Designing optimal interventions to control HIV in Africa using data-based models

描述（由申请人提供）：我们的总体目标是开发详细的基于空间显式数据的艾滋病毒传播动态模型，并使用这些模型设计预防艾滋病毒感染的最佳干预措施。我们提出的研究建立在我们过去二十年对艾滋病毒和其他传染病建模的研究基础上，但将我们的研究带入了一个新的方向：(i)侧重于建立基于数据的详细空间显式模型，以及（ii）使用优化技术设计艾滋病毒干预措施。此外，我们提议的新研究方向涉及新的合作：特别是与哥伦比亚大学，疾病控制和预防中心（CDC）和非洲艾滋病毒和艾滋病综合方案（ACHAP）；该计划是博茨瓦纳政府与比尔及梅林达·盖茨基金会之间的一项合作。我们的目标是使用基于数据的模型设计博茨瓦纳基于男性包皮环切术（MC）和暴露前预防（PrEP）的干预措施的最佳推广计划。博茨瓦纳是世界上艾滋病毒感染率最高的国家之一；在该国73万名年龄在15岁至49岁之间的成年人中，有40%感染了bbb。通过构建详细的空间显式传播模型和采用数学优化技术，我们将能够展示如何最大化MC和PrEP干预措施的有效性。具体而言，我们将确定应该开始推出这些干预措施的地理位置，并确定应该针对的性别（PrEP）和年龄组（PrEP和MC）。通过开发高分辨率的基于数据的空间风险图，我们将能够对结核杆菌病（和PrEP）对博茨瓦纳发病率（和耐药性）的潜在流行病学影响做出现实的预测。此外，风险图将使我们能够评估政府的MC推出计划的可行性。我们的方法和结果将能够用于确定和评估博茨瓦纳和其他非洲国家有关MC和PrEP干预措施的关键卫生政策决定。

DESCRIPTION (provided by applicant): Our overall objective is to develop detailed spatially-explicit data-based HIV transmission dynamic models and to use these models to design optimal interventions for preventing HIV infections. The research we are proposing builds upon our past two decades of research on modeling HIV and other infectious diseases, but takes our research in a new direction by: (i) focusing on building detailed data-based spatially-explicit models and (ii) using optimization techniques to design HIV interventions. In addition our proposed new research direction involves new collaborations: specifically with Columbia University, the Center for Disease Control and Prevention (CDC) and the African Comprehensive HIV and AIDS Program (ACHAP); ACHAP is a collaboration between the Government of Botswana and the Bill and Melinda Gates Foundation. We aim to design optimal rollout plans for interventions based on male circumcision (MC) and pre exposure prophylaxis (PrEP) in Botswana using data-based models. Botswana has one of the highest levels of HIV worldwide; ~40% of its' 730,000 adults between the ages of 15 & 49 are infected [1]. By constructing detailed spatially-explicit transmission models and employing mathematical optimization techniques we will be able to show how to maximize the effectiveness of MC and PrEP interventions. Specifically, we will identify the geographic locations where the rollout of these interventions should begin, and determine the gender (for PrEP) and age-groups (for PrEP and MC) that should be targeted. By developing high resolution data-based spatial risk maps we will be able to make realistic predictions for the potential epidemiological impact of MC (and PrEP) on incidence (and drug resistance) in Botswana. In addition the risk maps will enable us to assess the feasibility of the Governments' MC rollout plan. Our methods and results will be able to be used to determine and evaluate critical health policy decisions regarding MC and PrEP interventions in Botswana and other African countries. PUBLIC HEALTH RELEVANCE: We will develop detailed spatially-explicit data-based HIV transmission dynamic models and use these models to design optimal interventions for preventing HIV infections. Our methods and results will be able to be used to determine and evaluate critical health policy decisions regarding MC and PrEP interventions in Botswana, as well as in other African countries. Therefore our results will have direct relevance for health policy makers and Governments in Africa, as well as for other scientists.