A0649
Title: Inferring HIV transmission patterns from viral deep-sequence data via latent spatial Poisson processes
Authors: Fan Bu - University of Michigan (United States) [presenting]
Abstract: Viral deep-sequencing technologies play a crucial role in understanding disease transmission patterns because the higher resolution of these data provides evidence on transmission direction. To better utilize these data and account for uncertainty in phylogenetic analysis, a spatial Poisson process model is proposed to uncover HIV transmission flow patterns at the population level. Pairings of two individuals are represented with viral sequence data as typed points, with coordinates representing covariates such as sex and age and the point type representing the unobserved transmission statuses (linkage and direction). Points are associated with deep-sequence phylogenetic analysis summary scores that reflect the strength of evidence for each transmission status. The method jointly infers the latent transmission status for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, the framework does not require pre-classification of the transmission statuses of data points; instead, it learns them probabilistically through full Bayesian inference. By directly modeling continuous spatial processes with smooth densities, the method enjoys significant computational advantages over previous methods that discretize the covariate space. In an HIV transmission study from Rakai, Uganda, the framework is demonstrated to capture age structures in HIV transmission at high resolution and bring valuable insights.
