Download This PaperDynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread
19 Pages Posted: 3 Apr 2020 Last revised: 15 Jun 2020
Date Written: April 1, 2020
Abstract
A key challenge for estimating the epidemiological parameters of the 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 Wuhan to infer the true number in the city. This approach can only provide a static estimate of the epidemiological parameters before Wuhan lockdown on January 23, 2020, because there are almost no exported cases thereafter. Here, we propose a method to dynamically estimate the epidemiological parameters of the COVID-19 outbreak in Wuhan by recovering true numbers of infections from day-to-day official numbers. Using the method, we provide a comprehensive retrospection on how the disease 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. 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. In particular, transportation suspension and large scale hospitalization respectively prevented 33,719 and 90,072 people from getting infected in the nine-day time period right after its implementation.
Keywords: COVID-19, Epidemiological parameter, Government intervention, Bayesian estimation
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