Reconstructing HIV Epidemics from HIV Phylogenetics

点击翻译按钮获取中文摘要

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.