The ongoing COVID-19 pandemic has led to a serious health crisis, and information obtained from disease transmission models fitted to observed data is needed to inform containment strategies. As the transmission of virus varies from city to city in different countries, we use a two-level individual-level model to analyze the spatiotemporal SARS-CoV-2 spread. However, inference procedures such as Bayesian Markov chain Monte Carlo, which is commonly used to estimate parameters of ILMs, are computationally expensive. In this study, we use trained ensemble learning classifiers to estimate the parameters of two-level ILMs and show that the fitted ILMs can successfully capture the virus transmission among Wuhan and 16 other cities in Hubei province, China.

正在进行中的COVID-19大流行导致严重的健康危机，因此需要从适应观察数据的疾病传播模型获得的信息来指导遏制策略。由于病毒的传播在不同国家的不同城市之间存在差异，因此我们使用两级个人模型分析SARS-CoV-2时空传播。然而，诸如贝叶斯马尔可夫链蒙特卡洛之类的推理程序通常用于估计ILM的参数，在计算上是昂贵的。在这项研究中，我们使用训练有素的集成学习分类器来估计两级ILM的参数，并表明拟合的ILM可以成功捕获武汉市和湖北省其他16个城市之间的病毒传播。