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.

中文翻译：

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时间融合系数 SIR 模型在美国 COVID-19 疫情中的应用

在本文中，我们提出了一种具有时间融合系数的易感感染去除（SIR）模型。特别是，我们提出的模型通过贝叶斯收缩先验发现了 SIR 模型的传输率和去除率的潜在时间均匀性模式。所提出的方法的 MCMC 采样是通过灵活的包来促进的右。进行了广泛的模拟研究来检验所提出方法的经验性能。我们进一步应用所提出的方法来分析美国不同级别的 COVID-19 数据。