Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth - death model
Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth - death model
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DOI：
10.1098/rsif.2018.0512
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发表时间：
2018-09-01
影响因子：
3.9
通讯作者：
Stadler, Tanja
中科院分区：
文献类型：
--
作者：
Barido-Sottani, Joelle;Vaughan, Timothy G.;Stadler, Tanja
艾滋病毒感染者在艾滋病毒传播网络中形成集群。准确识别这些传播集群对于有效地针对公共卫生干预措施至关重要。聚类的一个原因是底层联系人网络包含许多本地社区。我们提出了一种新的最大似然法识别社区结构引起的传播集群，基于系统发育树。该方法采用多状态出生-死亡（MSBD）模型，该模型检测传播率的变化，将其解释为将流行病引入新的易感社区，即形成新的集群。我们表明，MSBD方法是能够可靠地推断集群和传输参数从病原体的致病性基于我们的模拟。与现有的基于分割点的聚类识别方法相比，我们的方法不要求聚类是单系的，也不依赖于难以解释的分割点参数的选择。我们提出了一个应用程序，我们的方法从瑞士电视队列研究的数据。该方法作为一个易于使用的R包提供。
HIV patients form clusters in HIV transmission networks. Accurate identification of these transmission clusters is essential to effectively target public health interventions. One reason for clustering is that the underlying Contact network contains many local communities. We present a new maximum-likelihood method for identifying transmission clusters caused by community structure, based on phylogenetic trees. The method employs a multi-state birth-death (MSBD) model which detects changes in transmission rate, which are interpreted as the introduction of the epidemic into a new susceptible community, i.e. the formation of a new cluster. We show that the MSBD method is able to reliably infer the clusters and the transmission parameters from a pathogen phylogeny based on our simulations. In contrast to existing cutpoint-based methods for cluster identification, our method does not require that clusters be monophyletic nor is it dependent on the selection of a difficult-to-interpret cutpoint parameter. We present an application of our method to data from the Swiss TV Cohort Study. The method is available as an easy-to-use R package.