Published July 30, 2022 | Version CC BY-NC-ND 4.0
Journal article Open

An Analysis of Methods for Forecasting Epidemic Disease Outbreaks using Information from Social Media

Creators

  • 1. Assistant Professor, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India.
  • 2. Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
  • 3. Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
  • 4. Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India

Contributors

  • 1. Assistant Professor, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India.

Description

Abstract: Predicting the rise or fall of an epidemic or pandemic is an essential part of establishing control over it. Post-World War 1, when there was an outbreak of the “Black Plague” there weren’t any means to analyze and predict. Although today we are equipped with tools like Machine Learning and Artificial Intelligence which have certainly enabled us to prevent unnecessary loss of life. It helps prepare the health officials to build the infrastructure and interpret the intensity of preparedness regulation of resources. The aim of this survey is to analyze and shed some light on the various algorithms and methods such as - regression models, neural networks, ARIMA, etc. Before building any model, gathering and processing the data is also essential. Hence our paper also focuses on which social media platforms proved beneficial in comparison to all we found and then made fit to be incorporated into the models. While researching for this paper, we observed that every disease has a different transmission type that leads to an outbreak and is a key factor in constructing a model. The literature evaluation in this work is centered on various prediction algorithms and their strategies for extracting online data from social media sites like Facebook and Twitter, all of which have drawn a lot of interest in early disease diagnosis for public health.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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References

