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.

项目概要/摘要 全球艾滋病毒流行继续发展，某些人群的发病率不断上升，其中包括男性 在美国，男男性行为（MSM）的比例在撒哈拉以南非洲的大部分地区有所下降。虽然有几个 已经找到了预防艾滋病毒传播的有效方法，但我们仍然缺乏了解这些方法是如何实现的 在特定人群和环境下，最好采取不同的干预措施来遏制艾滋病毒的流行。这 本研究的目的是开发优化设计所需的建模和仿真工具 针对特定亚组的网络知情艾滋病毒预防和治疗干预措施的随机对照试验 有感染艾滋病毒风险的人群。基于代理的流行病模型提供了一个实验室，可以在其中进行测试和 在实施昂贵的干预措施之前比较组合预防方案。联系网络 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 考虑该网络的特征。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），但可能存在报告偏差。它还 包括无法从个人报告中衡量的特征，例如具有许多特征的人的倾向 合作伙伴共同合作。后面的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 整合有关当地艾滋病毒流行的许多数据来源，使我们能够衡量艾滋病毒流行的特征 疾病传播的接触网络，以及 2）模拟试验的新多层网络模型 在试验设计中利用网络数据来预防艾滋病毒。所有工具都将公开 可通过 EpiModel 流行病建模包套件获得，并使用 HIV 数据进行演示 圣地亚哥（初级感染资源联盟，PIRC）和亚特兰大（InvolveMENt 和 元素队列）。该项目的优势包括我们团队在流行病建模方面拥有丰富的经验 和网络统计方法，以及 PIRC、InvolveMENt 和 EleMENt 提供的丰富数据 队列。关于公共卫生影响，我们将开发并广泛提供的工具允许定制 的干预措施，以最大限度地影响特定亚人群，从而解决现有的差距 在高危人群中预防艾滋病毒。