具有时间融合系数的SIR模型及其在美国新冠肺炎疫情中的应用
Time fused coefficient SIR model with application to COVID-19 epidemic in the United States.
作者信息
Yang Hou-Cheng, Xue Yishu, Pan Yuqing, Liu Qingyang, Hu Guanyu
机构信息
Department of Statistics, Florida State University, Tallahassee, FL, USA.
Department of Statistics, University of Connecticut, Storrs, CT, USA.
出版信息
J Appl Stat. 2021 Jun 4;50(11-12):2373-2387. doi: 10.1080/02664763.2021.1936467. eCollection 2023.
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 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.
摘要
在本文中，我们提出了一种具有时间融合系数的易感-感染-移除（SIR）模型。具体而言，我们提出的模型通过贝叶斯收缩先验发现SIR模型传播率和移除率潜在的时间齐性模式。R语言中的软件包为所提方法的MCMC抽样提供了便利。我们进行了广泛的模拟研究以检验所提方法的实证性能。我们进一步应用所提方法来分析美国不同层面的新冠肺炎数据。
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