Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2.

ABSTRACT: Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled loca

SUBMITTER: Lemey P 

PROVIDER: S-EPMC7547076 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature