Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, because the higher resolution of these data compared to standard Sanger sequencing provide evidence into the direction of infectious disease transmission. To more fully utilize these rich data and account for the uncertainties in phylogenetic analysis outcomes, we propose a spatial Poisson process model to uncover HIV transmission flow patterns at the population level. We represent pairings of two individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age, and the point type representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequenece phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require pre-classification of the transmission statuses of data points, instead learning them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, and bring valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

翻译：病毒深度测序数据在理解疾病传播网络流中发挥着关键作用，因为相较于标准桑格测序，这些数据更高的分辨率能为传染病的传播方向提供证据。为更充分利用这些丰富数据并考虑系统发育分析结果中的不确定性，我们提出一个空间泊松过程模型，以揭示群体水平上的HIV传播流模式。我们将存在病毒序列数据配对的个体对表示为类型点，坐标代表性别、年龄等协变量，点类型代表未观察到的传播状态（关联性和方向性）。这些点与通过标准深度测序系统发育分析获得的传播状态证据强度评分相关联。我们的方法能够联合推断所有配对的潜在传播状态，以及源-接受者协变量空间上的传播流曲面。与现有方法不同，我们的框架无需对数据点的传播状态进行预分类，而是通过全贝叶斯推断方案概率性地学习这些状态。通过直接对具有平滑密度的连续空间过程建模，我们的方法相较于依赖协变量空间离散化的先前方法具有显著的计算优势。我们证明该框架能够以高分辨率捕捉HIV传播中的年龄结构，并在针对乌干达南部病毒深度测序数据的案例研究中提供有价值的见解。