论文标题
非马克维亚流行模型的动态生存分析
Dynamic Survival Analysis for non-Markovian Epidemic Models
论文作者
论文摘要
我们提出了一种在最小假设下分析随机流行模型的新方法。该方法称为DSA，基于一个简单而强大的观察结果，即由PDE系统描述的种群级平均场轨迹也可能近似于个人感染和恢复的个人级时。这个想法产生了某个非马克维亚代理的模型，并为随机的感染和/或恢复时间样本提供了代理级的似然函数。对来自英国FMD的合成和真实流行数据的广泛数值分析以及印度的COVID-19都表现出良好的准确性，并确认方法在基于可能性的参数估计中的多功能性。随附的软件包为潜在用户提供了一种实用的工具，用于借助DSA方法建模，分析和解释流行数据。
We present a new method for analyzing stochastic epidemic models under minimal assumptions. The method, dubbed DSA, is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of PDE may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from the FMD in the United Kingdom and the COVID-19 in India show good accuracy and confirm method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modeling, analyzing and interpreting epidemic data with the help of the DSA approach.
