Survival dynamical systems: individual-level survival analysis from population-level epidemic models
Survival dynamical systems: individual-level survival analysis from population-level epidemic models
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DOI：
10.1098/rsfs.2019.0048
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发表时间：
2020-02-06
期刊：
影响因子：
4.4
通讯作者：
Rempala, Grzegorz A.
中科院分区：
文献类型：
--
作者：
KhudaBukhsh, Wasiur R.;Choi, Boseung;Rempala, Grzegorz A.
在本文中，我们证明了描述马尔可夫随机流行病模型的大种群极限的常微分方程的解在分析从种群中抽样的个体的数据时可以解释为生存或累积危害函数。我们将从种群水平方程中导出的个体水平生存和危险函数称为生存动力系统（SDS）。为了说明群体水平动态如何隐含可用于统计推断的个体水平感染和恢复时间的概率规律，我们展示了基于合成数据的数值示例。在这些例子中，我们表明SDS分析优于完整数据的最大似然分析。最后，我们使用SDS方法分析了2009年华盛顿州立大学甲型H1N1流感爆发的数据。
In this paper, we show that solutions to ordinary differential equations describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a survival dynamical system (SDS). To illustrate how population-level dynamics imply probability laws for individual-level infection and recovery times that can be used for statistical inference, we show numerical examples based on synthetic data. In these examples, we show that an SDS analysis compares favourably with a complete-data maximum-likelihood analysis. Finally, we use the SDS approach to analyse data from a 2009 influenza A(H1N1) outbreak at Washington State University.