  • Sujin Bae, Eunyoung (Christine) Sung & Ohbyung Kwon (2021), Accounting for social media effects to improve the accuracy of infection models: combatting the COVID-19 pandemic and infodemic, European Journal of Information Systems
  • Elaine O. Nsoesie1*, Olubusola Oladeji1, Aristide S.AbahAbah 2 & Martial L. Ndefo‑Mbah(2021), Forecasting influenza‑like illness trends in Cameroon using Google Search Data, Scientific Reports
  • Mat´ıas N´u˜nez,1, 2, 3 Nadia L. Barreiro,4 Rafael A. Barrio,5 and Christopher Rackauckas (2021), Forecasting virus outbreaks with social media data via neural ordinary differential equations, medRxiv
  • Beakcheol Jang, Ph.D.; Inhwan Kim, BSc; Jong Wook Kim2, Ph.D. (2021), Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study, Jmir Medical Informatics
  • Samira Yousefinaghani, Rozita Dara, Samira Mubareka and Shayan Sharif 3 (2021), Prediction of COVID-19 Waves Using social media and Google Search: A Case Study of the US and Canada, Frontiers
  • Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, and Muhammad Adnan (2021), Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches, Hindawi"
  • Ali Wahid, Steven H. Munkeby, Samuel Sambasivam (2021), MACHINE LEARNING-BASED FLU FORECASTING STUDY USING THE OFFICIAL DATA FROM THE CENTERS FOR DISEASE CONTROL AND PREVENTION AND TWITTER DATA, Issues in Informing Science+Information Technology"
  • Abir EL Azzaoui, Sushil Kumar Singh, Jong Hyuk Park (2021), SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City, Elsevier – ScienceDirect
  • Loukas Samaras*, Miguel-Angel Sicilia and Elena GarcíaBarriocanal(2021), Predicting epidemics using search engine data: a comparative study on measles in the largest countries of Europe, BMC Public health
  • Jiachen Sun and Peter A. Gloor1(2021), Assessing the Predictive Power of Online social media to Analyze COVID-19 Outbreaks in the 50 U.S. States, Preprints
  • Tejas Shinde; Parikshit Thatte; Sachin Sachdev; Vidya Pujari (2021), Monitoring of Epidemic Outbreaks Using Social Media Data, IEEE
  • Erhu Du, Eddie Chen, Ji Liub, Chunmiao Zheng (2021), How do social media and individual behaviors affect epidemic transmission and contro, Elsevier – ScienceDirect
  • Sarah F. McGough1,2,3*, John S. Brownstein2,3,4, Jared B. Hawkins2,3,4, Mauricio Santillana (2017), Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, social media, and News Report Data, Plos: neglected tropical diseases
  • Cuilian Li1, Li Jia Chen2, Xueyu Chen1, Mingzhi Zhang1, Chi Pui Pang1,2, Haoyu Chen (2020), Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020, Eurosurveillance
  • Loukas Samaras, Elena García-Barriocanal, Miguel-Angel Sicilia (2020), Comparing social media and Google to detect and predict severe epidemics, nature - Scientific Reports
  • Cuihua Shen, Anfan Chen, Chen Luo, Jingwen Zhang, Bo Feng, Wang Liao (2020), Using Reports of Symptoms and Diagnoses on social media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study, JMIR Publications
  • Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brugnoli, Ana Lucia Schmidt, Paola Zola, Fabiana Zollo Antonio Scala (2020), The COVID-19 social media infodemic, nature - Scientific Reports
  • Yufang Wang, Kuai Xu, Yun Kang, Haiyan Wang, Feng Wang, Adrian Avram (2020), Regional Influenza Prediction with Sampling Twitter Data and PDE Model, MDPI - International Journal of Environmental Research and Public Health
  • May Oo Lwin, Jiahui Lu, Anita Sheldenkar, Ysa Marie Cayabyab, Andrew Zi Han Yee, Helen Elizabeth Smith (2020), Temporal and textual analysis of social media on collective discourses during the Zika virus pandemic, Springer
  • Zhenghong Peng, Ru Wang, Lingbo Liu, Hao Wu (2020), Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data, MDPI - International Journal of Environmental Research and Public Health"
  • U Venkatesh, Periyasamy Aravind Gandhi (2020), Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis, Synapse"
  • Jiawei Li, Qing Xu, Raphael Cuomo, Vidya Purushothaman, Tim Mackey (2020), Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study, JMIR Publications"
  • J. Sooknanan, D. M. G. Comissiong(2020), Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics, Springer
  • Lei Qin, Qiang Sun, Yidan Wang, Ke-Fei Wu, Mingchih Chen, BenChang Shia, Szu-Yuan Wu (2020), Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index, MDPI - International Journal of Environmental Research and Public Health"
  • Aditya Joshi, Ross Sparks, James McHugh, Sarvnaz Karimi, Cecile Paris, Raina MacIntyre (2020), Harnessing Tweets for Early Detection of an Acute Disease Event, NCBI"
  • Ali Alessa, Miad Faezipour (2018), A review of influenza detection and prediction through social networking sites, BMC
  • Madhur Verma, Kamal Kishore, Mukesh Kumar, Aparajita Ravi Sondh, Gaurav Aggarwal, Soundappan Kathirvel(2018), Google Search Trends Predicting Disease Outbreaks: An Analysis from India, HIR - Healthcare Informatics Research"
