2020/08/10 by Hou-Cheng Yang, Hou‐Cheng Yang, Yishu Xue +8 · 1 citation
Mathematics · Medicine · #2019-20 coronavirus outbreak #Applications (stat.AP) #Bayesian probability #COVID-19 epidemiological studies #Computer science #Coronavirus disease 2019 (COVID-19) #Econometrics #Epidemic model #FOS: Computer and information sciences #Homogeneity (statistics) #Markov chain Monte Carlo #Mathematics #Medicine #Outbreak #Prior probability #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #Shrinkage #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #Telecommunications #Transmission (telecommunications) #Transmission rate #Virology #stat.AP
paper · pdf · doi:10.48550/arxiv.2008.04284
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/08/10 · openalex created_date 2020/08/13 · arxiv created 2021/03/10 · arxiv updated 2021/03/11 · openalex updated_date 2026/08/08
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.