Time fused coefficient SIR model with application to COVID-19 epidemic in the United States

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.

Automated summary

This paper proposes a SIR model with time fused coefficients that discovers the underlying time homogeneity pattern for transmission rate and removal rate using Bayesian shrinkage priors. The proposed method is validated through extensive simulation studies and applied to analyze COVID-19 data in the United States at different levels.