Time Fused Coefficient SIR Model with Application to COVID-19 Epidemic in the United States

TL;DR

This paper introduces a novel SIR model with time-varying coefficients using Bayesian methods, enabling better understanding of COVID-19 transmission dynamics in the US.

Contribution

The paper develops a time fused coefficient SIR model with Bayesian shrinkage priors and demonstrates its effectiveness through simulations and COVID-19 data analysis.

Findings

Model accurately captures time-varying transmission rates.

Effective in analyzing COVID-19 epidemic patterns.

Provides a flexible framework for epidemic modeling.

Abstract

In this paper, we propose a Susceptible-Infected-Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States.