# Modeling the Role of PrEP in Getting to Zero

> **NIH NIH R56** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $758,788

## Abstract

PROJECT SUMMARY/ABSTRACT
Progress in HIV prevention in the United States (US) has stalled, according to reports from the Centers for
Disease Control and Prevention (CDC) (1). Most recently, the Department of Health and Human Services has
made Ending the HIV Epidemic (EHE) a national priority (6), with intermediate goals of a 75% reduction within
five years and a 90% reduction in 10 years (7). GTZ programs rely on the concept of combination HIV
prevention, using evidence-based methods that have been tailored to suit local needs (8-11). Among each of
these programs is an emphasis on pre-exposure prophylaxis (PrEP), which is a versatile tool able to prevent
acquisition of HIV infection within diverse HIV-risk communities. Despite the success of PrEP in efficacy trial
settings, uptake has been slow in the US and highly variable: coverage among those with indications for PrEP
is estimated to range from 5-41% (median 18%), among US states (20).
Agent-based stochastic modeling is highly equipped to investigate complex epidemiologic questions, such as
the effects of the PrEP continuum in diverse settings, populations, and as part of combination HIV prevention.
The HIV Calibrated Dynamic Model (HIV-CDM), simulates HIV testing, transmission, treatment, and
prevention among a wide range of epidemic settings and is able to address the crucial questions facing PrEP
implementation in the US (40-43). Using the HIV-CDM, we propose to address the following aims:
Specific Aim 1: To expand and calibrate the current HIV-CDM to capture the epidemic dynamics, HIV risk
behavior, network mixing, and access to HIV prevention modalities within the most prominent GTZ programs
and priority settings throughout the US.
Specific Aim 2: To simulate the PrEP continuum in each of the specific settings noted below, including PrEP
eligibility within key populations, access, retention, and adherence. To inform these simulations, and generate
estimates for PrEP utilization up to 10 years into the future, we will integrate empirical data for each step of
the continuum. This will include a focus on both the development and testing of diverse PrEP eligibility
measures, including electronic health record-based algorithms, clinical checklists, and CDC guidelines.
Specific Aim 3: To evaluate the potential to reduce HIV incidence by 75% in five years, and 90% by 2030,
through targeted PrEP expansion, within the context of existing combination prevention packages in settings
with a history of HIV prevention successes (e.g., Boston and San Francisco), settings that have struggled in
their GTZ efforts (Miami, Atlanta), and rural settings that are priority areas for the new EHE initiative. This
approach will include network-based analyses that will investigate the most efficient methods of PrEP delivery
within heterogenous epidemics.

## Key facts

- **NIH application ID:** 10235298
- **Project number:** 1R56AI149736-01A1
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Daniel Escudero
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $758,788
- **Award type:** 1
- **Project period:** 2020-09-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10235298

## Citation

> US National Institutes of Health, RePORTER application 10235298, Modeling the Role of PrEP in Getting to Zero (1R56AI149736-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10235298. Licensed CC0.

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