Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread

Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang

Here, we solve the under-reporting problem from a distinctive perspective. Rather than relying on cases exported from Wuhan, we propose a method to dynamically estimate the epidemiological parameters of the COVID-19 outbreak in Wuhan over time by transforming day-do-day official numbers of infections. Specifically, we propose a Bayesian estimation method that seamlessly integrates a epidemic model characterizing the spread mechanism of the disease and a salient transformation approach, coupled with prior knowledge on key parameters of the epidemic model. Our proposed method has the following distinguishing features compared to existing methods. First, we tackle the under-reporting problem by proposing a straightforward yet effective transformation approach to adjust for potential discrepancies between official and true numbers to give better overall picture for the scope of the COVID-19 outbreak, thereby more reliably quantifying its key epidemiological parameters. Second, our approach conveniently incorporates the fast evolving knowledge from new COVID-19 literature to generate well-justified and more refined parameter estimation results with uncertainty quantification. Furthermore, the temporal dynamic estimation over time keeps track of the evolving disease spread in response to interventions and holds the promise of objectively monitoring and evaluating effectiveness of various containment measures. Our analysis uncovers and demonstrates the evolution of the COVID-19 outbreak in Wuhan from January 19, 2020 to March 5, 2020. In particular, for every day in this period, we apply the proposed method to estimate the effective reproduction number as well as true numbers of infections, such as the cumulative number of infected cases and the number of actively infected but not quarantined cases. Our proposed method also produces daily under-reporting factors, which indicate the degree of discrepancies between official and true numbers. Finally, using the dynamic epidemiological parameters estimated by our analysis, we evaluate the effects of two major interventions on the spread of COVID-19 in Wuhan.