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Modeling analytics in COVID-19: prediction, prevention, control, and evaluation

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  • Yang Lu
  • Therese L. Williams

Abstract

The outbreak of COVID-19 has attracted attention from all around the world. Governments and institutions have adopted ways to fight COVID-19, but its prevalence is still strong. The SIR model has important reference value for the novel coronavirus epidemic, offering both preventive measures and the ability to predict future trends. Based on an analysis of the classical epidemiological SIR model along with key parameters, this paper aims to analyze the patterns of COVID-19, to discuss potential anti-COVID-19 measures, and to explain why we need to conduct appropriate measures against COVID-19. The use of the SIR model can play an important role in public health emergencies. Among the parameters of the SIR model, the contact ratio and the reproduction ratio are the factors that have the potential to mitigate the consequences of COVID-19. Anti-COVID-19 measures include wearing a mask, washing one’s hands, keeping social distance, and staying at home if possible.

Suggested Citation

  • Yang Lu & Therese L. Williams, 2021. "Modeling analytics in COVID-19: prediction, prevention, control, and evaluation," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(3), pages 424-442, July.
    • Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:424-442
    DOI: 10.1080/23270012.2021.1946664
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    Cited by:

    1. Ronghua Xu & Yiran Liu & Meng Liu & Chengang Ye, 2023. "Sustainability of Shipping Logistics: A Warning Model," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    2. Qingchuan Zhang & Zihan Li & Wei Dong & Siwei Wei & Yingjie Liu & Min Zuo, 2023. "A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments," IJERPH, MDPI, vol. 20(5), pages 1-17, February.

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