Reconstructing HIV Epidemics from HIV Phylogenetics

项目摘要 艾滋病毒继续在少数群体中零星地传播，主要是从那些没有意识到自己 感染。更有效地定位未确诊的艾滋病毒携带者进行治疗，以及监测 为了加强预防工作，需要更好的流行病学技术。我们的项目汇集了一支 来自临床、分子生物学、流行病学、数学和进化的经验丰富的研究人员 菲尔兹。我们将结合进化理论，多尺度，开发创新的流行病学方法 动态建模、人工智能以及大规模临床和序列数据。在这次更新中，我们将 对我们之前关于HIV在宿主内的进化过程如何与流行病学相互作用的工作进行扩展 动力学。在量化了传播历史和由此产生的艾滋病毒系统发展之间的联系之后， 我们将艾滋病毒的演变和流行病学之间的关系概念化为三个层面： 在宿主内、在传播和在人口流行水平上。因为艾滋病毒生物学的基本过程 在对流行病水平进行建模时，进化在很大程度上被忽略了，在目标1中，我们检查了宿主内部 影响多元化的过程。我们将在新的合并中包括重组、选择和延迟 宿主内模式，以评估对流行病学水平的影响。我们还将量化以下方面的潜力- 主持人多方位的选择压力。在目标2中，我们关注的是发生在 变速箱。我们将开发一种新的基于前向时间概率模型的最大似然方法 改进了对多个传输方向和传输时间的推断 主机，并开发了一种传输异构性检测方法，既可以评估总体可能性 感染者之间的传播异质性，以及检测在系统发育树中的哪里超级 可能已经发生了传播。在目标3中，我们将开发机器学习方法来处理非常大的数据 SET(103-106名患者)，并使用额外的临床和人口学数据来增强系统发育，以便 重建潜在的传输历史。所有这三个目标都将涉及旨在发展 以及为下一代系统动力学应用改进方法。

Project Summary HIV continues to spread, episodically, among minority groups, and mostly from people unaware of their infection. To more efficiently locate undiagnosed people living with HIV for treatment, as well as to monitor prevention efforts, better epidemiological techniques are needed. Our project brings together a team of experienced researchers from clinical, molecular biology, epidemiological, mathematical, and evolutionary fields. We will develop innovative epidemiological methods by combining evolutionary theory, multi-scale dynamic modeling, artificial intelligence, and large-scale clinical and sequence data. In this renewal, we will expand on our previous work on how HIV within-host evolutionary processes interact with epidemiological dynamics. Having quantified the link between transmission history and the resulting HIV phylogeny among hosts, we conceptualize the relationship between the evolution and epidemiology of HIV into three levels: within-host, at transmission, and on the population epidemic level. Because essential processes of HIV biology and evolution have been largely ignored when modeling the epidemic level, in aim 1 we examine within-host processes that affect diversification. We will include recombination, selection, and latency in a new coalescent within-host model to evaluate the impact on the epidemiological level. We will also quantify potential within- host multi-directional selection pressures. In aim 2, we focus on mechanisms that occur around the time of transmission. We will develop a new maximum likelihood method based on a forward-time probabilistic model of transmission that improves the inference of transmission direction and time of transmission among multiple hosts, and develop a transmission heterogeneity detection method to both assess overall possible transmission heterogeneity among infected persons, as well as to detect where in a phylogeny super- spreading may have occurred. In aim 3, we will develop machine learning methods to handle very large data sets (103-106 patients), and use additional clinical and demographic data to augment phylogenies in order to reconstruct the underlying transmission history. All three aims will involve advancements aimed at developing and improving methods for the next generation of phylodynamic applications.