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变化，其中生物医学干预已上升到研究和实践的前沿。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)以及扩大接触前预防(PrEP)的有针对性的机会的努力重新注入了活力。虽然人们对结束艾滋病毒的前景充满热情，但关键是要考虑我们的干预努力将如何与他们所处的复杂人口和生物系统相互作用。我们的项目为一个独特的跨学科项目汇集了经验丰富的社会和生物医学科学家：建立一种综合的方法论 研究艾滋病毒种群动态的多层次生物学和行为基础的框架。我们将结合新开发的随机模型来表示人类伙伴关系网络的动态，以及表示宿主内部和宿主之间的病毒进化动态的模型。其结果将是一个全面的框架，用于研究不同传播网络条件下生物医学干预对艾滋病毒进化和耐药性的影响。我们的具体目标是：1)创建一个新的公共软件包，其中集成了病毒传播网络以及宿主内和宿主间病毒动力学和进化的模型；2)确定传播网络结构和生物医学干预措施如何影响艾滋病毒病毒载量的人群水平测量的趋势，包括设定的病毒载量(AS 作为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)；以及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.