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

Xian Yang, Shuo Wang, Yuting Xing, Ling Li, Richard Yi Da Xu, Karl J Friston, Yike Guo

Research output: Contribution to journalArticlepeer-review

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-ofthe- 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.

Original languageEnglish
Article numbere1009807
JournalPLoS computational biology
Volume18
Issue number2
Early online date23 Feb 2022
DOIs
Publication statusPublished - 23 Feb 2022

Fingerprint

Dive into the research topics of 'Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19'. Together they form a unique fingerprint.

Cite this