Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.
Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.
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DOI：
10.1093/molbev/msw275
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发表时间：
2017-04-01
影响因子：
10.7
通讯作者：
Colijn C
中科院分区：
文献类型：
--
作者：
Didelot X;Fraser C;Gardy J;Colijn C
基因组数据越来越多地被用于了解传染病流行病学。对来自给定爆发的分离株进行测序，并使用共享变异的模式来推断爆发中的哪些分离株彼此关系最密切。不幸的是，系统发生树通常用来代表这种变化是没有直接的信息谁感染谁系统发生树不是一个传播树。然而，一个传输树可以推断出从一个有性生殖，而占宿主内的遗传多样性，着色的有性生殖的分支，根据这些分支的主机。在这里，我们扩展了这种方法，并表明它可以适用于部分抽样和正在进行的疫情。这需要计算正确的概率所观察到的传播树，我们在这里演示了如何做到这一点的一大类流行病学模型。我们还演示了如何的分支着色方法可以将一个可变数量的独特的颜色来表示未采样的中间传输链。由此产生的算法是一个可逆的跳跃蒙特-卡罗马尔可夫链，我们应用到模拟数据和真实的数据从结核病的爆发。通过考虑未抽样病例和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期间在公共卫生环境中使用。我们在一个名为TransPhylo的R包中实现了这种传输树推理方法，该包可从www.example.com免费获得。
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.
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影响因子：
9.4
作者：
Diel, R;Rüsch-Gerdes, S;Niemann, S
通讯作者：
Niemann, S
影响因子：
3.4
作者：
Fourment, Mathieu;Holmes, Edward C.
通讯作者：
Holmes, Edward C.
影响因子：
2.1
作者：
Farrington, CP;Kanaan, MN;Gay, NJ
通讯作者：
Gay, NJ
DOI：
10.1126/science.1259657
发表时间：
2014-09-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gire SK;Goba A;Andersen KG;Sealfon RS;Park DJ;Kanneh L;Jalloh S;Momoh M;Fullah M;Dudas G;Wohl S;Moses LM;Yozwiak NL;Winnicki S;Matranga CB;Malboeuf CM;Qu J;Gladden AD;Schaffner SF;Yang X;Jiang PP;Nekoui M;Colubri A;Coomber MR;Fonnie M;Moigboi A;Gbakie M;Kamara FK;Tucker V;Konuwa E;Saffa S;Sellu J;Jalloh AA;Kovoma A;Koninga J;Mustapha I;Kargbo K;Foday M;Yillah M;Kanneh F;Robert W;Massally JL;Chapman SB;Bochicchio J;Murphy C;Nusbaum C;Young S;Birren BW;Grant DS;Scheiffelin JS;Lander ES;Happi C;Gevao SM;Gnirke A;Rambaut A;Garry RF;Khan SH;Sabeti PC
通讯作者：
Sabeti PC
影响因子：
4.5
作者：
Didelot X;Pang B;Zhou Z;McCann A;Ni P;Li D;Achtman M;Kan B
通讯作者：
Kan B