Optimal Strategies for HIV Treatment and Prevention in Sub-Saharan Africa

 描述(由申请人提供)：这些工具现在可以有效地治疗艾滋病毒；实施可以预防它的干预措施；甚至，根据建模研究，有可能消除它。然而，在艾滋病毒普遍流行的撒哈拉以南非洲(SSA)国家，最需要治疗和干预的国家，缺乏资源可能会限制此类计划的影响。本申请中提出的三项连续研究(分别为具体目标1-3)将结合建模和统计分析，以确定如何以最大的效率和效力部署有限的资源，以实现明确界定的治疗和预防目标。我们提出并将检验地理优化(Geo)假设：通过将资源不成比例地分配到发病率高于全国平均水平的地区，即通过使用地理目标，可以提高普遍流行的SSA国家艾滋病毒治疗方案和干预措施的效率(因此也就是有效性)。我们将重点关注SSA两种不同的广义艾滋病毒流行“类型”：主要是城市(在博茨瓦纳发现的“类型”)和主要是农村(在莱索托发现的“类型”)。这两种流行病都是世界上最严重的疫情之一。我们将通过与两个非政府组织(ACHAP和PIH)合作，最大限度地发挥我们工作的重要性，这两个非政府组织分别负责设计和实施博茨瓦纳和莱索托的艾滋病毒治疗和预防计划。该项目将艾滋病毒建模的两种新方法联系起来：(1)地理分析，通过估计感染和高危个人的数量和地理位置(我们将通过分析两国提供的详细的地理参考艾滋病毒数据来完成这一工作)，我们可以将治疗方案和干预措施定向到最需要它们的地理区域的社区；(2)优化我们将使用两国的地理参考数据来建立数学模型，我们将使用优化技术进行分析，以确定如何最有效地利用有限的资源。我们采用的优化方法将以地理位置假说为基础，并为每个目标量身定做：目标1，最大限度地减少治疗供应链中断的可能性；目标2，最大限度地(鉴于资源限制)两种干预措施(自愿男性医疗包皮环切术和预防治疗(TasP))对预防艾滋病毒感染的影响；目标3，最大限度地减少使用以TasP为基础的干预措施消除艾滋病毒所需的资源。一个跨学科的合作团队将进行这项研究，其中包括来自其重点国家的专家。由于我们将使用博茨瓦纳和莱索托的数据来参数化我们的模型，我们的结果将为他们的政策制定者提供特别的信息，使基于证据的决策成为可能。值得注意的是，我们将开发的方法和模型将足够普遍，足以适用于其他23个SSA国家，这些国家已经概括了流行病并参考了人口健康调查中的艾滋病毒数据。我们解决的问题(例如，如何最大限度地减少缺货)与所有25个国家/地区都相关。

 DESCRIPTION (provided by applicant): The tools are now available to effectively treat HIV; to implement interventions that could prevent it; and even, according to modeling studies, to potentially eliminate it. However, in the Sub-Saharan African (SSA) countries with generalized HIV epidemics where treatment and intervention is most needed, a lack of resources could limit the impact of such programs. The three sequential studies proposed in this application (Specific Aims 1-3, respectively) will combine modeling and statistical analyses to determine how limited resources can be deployed with maximum efficiency, and effectiveness, to achieve clearly defined goals for treatment and prevention goals. We propose, and will test, the Geographic Optimization (GeO) Hypothesis: the efficiency (and thus effectiveness) of HIV treatment programs and interventions in SSA countries with generalized epidemics can be increased by disproportionately allocating resources to areas where incidence is higher than the national average, i.e., by using geographic targeting. We will focus on two different "types" of generalized HIV epidemics in SSA: predominantly urban (the "type" found in Botswana) and predominantly rural (the "type" found in Lesotho). Both epidemics are among the most severe worldwide. We will maximize the significance of our work by collaborating with two NGOs (ACHAP and PIH) that are responsible for designing and implementing HIV treatment and prevention programs in Botswana and Lesotho, respectively. The project links two approaches new to HIV modeling: (1) Geographic analysis By estimating the number, and geographic location, of infected and at-risk individuals (which we will accomplish by analyzing detailed georeferenced HIV data available from both countries), we can target treatment programs and interventions to the communities in geographic regions where they are most needed; (2) Optimization We will use the georeferenced data from both countries to develop mathematical models, which we will analyze using optimization techniques to determine how limited resources can be utilized most effectively. The optimization approaches that we employ will be based on the GeO Hypothesis and tailored to each aim: in Aim 1 to minimize the probability of interruptions in the treatment supply chain; in Aim 2 to maximize (given resource constraints) the impact of two types of interventions (Voluntary Male Medical Circumcision and Treatment as Prevention (TasP)) on preventing HIV infections; in Aim 3 to minimize the resources needed to use TasP-based interventions to eliminate HIV. An interdisciplinary collaborative team will conduct the research, including experts from the countries that are its focus. As we will use data from Botswana and Lesotho to parameterize our models, our results will be particularly informative for their policymakers, enabling evidence-based decision-making. Notably, the methods and models we will develop will be sufficiently general to be useful for the 23 other SSA countries that have generalized epidemics and georeferenced HIV data from Demographic Health Surveys. The questions we address (e.g., how to minimize stock-outs) are relevant for all 25 countries.