Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.

ABSTRACT: In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission

SUBMITTER: Blenkinsop A 

PROVIDER: S-EPMC11951470 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature