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

Research output: Contribution to journalReview article

Qiong Jia, Yue Guo, Guanlin Wang, Stuart J. Barnes

Original languageEnglish
Article number6161
Pages (from-to)1-21
Number of pages21
JournalInternational Journal of Environmental Research and Public Health
Volume17
Issue number17
DOIs
Published25 Aug 2020

King's Authors

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.

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