Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread

  • Hongzhe Zhang Institute for Financial Services Analytics, University of Delaware, Newark, DE, United States. https://orcid.org/0000-0002-0541-1285
  • Xiaohang Zhao Institute for Financial Services Analytics, University of Delaware, Newark, DE, United States.
  • Kexin Yin Institute for Financial Services Analytics, University of Delaware, Newark, DE, United States.
  • Yiren Yan Institute for Financial Services Analytics, University of Delaware, Newark, DE, United States.
  • Wei Qian Institute for Financial Services Analytics, and Department of Applied Economics and Statistics, University of Delaware, Newark, DE, United States. https://orcid.org/0000-0003-1022-1141
  • Bintong Chen Institute for Financial Services Analytics Lerner College of Business and Economics, University of Delaware, Newark, DE, United States.
  • Xiao Fang | xfang@udel.edu Institute for Financial Services Analytics Lerner College of Business and Economics, University of Delaware, Newark, DE, United States. https://orcid.org/0000-0002-9429-5748


Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.
Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.
Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.
Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread.



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Original Articles
COVID-19, Epidemiological parameter, Government intervention, Bayesian estimation method
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How to Cite
Zhang, H., Zhao, X., Yin, K., Yan, Y., Qian, W., Chen, B., & Fang, X. (2021). Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread. Journal of Public Health Research, 10(1). https://doi.org/10.4081/jphr.2021.1906