Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.

ABSTRACT: Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-s

SUBMITTER: Bu F 

PROVIDER: S-EPMC10875513 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature