Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan Africa

项目总结/摘要 艾滋病毒是撒哈拉以南非洲成年人死亡的最大单一原因， 占成人死亡人数的三分之一。在撒哈拉以南非洲阻止艾滋病毒的关键模式之一是 鉴定受感染的个人和人群，以便提供生物医学和行为医学服务， 干预措施。然而，到2015年底，只有不到一半的艾滋病毒感染者获得了 抗逆转录病毒治疗（ART），尽管扩大了资格和正在努力诊断和启动 治疗更好地理解社会，行为，环境和经济背景， 影响艾滋病毒风险可以提高旨在识别和 以艾滋病毒感染者为目标。针对“利用大数据， 遏制艾滋病毒”（PA-15-273），该提案的总体目标是开发大规模的新分析工具， 预测艾滋病毒感染风险的数据，并产生关于新的或未被认识到的风险的假设 撒哈拉以南非洲地区。我们计划四个主要研究：（1）提取和协调所有子， 具有国家代表性的撒哈拉非洲人口和健康调查，其中包括艾滋病毒状况 在29个国家收集了60多万名男性和女性， （2）开发基于LASSO和XWAS的HIV感染预测分析工具 对社会、行为、环境和经济风险因素的现状和假设;（3） 在多国、大规模数据中确定艾滋病毒风险，并综合撒哈拉以南非洲地区的调查结果 （4）制定生物伦理学计划，以确定非洲新干预措施和政策的目标， 在文化和道德上都是正确的。该项目将开发利用大数据和高吞吐量 为全球艾滋病毒控制服务的分析方法。本项目的产出包括（1） 可访问的软件代码，用于有效地探索来自 最大的高维度，协调，全国代表性的代表性集合 家庭调查在世界上，（2）广泛的社会，环境，行为， 和经济因素预测艾滋病毒感染的60多万人检测艾滋病毒在29分， 撒哈拉国家，以及（3）道德框架，以实现实用，相关和适当的翻译 和调查结果的交流。新的艾滋病毒感染模式将有助于确定高危群体 以及制定干预措施，以遏制撒哈拉以南非洲的艾滋病毒流行。

Project Summary/Abstract HIV is the largest single cause of death among adults in Sub-Saharan Africa, responsible for about a third of all deaths among adults. One of the key paradigms to halting HIV in Sub-Saharan Africa relies on identification of infected individuals and populations for delivery of biomedical and behavioral interventions. However, by the end of 2015 less that half of HIV-infected individuals accessed antiretroviral therapy (ART) despite expansion of eligibility and ongoing efforts to diagnose and initiate treatment. A better understanding of the social, behavioral, environmental, and economic contexts that influence HIV risk could improve the effectiveness and efficiency of programs that aim to identify and target HIV-infected populations. In response to the program announcement for “Harnessing Big Data to Halt HIV” (PA-15-273), the overall goal of this proposal is to develop new analytic tools in large-scale data to predict risk of HIV infection and to generate hypotheses about new or under-recognized risk factors in Sub-Saharan Africa. We plan four primary investigations: (1) Extract and harmonize all Sub- Saharan African nationally representative Demographic and Health Surveys that include the HIV status of over 600,000 men and women collected in 29 countries, and hundreds to thousands of associated exposure variables; (2) Develop analytic tools based on LASSO and XWAS to predict HIV infection status and generate hypotheses about social, behaviorial, environmental, an economic risk factors; (3) Identify HIV risk in multi-country, large-scale data and synthesize findings across in Sub-Saharan Africa, and (4) develop a bioethics program to identify targets for new interventions and policies in a culturally and ethically sound manner. The project will develop leverage big data and high-throughput analytic methodology in the service of global HIV control. The outputs of this project include (1) accessible software code for efficient exploration of robust correlates of HIV status derived from the biggest collection of high-dimensional, harmonized, and nationally representative representative household surveys in the world, (2) an extensive landscape of the social, environmental, behavioral, and economic factors predictive of HIV infection among over 600K people tested for HIV in 29 Sub- Saharan countries, and (3) a ethical framework to enable practical, relevant, and appropriate translation and communication of findings. New models of HIV infection will facilitate identification of at-risk groups and the development of interventions to halt the HIV epidemic in Sub-Saharan Africa.