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.
翻译:在本文中,我们提出了一个带有时间引信系数的可视感染-逆转模型,特别是,我们提议的模型通过Bayesian收缩前科发现了SIR模型传输率和清除率的基本时间同质模式。 R. 微软包的模拟研究有助于为拟议方法进行MCMC取样。我们进行了广泛的模拟研究,以审查拟议方法的经验性能。我们进一步运用拟议的方法分析美国COVID-19数据的不同水平。