论文标题
时间融合系数的SIR模型，并应用于美国的COVID-19
Time Fused Coefficient SIR Model with Application to COVID-19 Epidemic in the United States
论文作者
论文摘要
在本文中，我们提出了一个具有时间融合系数的易感感染解析（SIR）模型。特别是，我们提出的模型发现了通过贝叶斯收缩先验的SIR模型传输速率和去除率的基本时间均匀性模式。在R中的Nimble套件促进了所提出方法的MCMC抽样。进行了广泛的仿真研究，以检查所提出方法的经验性能。我们进一步应用了提出的方法来分析美国不同水平的COVID-19数据。
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.
