期刊
MOLECULAR BIOLOGY AND EVOLUTION
卷 34, 期 4, 页码 997-1007出版社
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msw275
关键词
genomic epidemiology; transmission analysis; infectious disease outbreak
资金
- UK National Institute for Health Research Health Protection Research Unit in Modelling Methodology at Imperial College London
- Public Health England [HPRU-2012-10080]
- UK Medical Research Council [MR/N010760/1]
- Engineering and Physical Sciences Research Council of the UK [EP/K026003/1]
- BBSRC [BB/I00713X/1] Funding Source: UKRI
- EPSRC [EP/K026003/1] Funding Source: UKRI
- MRC [G0800596, MR/N010760/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/I00713X/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/K026003/1] Funding Source: researchfish
- Medical Research Council [MR/N010760/1, G0800596, MR/K010174/1B] Funding Source: researchfish
- National Institute for Health Research [HPRU-2012-10080] Funding Source: researchfish
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 realtime 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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