Inferring the Sources of HIV Infection in Africa from Deep-Sequence Data with Semi-Parametric Bayesian Poisson Flow Models
提出一种半参数贝叶斯泊松模型，利用病原体深度测序数据推断人群层面的传染病传播流向和感染来源，并应用于乌干达Rakai的HIV数据，发现青少年和年轻女性主要通过年龄差异关系感染。
Abstract Pathogen deep-sequencing is an increasingly routinely used technology in infectious disease surveillance. We present a semi-parametric Bayesian Poisson model to exploit these emerging data for inferring infectious disease transmission flows and the sources of infection at the population level. The framework is computationally scalable in high-dimensional flow spaces thanks to Hilbert Space Gaussian process approximations, allows for sampling bias adjustments, and estimation of gender- and age-specific transmission flows at finer resolution than previously possible. We apply the approach to densely sampled, population-based HIV deep-sequence data from Rakai, Uganda, and find substantive evidence that adolescent and young women were predominantly infected through age-disparate relationships in the study period 2009–2015.