  Viral deep-sequencing data play a crucial role toward understanding disease
transmission network flows, because the higher resolution of these data
compared to standard Sanger sequencing provide evidence into the direction of
infectious disease transmission. To more fully utilize these rich data and
account for the uncertainties in phylogenetic analysis outcomes, 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 gender
and age, and the point type 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-sequenece phylogenetic analysis. Our method is able to jointly
infer the latent transmission statuses 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 a
fully Bayesian inference scheme. By directly modeling continuous spatial
processes with smooth densities, our method enjoys significant computational
advantages compared to previous methods that rely on discretization of the
covariate space. We demonstrate that our framework can capture age structures
in HIV transmission at high resolution, and bring valuable insights in a case
study on viral deep-sequencing data from Southern Uganda.
