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Predicting new cases of COVID‐19 and the application to population sustainability analysis

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  • Chengcheng Bei
  • Shiping Liu
  • Yin Liao
  • Gaoliang Tian
  • Zichen Tian

Abstract

We propose a new spatio‐temporal point process model to predict infectious cases of COVID‐19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID‐19. We show that large‐scale testing and strict containment are key factors for the successful suppression of the COVID‐19 contagion. This study provides an effective tool to develop early warning systems for major infectious diseases, offering insights on how to develop prevention and control strategies to reduce the impact of disease and maintain population sustainability.

Suggested Citation

  • Chengcheng Bei & Shiping Liu & Yin Liao & Gaoliang Tian & Zichen Tian, 2021. "Predicting new cases of COVID‐19 and the application to population sustainability analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4859-4884, September.
    • Handle: RePEc:bla:acctfi:v:61:y:2021:i:3:p:4859-4884
    DOI: 10.1111/acfi.12785
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    References listed on IDEAS

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    Cited by:

    1. Hanns de la Fuente-Mella & Rolando Rubilar & Karime Chahuán-Jiménez & Víctor Leiva, 2021. "Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries," Mathematics, MDPI, vol. 9(13), pages 1-13, July.

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