Integrated bio-social models for HIV epidemiology

描述（由申请人提供）：艾滋病毒预防领域发生了巨大的转变，其中生物医学干预措施已被提升到研究和实践的最前沿。这为扩大全球获得抗逆转录病毒治疗和支持治疗即预防（TasP）以及扩大有针对性的接触前预防（PrEP）的努力注入了新的活力。虽然人们对消灭艾滋病毒的前景充满热情，但必须考虑我们的干预努力将如何与它们所处的复杂的人口和生物系统相互作用。我们的项目汇集了经验丰富的社会和生物医学科学家的一个独特的跨学科项目：建立一个综合的方法论 该框架旨在研究艾滋病毒种群动态的多层次生物学和行为基础。我们将结合联合收割机新开发的随机模型，代表人类的伙伴关系网络的动态模型，代表病毒的进化动力学内和主机之间。其结果将是一个综合框架，用于研究生物医学干预措施对不同传播网络条件下艾滋病毒演变和耐药性的影响。我们的具体目标是：1）创建一个新的公共软件包，将病毒传播网络模型以及宿主内和宿主间病毒动态和演变模型整合起来; 2）确定传播网络结构和生物医学干预措施如何影响人口一级艾滋病毒载量测量的趋势，包括设定点病毒载量（如 艾滋病毒毒力的替代指标）和社区病毒载量（作为干预影响评估的潜在指标）; 3）调查传播网络结构和生物医学干预措施对艾滋病毒耐药性突变的出现和传播的相互作用。

DESCRIPTION (provided by applicant): There has been a huge shift in the HIV prevention field, in which biomedical interventions have been elevated to the forefront of research and practice. This has re-energized efforts to expand global access to antiretroviral therapy and support treatment as prevention (TasP), as well as to expand targeted access to pre-exposure prophylaxis (PrEP). While there is enthusiasm about the prospect of ending HIV, it is critical to consider how our intervention efforts will interact with the complex demographic and biological systems in which they are placed. Our project brings together experienced social and biomedical scientists for a unique interdisciplinary project: to build an integrated methodological framework to study the multi-level biological and behavioral foundations of the population dynamics of HIV. We will combine newly developed stochastic models for representing the dynamics of human partnership networks with models that represent viral evolutionary dynamics within and between hosts. The result will be a comprehensive framework for studying the impact of biomedical interventions on HIV evolution and drug resistance under different transmission network conditions. Our specific aims are: 1) To create a new public software package that integrates models for viral transmission networks and intra- and inter-host viral dynamics and evolution; 2) To determine how transmission network structure and biomedical interventions influence trends in population-level measures of HIV viral load, including set point viral load (as a proxy for HIV virulence) and community viral load (as a potential metric for intervention impact evaluation); and 3) To investigate the interaction of transmission network structure and biomedical interventions on the emergence and spread of HIV drug resistance mutations.