Title:           Inference of communicable disease transmission using genomic data

Abstract:  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 phylogenetic trees. However these are not directly informative about who infected whom: a phylogenetic tree is not a transmission tree. 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. We show how this approach can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of a partially observed transmission tree and we demonstrate how to do this for a large class of epidemiological models. The resulting uncertainty on who infected whom can be high and we explore two solutions to this problem: the use of several genomes per host, and the use of additional epidemiological data.

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Bio: Xavier Didelot studied for his doctorate in the Department of Statistics at the University of Oxford, and carried postdoctoral research work in Warwick and Oxford. He also previously worked for several years in the Department of Infectious Disease Epidemiology at Imperial College London, before taking his current appointment as Professor of Statistical Epidemiology and Genomics at the University of Warwick. Xavier Didelot's research is concerned with understanding the way bacterial pathogens evolve, spread and cause disease. He has analysed both epidemiological and genomic data from a wide range of bacteria. A key aim is to develop new bioinformatics and statistical methods that can handle the very large amounts of data made available by novel high-throughput sequencing techniques.