Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases.

ABSTRACT: Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of

SUBMITTER: Alamil M 

PROVIDER: S-EPMC6553606 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature