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: 10186693
• 负责人: Breschine Cummins
• 金额: $ 54.95万
• 依托单位: MONTANA STATE UNIVERSITY - BOZEMAN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186693
• 关键词: AIDS prevention; AIDS/HIV problem; Address; Africa South of the Sahara; Behavioral; Biological; 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; Hispanics; Incidence; Individual; Intervention; Laboratories; Lead; Literature; Measures; Methods; Modeling; Network-based; Patient Self-Report; Patients; 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 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.

项目总结/摘要 全球艾滋病毒流行病继续演变，一些人群的发病率不断攀升，包括男性， 在美国，男男性接触者（MSM）的比例在下降，而在撒哈拉以南非洲的大部分地区。虽然若干 虽然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。 在特定的人群和背景下，采取各种干预措施是遏制艾滋病毒流行的最佳办法。的 本研究的目的是开发优化设计所需的建模和仿真工具， 在特定亚群中进行的网络信息艾滋病预防和治疗干预的随机对照试验 有感染艾滋病毒风险的人群。基于代理的流行病建模提供了一个实验室， 在实施昂贵的干预措施之前，先比较综合预防方案。的关系网 对疾病的传播和干预措施的有效性有重要影响;流行病模型需要 说明该网络的特征。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），但受到报告偏见的影响。它还 包括无法从个人报告中衡量的特征，例如， 合作伙伴一起合作。后一个特征要么不包括在流行病模型中，要么包括但不包括 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）一个模型， 整合有关当地艾滋病毒流行病的许多数据来源，使我们能够衡量 疾病传播的接触网络，以及2）模拟试验的新多层网络模型 在试验设计中使用网络数据的艾滋病预防方法。所有工具都将公开 可通过EpiModel流行病建模包套件获得，并使用来自HIV的数据进行演示 在圣地亚哥（原发性感染资源联盟，或PIRC）和亚特兰大（参与MENT和 Elementcohort）。这个项目的优势包括我们团队在流行病建模方面的丰富经验 和网络的统计方法，以及PIRC、InvolveMENT和EleMENT提供的丰富数据 同伙关于公共卫生影响，我们将开发并广泛提供的工具允许量身定制 对特定亚群体产生最大影响的干预措施， 在高危人群中预防艾滋病毒。