Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates
Author(s)
Blenkinsop, Alexandra
Sofocleous, Lysandros
Di Lauro, Francesco
Kostaki, Evangelia Georgia
Van Sighem, Ard
Type
Journal Article
Abstract
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 passing since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host
evolutionary rates. This prompted 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.
approaches for identifying transmission pairs do not account for the time passing since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host
evolutionary rates. This prompted 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.
Date Issued
2025-03-12
Date Acceptance
2024-12-09
Citation
Statistical Methods in Medical Research
ISSN
0962-2802
Publisher
SAGE Publications
Journal / Book Title
Statistical Methods in Medical Research
Copyright Statement
© The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
License URL
Identifier
10.1177/09622802241309750
Subjects
phylodynamics
HIV prevention
evolutionary clock
Publication Status
Published online
Rights Embargo Date
10000-01-01