Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.

ABSTRACT: There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close

SUBMITTER: Campbell F 

PROVIDER: S-EPMC6457559 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature