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

> **NIH NIH R01** · MONTANA STATE UNIVERSITY - BOZEMAN · 2021 · $549,516

## Abstract

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.

## Key facts

- **NIH application ID:** 10186693
- **Project number:** 5R01AI147441-03
- **Recipient organization:** MONTANA STATE UNIVERSITY - BOZEMAN
- **Principal Investigator:** Breschine Cummins
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $549,516
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186693

## Citation

> US National Institutes of Health, RePORTER application 10186693, Modeling and simulation tools for optimizing design of network-informed clinical trials of combination HIV prevention interventions (5R01AI147441-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10186693. Licensed CC0.

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