Combining theoretical and empirical network approaches to HIV transmission

描述（由申请人提供）：广泛的实证研究表明，艾滋病毒和性传播感染的传播受到接触网络结构和行为风险的深刻影响。然而，从这些研究中收集到的不同见解尚未被整合到一个总体框架中，以了解性传播感染在异质人群中的地方性和流行性传播。有许多数学方法可以通过接触网络对疾病传播进行建模（例如，隔室模型;基于行为者的模型;网络模拟;指数随机图模型[也称为p* 模型]），但大多数简化假设与经验观察不一致。特别是，许多模型假设接触和行为模式在疫情期间保持不变。在这个探索性的项目中，我们提出：（1）通过对八项经验网络研究的数据进行广泛的统计分析，建立将人口结构和行为风险与STI传播动态联系起来的广泛假设;（2）使用明确考虑网络流动性对流行病学影响的强大的新数学模型来验证这些假设。我们的经验数据来自八个已完成的网络研究（六个在亚特兰大GA和科罗拉多斯普林斯CO和弗拉格斯塔夫AZ各一个），和一个正在进行的研究在亚特兰大，所有这些都使用相同的基本问卷。我们将收集和合并这些研究，我们估计这些研究中80%的变量将是一致的。我们将进行多变量分析，以确定哪些行为和人口因素对艾滋病毒/性传播疾病的传播影响最大。然后，使用数学模型，将网络结构，行为风险，和流行模式计算出我们的八项研究，我们将定量探讨这些影响因素的影响，各种实证网络研究设计的有效性，以及不同的病原体在同一网络中的差异传播。公共卫生相关性：实地研究令人信服地表明，许多因素同时影响艾滋病毒/性传播感染的传播，但这种直觉很难记录。通过观察八个不同的社区通过一个共同的透镜和适应最新一代的数学模型，以反映观察到的模式，我们的目标是构建一个理论框架，强大的网络结构，行为风险和流行病学的结果。

DESCRIPTION (provided by applicant): Extensive empirical research has shown that the transmission of HIV and STIs is profoundly influenced by contact network structure and behavioral risks. Yet the diverse insights gleaned from these studies have not yet been integrated into a general framework for understanding the endemic and epidemic spread of STIs through heterogeneous populations. There are a number of mathematical approaches to modeling disease transmission through contact networks (for example, compartment models; actor-based models; network simulations; and exponential random graph models [also know as p* models]), but most make simplifying assumptions that are inconsistent with empirical observations. In particular, many models assume that contact and behavioral patterns remain constant for the duration of an outbreak. In this exploratory project, we propose (1) to develop broad hypotheses linking population structure and behavioral risk to STI transmission dynamics through extensive statistical analyses of data from eight empirical network studies, and (2) to test these hypotheses using powerful new mathematical models that explicitly consider the epidemiological impact of network fluidity. Our empirical data derive from eight completed network studies (six in Atlanta GA and one each in Colorado Springs CO and Flagstaff AZ), and one ongoing study in Atlanta, all of which used the same basic questionnaire. We will assemble and amalgamate these studies, and we estimate that 80% of the variables in each of these studies will be congruent. We will conduct multivariable analyses to determine which behavioral and population factors most strongly influence the transmission of HIV/STIs. Then, using mathematical models that incorporate the network structures, behavioral risks, and prevalence patterns calculated from our eight studies, we will quantitatively explore the implications of these influential factors, the efficacy of various empirical network study designs, and the differential transmission of diverse pathogens in the same network. PUBLIC HEALTH RELEVANCE: Field studies convincingly suggest that many factors simultaneously influence HIV/STI transmission, but such intuition is difficult to document. By viewing eight diverse communities through a common lens and adapting the latest generation of mathematical models to reflect the observed patterns, we aim to construct a theoretical framework that robustly relates network structure, behavioral risks, and epidemiological outcomes.