Modeling and simulation tools for optimizing design of network-informed clinical trials of combination HIV prevention interventions

项目摘要/摘要 全球艾滋病毒流行继续演变，一些人群的发病率攀升，包括 在美国与男性发生性行为(MSM)，在撒哈拉以南非洲的大部分地区呈下降趋势。虽然有几个 已经找到了预防艾滋病毒传播的有效方法，但我们仍然缺乏对这些方法的了解 在特定的人口和背景下，最好能采取各种干预措施来遏制艾滋病毒流行。这个 本研究的目标是开发所需的建模和仿真工具，以优化设计 网络知晓的艾滋病预防和治疗干预在特定亚群中的随机对照试验 面临艾滋病毒感染风险的人群。基于代理的流行病建模提供了一个实验室，可以在其中进行测试和 在实施昂贵的干预措施之前，比较联合预防计划。人脉网络 对疾病传播和干预措施的有效性有重要影响；流行病模型需要 考虑到该网络的功能。这包括可以很容易地从个人自我测量的特征 报告(例如性伴侣数量的分布)，但容易出现报告偏差。它还 包括无法从单个报告中衡量的功能，例如具有多个 合作伙伴一起合作。后一种特征要么不包含在流行病模型中，要么包含但不包含 由数据提供信息。通过这项研究，我们的团队将开发两个相关的建模工具：1)一个可以 结合有关当地艾滋病毒流行的许多来源的数据，使我们能够衡量 疾病传播的接触网络，以及2)模拟试验的新的多层网络模型 在试验设计中利用网络数据的艾滋病毒预防。所有工具都将公开制作 可通过EpiModel传染病建模包套件获得，并使用来自HIV的数据进行了演示 圣地亚哥(初级感染资源联盟，简称PIRC)和亚特兰大(参与和 元素队列)。这个项目的优势包括我们的团队在流行病建模方面的丰富经验 网络的统计方法，以及来自PIRC、参与度和元素的丰富数据 一群人。关于对公共健康的影响，我们将开发并广泛提供许可证定制的工具 干预措施，以最大限度地影响特定的亚人群，从而解决在以下方面的剩余差距 在高危人群中预防艾滋病毒。

PROJECT SUMMARY/ABSTRACT The global HIV epidemic continues to evolve, with incidence climbing in some populations, including men who have sex with men (MSM) in the United States, and declining in much of sub-Saharan Africa. While several effective methods to prevent HIV transmission have been found, we still lack understanding of how these varied interventions can best be deployed to curtail the HIV epidemic in a given population and context. The objective of this study is to develop modeling and simulation tools required to optimize the design of randomized controlled trials of network-informed HIV prevention and treatment interventions in specific sub- populations at risk for HIV infection. Agent-based epidemic modeling provides a laboratory in which to test and compare combination prevention programs before implementing costly interventions. The network of contacts has important effects on the spread of disease and the effectiveness of interventions; epidemic models need to account for features of that network. This includes features that can be readily measured from individual self- reports, (e.g., the distribution of the number of sexual partners), but that are subject to reporting biases. It also includes features that are not measurable from individual report, such as a tendency for people with many partners to partner together. The latter features are either not included in epidemic models or included but not informed by data. With this study, our team will develop two related modeling tools: 1) a model that can incorporate many sources of data about a local HIV epidemic to allow us to measure characteristics of the contact network over which the disease spreads, and 2) a new multi-layer network model that simulates trials of HIV prevention that make use of network data in the design of the trial. All tools will be made publicly available through the EpiModel suite of epidemic modeling packages, and demonstrated using data from HIV cohorts in San Diego (the Primary Infection Resource Consortium, or PIRC) and Atlanta (the InvolveMENt and EleMENt cohorts). Strengths of this project include our team's extensive experience with epidemic modeling and statistical methods for networks, and the rich data available from PIRC, InvolveMENt, and EleMENt cohorts. Regarding public health impact, the tools we will develop and make broadly available permit tailoring of interventions for maximum impact on specific sub-populations and thereby address remaining gaps in prevention of HIV in high-risk populations.