Department of Biostatistics, University of California - Los Angeles, Los Angeles, CA 90024, United States.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Biometrics. 2024 Jan 29;80(1). doi: 10.1093/biomtc/ujad015.
Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types 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-sequence 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 preclassification of the transmission statuses of data points, and instead learns 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, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.
病毒深度测序数据在理解疾病传播网络流方面发挥着至关重要的作用，与标准的 Sanger 测序相比，它提供了更高的分辨率。为了更充分地利用这些丰富的数据，并考虑到系统发育分析结果中的不确定性，我们提出了一种空间泊松过程模型，以揭示人群水平的人类免疫缺陷病毒（HIV）传播流模式。我们将具有病毒序列数据的个体对表示为分型点，坐标表示性别和年龄等协变量，点类型表示未观察到的传播状态（连接和方向）。点与通过标准深度测序系统发育分析获得的每个传播状态的证据强度观察得分相关联。我们的方法能够联合推断所有配对的潜在传播状态和源-接受者协变量空间上的传播流表面。与现有方法不同，我们的框架不需要对数据点的传播状态进行预分类，而是通过完全贝叶斯推断方案对其进行概率学习。通过直接对具有平滑密度的连续空间过程进行建模，与依赖协变量空间离散化的先前方法相比，我们的方法具有显著的计算优势。我们证明了我们的框架可以以高分辨率捕捉 HIV 传播中的年龄结构，在乌干达南部的病毒深度测序数据的案例研究中提供了有价值的见解。
