Didelot Xavier, Fraser Christophe, Gardy Jennifer, Colijn Caroline
Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London, United Kingdom.
Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Mol Biol Evol. 2017 Apr 1;34(4):997-1007. doi: 10.1093/molbev/msw275.
Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.
基因组数据越来越多地被用于理解传染病流行病学。对给定疫情中的分离株进行测序，并利用共享变异模式来推断疫情中的哪些分离株彼此之间关系最为密切。不幸的是，通常用于表示这种变异的系统发育树并不能直接说明谁感染了谁——系统发育树不是传播树。然而，可以从系统发育中推断出传播树，通过根据分支所在的宿主对系统发育树的分支进行着色来考虑宿主内的遗传多样性。在这里，我们扩展了这种方法，并表明它可以应用于部分采样和正在进行的疫情。这需要计算观察到的传播树的正确概率，我们在此展示了如何针对一大类流行病学模型做到这一点。我们还展示了分支着色方法如何可以纳入可变数量的独特颜色来表示传播链中未采样的中间宿主。由此产生的算法是一种可逆跳跃蒙特卡罗马尔可夫链，我们将其应用于结核病疫情的模拟数据和真实数据。通过考虑未采样的病例以及可能尚未结束的疫情，我们的方法特别适合在实时疫情调查的公共卫生环境中使用。我们在一个名为TransPhylo的R包中实现了这种传播树推断方法，该包可从https://github.com/xavierdidelot/TransPhylo免费获取。
