ABSTRACT

Simple branching processes for infectious disease transmission assume all individuals are homogeneous, which means that risk factors that may inhibit or increase transmission are unable to be identified. In this work, we develop a branching process model that allows for identification of risk factors by assuming the probability of onward transmission is determined by the individual’s covariates. Because enumerating the transmission trees is infeasible for large clusters, we develop an algorithm to sample transmission trees to compute approximate maximum likelihood estimates. We then discuss how our model can be extended to account for cases that are undetected but are part of the true transmission tree. We use our method to investigate individual characteristics that are associated with transmission of Tuberculosis using clusters of detected cases in Maryland from 2003 to 2009. We find that later detection within a cluster is associated with an increased probability of onward transmission (OR = 1.41 [95% CI: 1.31, 1.52]). We show some of most likely transmission trees from our model, and results can be reproduced via our R package InfectionTrees. Supplementary files for this article are available online.

摘要

传染病传播的简单分支过程假设所有个体都是同质的，这意味着无法确定可能抑制或增加传播的风险因素。在这项工作中，我们开发了一个分支过程模型，该模型允许通过假设向前传播的概率由个体的协变量确定来识别风险因素。因为枚举传输树对于大型集群是不可行的，我们开发了一种算法来采样传输树以计算近似最大似然估计。然后，我们讨论如何扩展我们的模型以考虑未检测到但属于真实传输树的一部分的情况。我们使用我们的方法使用 2003 年至 2009 年在马里兰州检测到的病例群来调查与结核病传播相关的个体特征。我们发现，在一个集群中的后期检测与向前传播的概率增加有关（OR = 1.41 [95 % CI：1.31，1.52]）。我们从我们的模型中展示了一些最有可能的传播树，结果可以通过我们的 R 包 InfectionTrees 重现。本文的补充文件可在线获取。