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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qiong Jia & Yue Guo & Guanlin Wang & Stuart J. Barnes, 2020. "Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework," IJERPH, MDPI, vol. 17(17), pages 1-21, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6161-:d:403634
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    5. Elif Bozkaya & Levent Eriskin & Mumtaz Karatas, 2023. "Data analytics during pandemics: a transportation and location planning perspective," Annals of Operations Research, Springer, vol. 328(1), pages 193-244, September.
    6. Biresh Kumar & Sharmistha Roy & Anurag Sinha & Celestine Iwendi & Ľubomíra Strážovská, 2022. "E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    7. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).

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