A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

TL;DR

This paper introduces a Bayesian updating framework using data assimilation to dynamically estimate COVID-19 infection parameters, enabling detailed assessment of intervention impacts and resurgence risks across different regions.

Contribution

It presents a novel Bayesian data assimilation approach with a new parameterization of intervention effects for real-time epidemic monitoring.

Findings

Quantified the effects of interventions in European countries, the US, and Wuhan.

Identified resurgence risks in the USA based on estimated parameters.

Provided a framework for ongoing assessment of intervention strategies.

Abstract

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA.