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

• Main Authors: Hongzhe Zhang, Xiaohang Zhao, Kexin Yin, Yiren Yan, Wei Qian, Bintong Chen, Xiao Fang
• Format: Article
• Language: English
• Published: PAGEPress Publications 2021-03-01
• Series: Journal of Public Health Research
• Subjects: COVID-19; Epidemiological parameter; Government intervention; Bayesian estimation method
• Online Access: https://www.jphres.org/index.php/jphres/article/view/1906
• ISSN: 2279-9028; 2279-9036

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.