HIV - Emergence of Drug Resistance

描述(由申请人提供)：此申请是对NOT-OD-10-033的回应，“NIH宣布通过NIH基本行为和社会科学机会网络(OppNet)为艾滋病毒/艾滋病相关研究提供恢复法案资金，用于竞争性修订申请(RO1、R03、R15、R21、R21/R33和R37)。家长奖的工作重点是将数学建模与数据集的统计分析相结合，以预测美国、欧洲和非洲艾滋病毒耐药毒株的演变。在修订中，我们的工作将扩大，为艾滋病毒感染制定详细的风险地图，这些地图在空间上是明确的，并基于博茨瓦纳的数据。我们的目标是(I)开发详细的基于数据的博茨瓦纳空间风险地图，该地图将确定感染风险最高的个人(基于性别和年龄)及其在全国各地的地理位置，以及(Ii)使用这些风险地图和基于个人的随机模拟模型来评估暴露前预防(PrEP)干预措施在博茨瓦纳减少传播和增加耐药性方面的潜在后果。此外，我们将使用最优控制理论来设计实施计划，使PrEP在预防感染方面的有效性最大化，并将PrEP在增加耐药性方面的影响降至最低。我们将预测PrEP干预措施在短期和长期增加二线治疗需求方面的潜在影响。我们建议进行的研究以我们过去二十年来对艾滋病毒和其他传染病建模的研究为基础，但通过以下方式使我们的艾滋病毒研究向新的方向发展：(1)根据性别、年龄和地理位置构建艾滋病毒的空间风险地图；(2)利用空间统计确定艾滋病毒感染风险的“热点”；(3)利用基于个人的模拟建模建立详细的基于数据的高分辨率模型；(4)为博茨瓦纳设计一个国别模型，侧重于艾滋病毒流行在全国各地的地理异质性；(5)利用优化技术，根据PrEP设计艾滋病毒干预措施。我们提议的新研究涉及与杀菌剂国际伙伴关系、疾病控制和预防中心(CDC)以及非洲全面艾滋病毒和艾滋病计划(ACHAP)的合作。ACHAP是一个非营利性组织，将博茨瓦纳政府与比尔和梅林达·盖茨基金会联系起来。我们的目标是利用从我们在博茨瓦纳的合作者那里获得的数据和从我们在疾控中心的合作者那里获得的关于PrEP的病毒学数据，设计基于PrEP在博茨瓦纳的干预措施的最佳推广计划。通过构建详细的空间显式模型和采用优化技术，我们将能够确定如何最大限度地提高PrEP干预措施的有效性，并确定应该开始推广的最佳地理位置。 公共卫生相关性：艾滋病毒大流行的控制是一个尚未解决的全球性问题。我们的总体目标是为博茨瓦纳开发详细的基于空间显式数据的艾滋病毒传播模型，并利用这些模型设计预防艾滋病毒感染的最佳干预措施。我们的研究结果将与资源受限国家的卫生政策制定者和政府以及其他科学家直接相关。

DESCRIPTION (provided by applicant): This application is in response to NOT-OD-10-033, "NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications (RO1, RO3, R15, R21, R21/R33, and R37) for HIV/AIDS-related Research through the NIH Basic Behavioral and Social Science Opportunity Network (OppNet)." The work of the parent award focuses on combining mathematical modeling with statistical analysis of datasets to predict the evolution of drug-resistant strains of HIV in the US, Europe, and Africa. In the revision our work will be extended by developing detailed risk maps for HIV infection that are spatially-explicit and based on data from Botswana. We aim to (i) develop detailed data-based spatial risk maps of Botswana which will identify individuals (based on gender and age) that are most at risk of infection and where they are geographically located throughout the country and (ii) use these risk maps and individual- based stochastic simulation modeling to evaluate the potential consequences of Pre-Exposure Prophylaxis (PrEP) interventions on decreasing transmission and increasing drug-resistance in Botswana. In addition we will use optimal control theory to design an implementation plan that will maximize the effectiveness of PrEP in preventing infections and minimize the effect of PrEP on increasing resistance. We will predict the potential impact of PrEP interventions on increasing the need for second-line therapies in both the short-term and long- term. The research we are proposing builds upon our past two decades of research on modeling HIV and other infectious diseases, but takes our HIV research in new directions by: (i) constructing spatial risk maps for HIV based on gender, age and geography, (ii) using spatial statistics to identify "hot-spots" of risk for acquiring HIV, (iii) building detailed data-based high resolution models using individual-based simulation modeling, (iv) designing a country-specific model for Botswana focusing on the geographic heterogeneity of the HIV epidemic throughout the country and (v) using optimization techniques to design HIV interventions based on PrEP. Our proposed new research involves collaborations with the International Partnership for Microbicides, the Center for Disease Control and Prevention (CDC) and the African Comprehensive HIV and AIDS Program (ACHAP). ACHAP is a non-profit organization that links the Government of Botswana with the Bill and Melinda Gates Foundation. We aim to design optimal rollout plans for interventions based on PrEP in Botswana using data obtained from our collaborators in Botswana and virologic data on PrEP obtained from our collaborators at the CDC. By constructing detailed spatially-explicit models and employing optimization techniques we will be able to identify how to maximize the effectiveness of PrEP interventions and to identify the optimal geographic locations where the rollout should begin. PUBLIC HEALTH RELEVANCE: The control of the HIV pandemic is a global problem that has yet to be solved. Our overall objective is to develop detailed spatially-explicit data-based HIV transmission models for Botswana and to use these models to design optimal interventions for preventing HIV infections. Our results will have direct relevance for health policy makers and Governments in resource-constrained countries, as well as for other scientists.