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

2020/08/10 by Hou‐Cheng Yang, Yishu Xue, Yang, Hou-Cheng +7
Mathematics · #Applications (stat.AP) #COVID-19 epidemiological studies #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2008.04284

openalex publication_date 2020/08/10 · openalex created_date 2020/08/13 · openalex updated_date 2026/07/28

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.

Citations

• The horseshoe estimator for sparse signals
• The Mathematics of Infectious Diseases
• Sparsity information and regularization in the horseshoe and other shrinkage priors
• Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study
• Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

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