# Targeted Learning using adaptive designs for HIV Epidemic control in East Africa

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $566,402

## Abstract

PROJECT SUMMARY
Scientific approaches to detect and respond to gaps in the public health response to HIV face a broad
challenge with profound implications: after more than a decade of intensive service scale-up, the magnitude
and reasons for remaining gaps in the treatment and prevention cascades differ markedly across people,
communities, organizations, and geographies. As a result, strategies to control the epidemic must
efficiently identify people and places most in need of intensified HIV testing, prevention, and treatment
support, and deliver to each a timely and effective intervention. This poses a distinct scientific task: methods
are required to discover and leverage continually evolving information to optimize “local” estimates of how
best to allocate limited resources for both measurement and intervention. To meet this need, we propose
a set of theoretical and applied aims that advance the use of adaptive designs– i.e. repeated “learn-and-
apply” cycles– as a strategic approach for implementation science in heterogenous environments. Our
approach uses four steps. First, we will extend existing statistical theory to allow machine learning to direct
ongoing adaptation in who to measure and how to intervene, thereby creating approaches that maximally
leverage the increasingly rich program data available in the HIV epidemic response. Second, we will use
existing data from several unique field studies to create “virtual laboratories” for method validation and
evaluation: 1) a sampling study to assess mortality on treatment in 64 facilities and 165,000 patients in
Zambia; 2) an 1800-person individually randomized trial to optimize retention in HIV treatment in Kenya;
and, 3) a cluster randomized trial of HIV treatment as prevention in 32 communities and 150,000 adults in
Kenya and Uganda. Third, we will use these data to compare the methods developed against benchmarks
provided by standard approaches (e.g. fixed sampling or balanced randomization). Fourth, we will develop
accessible software for several general classes of problems in the HIV epidemic response and
implementation science more broadly. Aim 1 seeks to develop and evaluate adaptive sampling designs
that target data gathering based on past information to better detect locations and subpopulations with
elevated risk. Aim 2 seeks to develop and evaluate adaptive designs that continuously target intervention
assignment to learn how best to distribute interventions with heterogeneous effects. Aim 3 seeks to develop
and evaluate adaptive strategies that use past data to target combined data gathering and public health
interventions to simultaneously discover regions of elevated risk and guide intervention decisions. Overall
these methods offer the promise of detecting gaps in the HIV response more quickly, with fewer resources,
and with fewer “missed signals”, and of intervening more effectively within a context of constrained
resources by assigning interventions better tailored to indi...

## Key facts

- **NIH application ID:** 10236261
- **Project number:** 5R01AI074345-11
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Maya Liv Petersen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $566,402
- **Award type:** 5
- **Project period:** 2007-07-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10236261

## Citation

> US National Institutes of Health, RePORTER application 10236261, Targeted Learning using adaptive designs for HIV Epidemic control in East Africa (5R01AI074345-11). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10236261. Licensed CC0.

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