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Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks

Xavier Didelot, Christophe Fraser, Jennifer Gardy, Caroline Colijn
doi: https://doi.org/10.1101/065334
Xavier Didelot
1Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London, W2 1PG, United Kingdom
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Christophe Fraser
1Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London, W2 1PG, United Kingdom
2Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
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Jennifer Gardy
3Communicable Disease Prevention and Control Services, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
4School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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Caroline Colijn
5Department of Mathematics, Imperial College, London SW7 2AZ, UK
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Abstract

Genomic data is 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 colouring 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 colouring approach can incorporate a variable number of unique colours 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 our technique in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 22, 2016.
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Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks
Xavier Didelot, Christophe Fraser, Jennifer Gardy, Caroline Colijn
bioRxiv 065334; doi: https://doi.org/10.1101/065334
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Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks
Xavier Didelot, Christophe Fraser, Jennifer Gardy, Caroline Colijn
bioRxiv 065334; doi: https://doi.org/10.1101/065334

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