Yang, Xian;
Wang, Shuo;
Xing, Yuting;
Li, Ling;
Xu, Richard Yi Da;
Friston, Karl J;
Guo, Yike;
(2022)
Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.
PLoS Computational Biology
, 18
(2)
, Article e1009807. 10.1371/journal.pcbi.1009807.
(In press).
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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; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
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