论文标题
俄亥俄州共同19的流行病应用于COVID-19
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio
论文作者
论文摘要
动力学生存分析（DSA）是基于应用于个人（代理）感染和恢复历史的平均场动力学建模流行病的框架。最近，DSA已被证明是分析复杂的非马克维亚流行过程的有效工具，这些过程否则很难使用标准方法来处理。 DSA的优点之一是它以简单但不明确的形式代表典型的流行数据，涉及某些微分方程的解决方案。在这项工作中，我们描述了如何借助适当的数值和统计方案将复杂的非马克维亚DSA模型应用于特定数据集。这些想法以俄亥俄州Covid-19流行病的数据示例进行了说明。
The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, DSA has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of DSA is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations. In this work we describe how a complex non-Markovian DSA model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.
