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.

翻译：摘要：在遏制传染病传播的过程中，病原体基因组数据可用于重建传播事件并描述群体层面的感染来源。大多数识别传播对的方法未考虑个体内病原体变异发生后所经过的时间，这对具有高宿主内进化速率的病毒而言存在问题。这促使我们基于系统发育数据及源自临床生物标志物的额外感染时间估计，来考量可能的传播对。我们开发了贝叶斯混合模型，以进化时钟作为信号成分，并引入描述混合权重的附加混合效应或协变量随机函数，从而将潜在传播对归类为可能或不可能的传播对。我们证明，即便有额外的时间流逝数据，尽管无法在个体层面确定性识别来源，但对群体层面传播来源的推断仍具可行性，且其准确性优于仅使用无感染时间估计的系统发育数据。我们将该方法应用于估计2010-2021年间阿姆斯特丹MSM传播网络中HIV感染的年龄特异性来源。本研究证实，感染时间估计提供了表征传播来源的具有信息量的数据，并展示了如何通过多维混合模型实现系统发育源归因。