Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework

Qiong Jia, Yue Guo, Guanlin Wang and Stuart J. Barnes
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Qiong Jia: Department of Management, Hohai Business School, Hohai University, Nanjing 211100, China
Yue Guo: The Department of Information System and Management Engineering, Faculty of Business, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, China
Guanlin Wang: Department of Management, Hohai Business School, Hohai University, Nanjing 211100, China
Stuart J. Barnes: CODA Research Centre, King’s Business School, King’s College London, Bush House, 30 Aldwych, London WC2B 4BG, UK

IJERPH, 2020, vol. 17, issue 17, 1-21

Abstract: Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

Keywords: COVID-19; big data analysis; major public health incidents; epidemic prevention and control; visual analysis; deep learning; predictive analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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