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

点击翻译按钮获取中文摘要

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.