Using phylogenetic analysis of large-scale Next-Gen HIV sequence datasets and computer simulation to assess the impact of high-risk populations on the

项目标题：使用大规模下一代艾滋病毒序列数据集的系统发育分析和计算机模拟来评估高危人群对乌干达抗逆转录病毒治疗推广的影响。背景过去10年来，针对艾滋病毒阳性个人的抗逆转录病毒(ARV)疗法在全球范围内发生了巨大的扩张，现在总共3700万人中有超过1500万人正在接受治疗1。有效的治疗阻止了艾滋病毒的传播，尽管艾滋病毒携带者人数增加了约20%，但每年的新感染人数已经比15年前减少了10%。联合国艾滋病规划署寻求通过将治疗比例提高到已知感染者的90%，到2020年将艾滋病毒传播率减少25%。然而，它一直假设撒哈拉以南非洲“普遍的”异性恋艾滋病毒流行的所有社区在传播率和获得护理方面都是相似的，情况远非如此。乌干达以很好地控制艾滋病毒流行而闻名，但该国有150多万艾滋病毒携带者。尽管在农村人口中只有5%的人感染了艾滋病毒，但在维多利亚湖周围，有一些人与渔业有关，在湖边或岛屿上，高达25%的人感染了艾滋病毒。如果通过社区间传播，这些人口对全国流行病有很大贡献，则国家抗逆转录病毒方案的有效性将受到威胁。该项目将把艾滋病毒序列分析与进化和流行病学驱动的数学和模拟模型结合起来，以估计社区之间的传播并预测其影响。为了了解新的数据和方法将产生多大的不同，该项目将利用Leigh Brown小组开发的现有艾滋病毒流行计算模拟模型。通过分析人口接触结构的关键特征的现有数据(网络结构和动态，根据我们对艾滋病毒流行病学的先验知识)，该模型将进行调整，以匹配所研究的人口的已知特征，并将输出从所描述的人口类型产生的病毒基因组。作为它们进化的路线，“传播树”在模拟中是已知的，这允许测试分析方法有多好，并确定可能影响疾病控制决策的关键假设，因此需要改进。目的基因组数据的分析将为人口接触模型的发展提供信息。利用高教授在密集数据集网络分析方面的专业知识，目标是确定接触者结构或混合对观察到的感染率的影响，特别是个人水平传播模式与社区水平观察之间的关系。这将使我们了解降低这些社区的感染率对普通人群感染率的重要性。由于这可能需要比其他地方更多的人均支出，重要的是要证明国家影响，以说服负责的机构最有效地集中精力。这项工作的结果将与参与在撒哈拉以南非洲推广抗逆转录病毒疗法的每个人高度相关。培训成果通过项目的总结，学生将成为计算生物学方面的专家，包括一种或多种语言的编程，序列数据的统计分析(基于似然和贝叶斯)，复杂人群中的流行病学建模，并熟悉全球卫生干预策略的交付问题。

Project Title: Using phylogenetic analysis of large-scale Next-Gen HIV sequence datasets and computer simulation to assess the impact of high-risk populations on the ART rollout in Uganda.BackgroundA huge global expansion of the availability of antiretroviral (ARV) therapy for HIV-positive individuals has occurred in the last 10 years and now over 15 million, out of a total of 37 million, are on treatment1. Effective treatment stops HIV transmission and already there are annually 10% fewer new infections than 15 years ago although there are ~20% more HIV+ people. UNAIDS seeks to reduce HIV transmission by 25% by 2020 by increasing the proportion on treatment to 90% of those known to be infected2. However, it has been assuming that all communities in the "generalised" heterosexual HIV epidemics of sub-Saharan Africa are similar in transmission rate and access to care and this is far from the case. Uganda has a strong reputation for managing its HIV epidemic well, but it has more than 1.5 million HIV+ individuals. Although in the rural population only 5% are HIV+, around Lake Victoria there are populations associated with the fishing industry, on the lake shore, or on islands, where up to 25% are HIV+. If, by transmission between communities, these populations contribute substantially to the national epidemic, the effectiveness of the national programme for ARV rollout will be threatened. This project will integrate HIV sequence analysis with evolutionary and epidemiologically driven mathematical and simulation models in order to estimate transmission between communities and predict its impact. To understand how much difference the new data and methods will make the project will exploit an existing computational simulation model of an HIV epidemic that has been developed in the Leigh Brown group. By analysing available data for key features of the population contact structure (network structure and dynamics, as informed by our prior knowledge of HIV epidemiology), the model will be adjusted to match the known features of the populations studied, and will output virus genomes that arise from the type of population that is described. As the route by which they evolved, the "transmission tree" is known in the simulation, this allows testing of how good the methods of analysis are, and identifies key assumptions that could influence decision-making in regards to disease control and therefore require refinement. AimsAnalysis of the genome data will inform the development of models of population contact. Making use of Prof Kao's expertise in network analysis of dense datasets, the aim will be to identify the influence that contact structure or mixing has on the observed infection rate, and in particular the relationship between individual level patterns of transmission, and the community level observations. This will inform our understanding of the importance of reducing infection rates in these communities for infection rates in the general population. As that may require greater expenditure per person than elsewhere, it will be important to demonstrate the national impact to persuade the agencies responsible to focus most effectively. The results of this work will be highly relevant to everyone involved in the rollout of ARV therapy in sub-Saharan Africa. Training OutcomesBy the conclusion of the project the student will be expert in computational biology including programming in one or more languages, statistical analysis of sequence data (likelihood-based and Bayesian), epidemiological modeling in complex populations and familiar with issues around delivery of intervention strategies for global health.