Inferring HIV Transmission Patterns from Viral Deep-Sequence Data via Latent Spatial Poisson Processes

Music City Center 

Viral deep-sequencing technologies play a crucial role toward understanding disease transmission patterns, because the higher resolution of these data provide evidence on transmission direction. To better utilize these data and account for uncertainty in phylogenetic analysis, we propose a spatial Poisson process model to uncover HIV transmission flow patterns at the population level. We represent pairings of two individuals 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. Our 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, our framework does not require pre-classification of the transmission statuses of data points, instead learning them probabilistically through fully Bayesian inference. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages over previous methods that discretize the covariate space. In a HIV transmission study from Rakai, Uganda, we demonstrate that our framework can capture age structures in HIV transmission at high resolution and bring valuable insights. (This is joint work with Kate Grabowski, Joseph Kagaayi, Oliver Ratmann, and Jason Xu.)

Latent Spatial Poisson Processes