  • Hyekyung Woo, Youngtae Cho, Eunyoung Shim, Jong-Koo Lee, Chang-Gun Lee, Seong Hwan Kim (2016), Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea, JMIR Publications"
  • Soheila Molaei, Mohammad Khansari, Hadi Veisi & Mostafa Salehi (2019), Predicting the spread of influenza epidemics by analyzing Twitter messages, Springer
  • Yuzhou Zhang, Laith Yakob, Michael B. Bonsall & Wenbiao Hu (2019), Predicting seasonal influenza epidemics using crosshemisphere influenza surveillance data and local internet query data, Scientific Reports
  • Nduwayezu, Maurice; Satyabrata, Aicha; Han Suk-Young; Kim Jung Eon; Kim Hoon; Park Junseok; Hwang Won-Joo (2019), Malaria Epidemic Prediction Model by Using Twitter Data and Precipitation Volume in Nigeria, Journal of Korea Multimedia Society
  • Ying Chen, Yuzhou Zhang, Zhiwei Xu, Xuanzhuo Wang, Jiahai Lu & Wenbiao Hu (2019), Avian Influenza A (H7N9) and related Internet search query data in China, Scientific Reports
  • Anahita Safarishahrbijari; Nathaniel D Osgood (2019), Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study, JMIR Publications [CrossRef] 34. Balsam Alkouz; Zaher Al Aghbari; Jemal Hussien Abawajy (2019), Tweetfluenza: Predicting flu trends from Twitter data, IEEE Explore
  • Xianglei Zhu, Bofeng Fu, Yaodong Yang, Yu Ma, Jianye Hao, Siqi Chen, Shuang Liu, Tiegang Li, Sen Liu, Weiming Guo & Zhenyu Liao (2019), Attention-based recurrent neural network for influenza epidemic prediction, BMC Bioinformatics
  • Yuvaana Sundarakrishnan, Akshay George Koshy, K.P. Vijayakumar (2019), Epidemic Prediction, International Research Journal of Engineering and Technology (IRJET)
  • Aditya Lia Ramadona, Yesim Tozan, Lutfan Lazuardi, Joacim Rocklöv (2019), A combination of incidence data and mobility proxies from social media predict the intra-urban spread of dengue in Yogyakarta, Indonesia, PLOS
  • Bijaya Adhikari, Xinfeng Xu, Naren Ramakrishnan and B. Aditya Prakash (2019), EpiDeep: Exploiting Embeddings for Epidemic Forecasting, ACM Digital Library
  • Shoko Wakamiya, Mizuki Morita, Yoshinobu Kano, Tomoko Ohkuma, Eiji Aramaki(2019), Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations, JMIR Publications
  • Gaoyang Liu, Chen Wang, Kai Peng, Haojun Huang, Yutong Li, and Wenqing Cheng (2019), SocInf: Membership Inference Attacks on Social Media Health Data with Machine Learning, IEEE Explore
  • Mrs. Disha Sushant Wankhede, Dr. Selvarani Rangasamy,"REVIEW ON DEEP LEARNING APPROACH FOR BRAIN TUMOR GLIOMA ANALYSIS" Journal of Information Technology in Industry, VOL. 9 NO. 1 (2021) pp. 395 - 408, DOI: https://doi.org/10.17762/itii.v9i1.144
  • Disha Sushant Wankhede, R. Selvarani, Dynamic architecture based deep learning approach for glioblastoma brain tumor survival prediction, Neuroscience Informatics, Volume 2, Issue 4, 2022, 100062, ISSN 2772-5286, https://doi.org/10.1016/j.neuri.2022.100062. (https://www.sciencedirect.com/science/article/pii/S277252862200024 3)
  • Wankhede, D.S., Pandit, S., Metangale, N., Patre, R., Kulkarni, S., Minaj, K.A. (2022). Survey on Analyzing Tongue Images to Predict the Organ Affected. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_56
  • "Artificial Intelligence and its Subsets: Machine Learning and its Algorithms, Deep Learning, and their Future Trends ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and ISSN Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi112-i117, May-2022, Available at: http://www.jetir.org/papers/JETIR2205914.pdf
  • Shetty, A. Thorat, R. Singru, M. Shigawan, and V. Gaikwad, "Predict Socio-Economic Status of an Area from Satellite Image Using Deep Learning," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 177-182, doi: 10.1109/ICESC48915.2020.9155696
  • Gudapati S.P., Gaikwad V. (2021) Light-Weight Key Establishment Mechanism for Secure Communication Between IoT Devices and Cloud. In: Satapathy S., Bhateja V., Janakiramaiah B., Chen YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_55
  • Trupti Vasantrao Bhandare, & Selvarani Rangasamy. (2021). Review on Heart Disease Diagnosis Using Deep Learning Methods. International Journal of Next-Generation Computing, 12(2), 91–102. https://doi.org/10.47164/ijngc.v12i2.206
  • Vasantrao, BhandareTrupti & Rangasamy, Selvarani. (2021). Weighted Clustering for Deep Learning Approach in Heart Disease Diagnosis. International Journal of Advanced Computer Science and Applications. 12. 10.14569/IJACSA.2021.0120944.

Subjects

ISSN: 2277-3878 (Online)
https://portal.issn.org/resource/ISSN/2277-3878
Retrieval Number: 100.1/ijrte.B71600711222
https://www.ijrte.org/portfolio-item/b71600711222/
Journal Website: www.ijrte.org
https://www.ijrte.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
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