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

用于优化 HIV 预防联合干预措施的网络信息临床试验设计的建模和模拟工具

• 批准号: 10622168
• 负责人: Ravi Goyal
• 金额: $ 61万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10622168
• 关键词: AIDS prevention; AIDS/HIV problem; Address; Africa South of the Sahara; Behavioral; Biological; Black race; Characteristics; Clinical; Clinical Trials; Code; Communities; Contact Tracing; Containment; Data; Data Sources; Development; Disease; Effectiveness of Interventions; Elements; Epidemic; Glean; HIV; HIV Infections; HIV prevention trial; Hispanic; Incidence; Individual; Intervention; Laboratories; Lead; Literature; Measures; Methods; Modeling; Network-based; Patient Self-Report; Patients; Persons; Phylogenetic Analysis; Population; Prevention; Prevention program; Primary Infection; Public Health; Randomized; Randomized Controlled Trials; Reporting; Resources; Risk; Risk Behaviors; Sexual Partners; Smallpox; Source; Statistical Methods; Structure; Surveys; Testing; United States; Virus; Work; base; cohort; design; experience; high risk; high risk population; improved; intervention cost; men who have sex with men; models and simulation; multiple data sources; network models; open source; prevent; preventive intervention; randomized controlled design; recruit; social; social contact; social networking website; successful intervention; therapy design; tool; transmission process; trial design; vaccination strategy; virtual laboratory; virus genetics

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.

项目摘要/摘要 全球艾滋病毒的流行继续发展，一些人群的发病率在包括男人中 在美国与男性发生性关系，并在撒哈拉以南非洲大部分地区下降。而几个 已经发现了预防艾滋病毒传播的有效方法，我们仍然缺乏对这些方式的了解 在给定的人群和环境中，可以最好地部署各种干预措施来减少艾滋病毒流行。这 这项研究的目的是开发建模和仿真工具，以优化设计 特定亚款的网络信息预防和治疗干预措施的随机对照试验 艾滋病毒感染风险的人群。基于代理的流行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，请先比较预防计划。联系网络 对疾病的传播和干预措施有重要影响；流行模型需要 说明该网络的功能。这包括可以轻松从个人自我测量的功能 报告（例如，性伴侣人数的分布），但要报告偏见。也是如此 包括无法根据个人报告可测量的功能，例如有很多人的趋势 合作伙伴一起合作。后一个特征要么不包括在流行模型中，要么包括 通过数据告知。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 结合许多有关局部HIV流行的数据来源，以使我们能够衡量 疾病传播的接触网络，以及2）一种新的多层网络模型，该模型模拟试验 在试验设计中使用网络数据的预防艾滋病毒预防。所有工具将公开制造 通过流行病建模包的EpiModel套件可用，并使用来自HIV的数据证明 圣地亚哥（主要感染资源联盟或PIRC）和亚特兰大的同伙（参与和 元素队列）。该项目的优势包括我们团队在流行模型中的丰富经验 网络的统计方法以及PIRC，参与和元素可获得的丰富数据 同伙。关于公共卫生影响，我们将开发的工具并提供广泛可用的许可证裁缝 干预措施以最大程度地影响特定的子人群，从而解决了剩余的差距 预防高危人群中的艾滋病毒。