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

ABSTRACT: 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. 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

SUBMITTER: Zhang H 

PROVIDER: S-EPMC7967497 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature