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

Hou-Cheng Yang; Yishu Xue; Yuqing Pan; Qingyang Liu; Guanyu Hu

doi:10.1080/02664763.2021.1936467

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

J Appl Stat. 2021 Jun 4;50(11-12):2373-2387. doi: 10.1080/02664763.2021.1936467. eCollection 2023.

Authors

Hou-Cheng Yang¹, Yishu Xue², Yuqing Pan³, Qingyang Liu², Guanyu Hu⁴

Affiliations

¹ Department of Statistics, Florida State University, Tallahassee, FL, USA.
² Department of Statistics, University of Connecticut, Storrs, CT, USA.
³ Microsoft Corporation, Redmond, WA, USA.
⁴ Department of Statistics, University of Missouri Columbia, Columbia, MO, USA.

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.

Keywords: 62P10; MCMC; Time fusion; homogeneity pursuit; infectious diseases; shrinkage prior.