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.
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 -