policy options for states to repair their labor markets.","COVID-19, employment rates, inequality, pandemic recession, recovery","","","","","","","","","","","",""
"Journal Article","Prentice CR,Carroll R","","On stay at home orders: Using the power of data science for spatial and temporal modeling and visualization of COVID-19","","","2021","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2021","","","","","https://www.researchsquare.com/article/rs-275352/latest.pdf","","","","","","… animation using per capita state-level data (January 22, 2020 – July 8, 2020),” HCPDS working paper, 10. The Atlantic (2020), “The COVID Tracking Project .,” CC BY-NC-4.0 license, Available athttps://covidtracking.com/. Trombetta …","","","","","","","","","","","","",""
"Journal Article","Srivastava A,Chowell G","","Modeling Study: Characterizing the Spatial Heterogeneity of the COVID-19 Pandemic through Shape Analysis of Epidemic Curves","","","2021","","","","COVID Tracking Project","","","","researchsquare.com","","","","","2021","","","","","https://www.researchsquare.com/article/rs-223226/latest.pdf","","","","","","… For the USA analysis, we retrieved daily cumulative case count data from the COVID Tracking Project , a volunteer organization dedicated to collecting and publishing data on the spread of COVID-19 in the United States [22] …","","","","","","","","","","","","",""
"Journal Article","Fitzgerald K,Hellwig C,Martin JS","","COVID-19 Vulnerable Population Assessment","hamiltonnj.com","","","","","","COVID Tracking Project","","","","","","","","","","","","","","https://www.hamiltonnj.com/filestorage/228428/228430/228796/252019/526606/Health_Div_-_Hamilton_Vulnerable_Populations_Assessment_3-21_FINAL.pdf","","","","","","… o State-level statistics for NJ show Hispanic/Latinx persons were most likely to contract COVID-19, and Black/African American persons are most likely to die from COVID-19 (The COVID Tracking Project , 2021). Community Impacts from COVID-19 …","","","","","","","","","","","","",""