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In stopping the spread of infectious diseases, pathogen genomic data can beused to reconstruct transmission events and characterize population-levelsources of infection. Most approaches for identifying transmission pairs do notaccount for the time that passed since divergence of pathogen variants inindividuals, which is problematic in viruses with high within-host evolutionaryrates. This is prompting us to consider possible transmission pairs in terms ofphylogenetic data and additional estimates of time since infection derived fromclinical biomarkers. We develop Bayesian mixture models with an evolutionaryclock as signal component and additional mixed effects or covariate randomfunctions describing the mixing weights to classify potential pairs into likelyand unlikely transmission pairs. We demonstrate that although sources cannot beidentified at the individual level with certainty, even with the additionaldata on time elapsed, inferences into the population-level sources oftransmission are possible, and more accurate than using only phylogenetic datawithout time since infection estimates. We apply the approach to estimateage-specific sources of HIV infection in Amsterdam MSM transmission networksbetween 2010-2021. This study demonstrates that infection time estimatesprovide informative data to characterize transmission sources, and shows howphylogenetic source attribution can then be done with multi-dimensional mixturemodels.

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates