  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 that passed since divergence of pathogen variants in
individuals, which is problematic in viruses with high within-host evolutionary
rates. This is prompting 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 signal component and additional mixed effects or covariate random
functions describing the mixing weights to classify potential pairs into likely
and unlikely transmission pairs. We demonstrate that although sources cannot be
identified at the individual level with certainty, even with the additional
data on time elapsed, inferences into the population-level sources of
transmission are possible, and more accurate than using only phylogenetic data
without time since infection estimates. We apply the approach to estimate
age-specific sources of HIV infection in Amsterdam MSM transmission networks
between 2010-2021. This study demonstrates that infection time estimates
provide informative data to characterize transmission sources, and shows how
phylogenetic source attribution can then be done with multi-dimensional mixture
models.
