论文标题
SIRS模型的异质性学习：COVID-19的应用
Heterogeneity Learning for SIRS model: an Application to the COVID-19
论文作者
论文摘要
我们提出了一种贝叶斯异质性学习方法，用于易感感染的易启动感染（SIRS）模型，该模型允许在不同地区的最新冠状病毒（COVID-19）的传播率，恢复速率和免疫率损失的基础聚类模式。我们提出的方法同时推断了参数估计和聚类信息，其中包含簇数和群集配置。具体而言，我们的关键思想是将SIRS模型制定为层次形式，并为有限混合物先验的混合物分配用于异质性学习的混合物。检查了所提出的模型的性质，并使用马尔可夫链蒙特卡洛采样算法从后验分布中采样。进行了广泛的仿真研究以检查所提出方法的经验性能。我们进一步应用了所提出的方法来分析美国的州级数据。
We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest coronavirus (COVID-19) among different regions. Our proposed method provides simultaneously inference on parameter estimation and clustering information which contains both number of clusters and cluster configurations. Specifically, our key idea is to formulates the SIRS model into a hierarchical form and assign the Mixture of Finite mixtures priors for heterogeneity learning. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze the state level COVID-19 data in U.S.
