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

项目名称：使用系统发育分析的大规模下一代艾滋病毒序列数据集和计算机模拟，以评估高风险人群对ART推出在Uganda.BackgroundA巨大的全球扩张的可用性抗逆转录病毒（ARV）治疗艾滋病毒阳性的个人已经发生在过去的10年，现在超过15万，出了37万，是在treatment1。有效的治疗阻止了艾滋病毒的传播，每年的新感染病例比15年前减少了10%，尽管艾滋病毒阳性者增加了20%。联合国艾滋病规划署寻求到2020年将艾滋病毒传播减少25%，将治疗比例提高到已知感染者的90%。然而，人们一直假设撒哈拉以南非洲“普遍”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，但事实远非如此。乌干达在管理艾滋病毒流行方面享有很好的声誉，但它有150多万艾滋病毒阳性者。虽然农村人口中只有5%是艾滋病毒阳性，但在维多利亚湖周围，在海岸或岛屿上，与渔业有关的人口中艾滋病毒阳性率高达25%。如果这些人群通过社区之间的传播在很大程度上助长了全国性的流行病，那么国家抗逆转录病毒药物推广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差异，该项目将利用Leigh Brown小组开发的现有艾滋病毒流行病计算模拟模型。通过分析人口接触结构的关键特征（网络结构和动态，如我们对艾滋病毒流行病学的先验知识所告知的）的现有数据，将对模型进行调整，以匹配所研究人口的已知特征，并将输出所描述的人口类型产生的病毒基因组。作为它们演变的途径，“传播树”在模拟中是已知的，这允许测试分析方法的好坏，并确定可能影响疾病控制决策的关键假设，因此需要改进。对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，目的是确定接触结构或混合对观察到的感染率的影响，特别是个人层面的传播模式与社区层面观察之间的关系。这将使我们认识到降低这些社区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，因此必须显示国家影响，以说服负责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一个人高度相关。培训成果通过该项目的结束，学生将成为计算生物学方面的专家，包括用一种或多种语言编程、序列数据的统计分析（基于可能性和贝叶斯）、复杂人群的流行病学建模，并熟悉有关提供干预措施的问题全球健康策略。

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.