Bayesian reconstruction of transmission within outbreaks using genomic variants

Publishing date: 2018-04-18

Published on: PLOS Computational Biology

summary: Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics. Nicola De Maio et al here propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error.

authors: Nicola De Maio, Colin J. Worby, Daniel J. Wilson, Nicole Stoesser

link to paper: 10.1371/journal.pcbi.1006117

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