Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

ABSTRACT: Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; t

SUBMITTER: Yang X 

PROVIDER: S-EPMC8923496 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature