2020/12/22 by Xian Yang, Yang, Xian, Shuo Wang +11
Mathematics · Medicine · #Applications (stat.AP) #Biological Physics (physics.bio-ph) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Influenza Virus Research Studies #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.2101.01532
openalex publication_date 2020/12/22 · openalex created_date 2021/11/08 · openalex updated_date 2026/07/28
The evolution 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 Rt estimation, 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 revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data.