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Bayesian data assimilation for estimating epidemic evolution: a COVID-19 study (preprint)
arxiv; 2020.
Preprint
in English
| PREPRINT-ARXIV | ID: ppzbmed-2101.01532v2
ABSTRACT
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.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-ARXIV
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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