Bayesian data assimilation for estimating epidemic evolution: a COVID-19 study

TL;DR

This paper introduces DARt, a Bayesian data assimilation framework for real-time estimation of COVID-19 transmission parameters, addressing delays, abrupt changes, and uncertainty quantification to improve epidemic monitoring.

Contribution

The paper presents a novel Bayesian data assimilation method, DARt, that enhances real-time estimation of epidemic parameters by handling delays, abrupt changes, and uncertainty quantification.

Findings

DARt effectively accounts for observation delays.

It captures abrupt changes in transmission dynamics.

It provides accurate, timely estimates of COVID-19 Rt.

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.