Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 17, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/ijerph17176161
Keywords
COVID-19; big data analysis; major public health incidents; epidemic prevention and control; visual analysis; deep learning; predictive analysis
Funding
- National Natural Science Foundation of China [71872061, 71702045]
- Humanities and Social Sciences Foundation of the Ministry of Education in China [16YJC630028, 17YJC630047]
- Consumer and Organizational Digital Analytics (CODA) Research Centre at King's College London
- Guangdong Provincial General University Humanities and Social Sciences Key Research Base-Shantou University Guangdong-Taiwan Enterprise Cooperation Research Institute Open Fund Project
- Fundamental Research Funds for the Central Universities [2018B20614, 2017B14414]
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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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