TY - JOUR
T1 - Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics
T2 - Applications to COVID-19
AU - Yang, Xian
AU - Wang, Shuo
AU - Xing, Yuting
AU - Li, Ling
AU - Xu, Richard Yi Da
AU - Friston, Karl J.
AU - Guo, Yike
N1 - Publisher Copyright:
© 2022 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/2/23
Y1 - 2022/2/23
N2 - 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.
AB - 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.
KW - Algorithms
KW - Basic Reproduction Number
KW - Bayes Theorem
KW - COVID-19/epidemiology
KW - Humans
KW - SARS-CoV-2/isolation & purification
UR - http://www.scopus.com/inward/record.url?scp=85125419635&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1009807
DO - 10.1371/journal.pcbi.1009807
M3 - Journal article
C2 - 35196320
AN - SCOPUS:85125419635
SN - 1553-734X
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 2
M1 - e1009807
ER -