HIV - Emergence of Drug Resistance

描述（由申请人提供）：这一更新申请将结合对特殊数据集的统计分析、数学模型和优化技术，在资源丰富和资源有限的国家设计流行病控制策略。此外，还将设计优化治疗方案扩展的策略。最近为预防艾滋病毒传播而开发的基于抗逆转录病毒的工具有可能改变艾滋病毒大流行的进程。与此同时，抗逆转录病毒治疗在非洲的推广正在急剧扩大。未确诊和感染艾滋病毒的人数（即“隐性流行病”的规模）的不确定性是治疗方案和干预措施取得成功的关键障碍。我们将通过分析丹麦（哥本哈根）、博茨瓦纳和莱索托合作者提供的数据，估计这些国家“隐性流行病”的规模。这些估计将构成我们分析的基础。在Aim 1A中，我们评估了丹麦男男性行为人群的治疗水平的提高是否与HIV发病率的降低相关；在Aim 1B中，我们使用建模来确定需要满足的条件，以“治疗作为预防”为基础的干预措施，以消除该社区的艾滋病毒。在目标2中，我们使用地理参考数据为博茨瓦纳和莱索托构建国家级预测艾滋病毒流行率图，并估计疾病负担；这两个国家是世界上艾滋病毒流行最严重的国家之一。我们使用这些地图量化每个国家获得治疗的可及性，并确定如何优化治疗方案的效率（例如，尽量减少治疗供应链的中断）。在目标3中，使用目标2的结果，我们构建传播模型并确定如何优化干预设计以减少艾滋病毒传播；特别是在资源有限的情况下，确定如何最大限度地减少发病率。

DESCRIPTION (provided by applicant): This renewal application will combine statistical analyses of extraordinary datasets with mathematical models and optimization techniques to design epidemic control strategies in both resource-rich and resource- constrained countries. In addition, strategies for optimizing the expansion of treatment programs will be designed. The recent development of antiretroviral-based tools to prevent HIV transmission could potentially alter the course of the HIV pandemic. At the same time, the rollout of ARV treatment in Africa is expanding dramatically. Uncertainty about the number of individuals who are undiagnosed and infected with HIV (i.e., the size of the "hidden epidemic") is a critical obstacle to the success o both treatment programs and interventions. We will estimate the size of the "hidden epidemic" in Denmark (in Copenhagen), Botswana, and Lesotho, through analyses of data provided by collaborators in these countries. These estimates will form the foundation for our analyses. In Aim 1A we evaluate, for the Danish community of men who have sex with men, whether increasing levels of treatment have been correlated with decreasing HIV incidence; in Aim 1B we use modeling to determine the conditions that need to be met for "treatment as prevention"-based interventions to eliminate HIV in that community. In Aim 2 we use georeferenced data to construct country-level predictive HIV prevalence maps for, and estimate the burden of disease in, Botswana and Lesotho; two countries with among the most severe HIV epidemics worldwide. We use these maps to quantify the accessibility to treatment across each country and to determine how to optimize the efficiency of treatment programs (e.g., minimize interruptions in the treatment supply chain). In Aim 3, using the results from Aim 2, we construct transmission models and determine how to optimize the design of interventions to reduce HIV transmission; specifically to determine how best to minimize incidence, under resource constraints.