# Automation and Evaluation of Real-Time Transmission Network-Based HIV Prevention Services in New York City

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $716,440

## Abstract

ABSTRACT
Introduction. The HIV epidemic in the United States persists. Public health departments across the country
are exploring how to better use viral genetic sequence data to reconstruct transmission networks and guide
prevention efforts. In 2017, the Centers for Disease Control and Prevention (CDC) and University of California,
San Diego (UCSD) unveiled a nationwide initiative, Secure HIV-TRACE, to allow public health departments to
construct genetic transmission networks in near-real time. Concurrently, the New York City (NYC) Public
Health Labs (PHL) began sequencing viral genotypes themselves (i.e., point-of-diagnosis genotyping) for
people diagnosed by select providers, in order to reduce the time between diagnosis and sequence analysis.
The goal of this these programs are to direct standard pubic health resources in near real-time to cases,
clusters, and locations of greatest concern (i.e., greatest potential for future cases). The underlying assumption
of this strategy is that prioritization based on cluster growth dynamics inferred from genetic sequence data will
disproportionately reduce future disease burden. Therefore, the important question is not whether real-time
time targeting can prevent incident infections; rather, the important question is: Can real-time targeting prevent
more incident infections than current, network-agnostic public health strategies? Methods. Here, we propose
an observational study designed to evaluate the impact of these real-time strategies, which are currently
implemented in NYC. We will automate Secure HIV-TRACE to construct genetic transmission clusters in real-
time to guide standard public health services in NYC. Using these clusters, we will identify Index Cases (i.e.,
HIV-infected people diagnosed by select provides who receive point-of-diagnosis genotyping by PHL) and then
to identify Priority Partners (i.e., in-care viremic or out-of-care HIV-infected partners, genetically linked to the
Index Case). Standard public health services will then be offered to these Priority Partners [e.g., return to care,
antiretroviral therapy (ART) adherence support, partner elicitation services]. Using data routinely reported to
NYC Department of Health and Mental Hygiene, we will assess whether these services provided to Priority
Partners lead to (i) increased rates of viral suppression in the Priority Partners, (ii) less than predicted incident
HIV cases in their transmission cluster, (iii) an interaction with past cluster growth to result in even less than
predicted incident HIV cases in their transmission cluster, and (iv) an interaction with past cluster growth to
identify more than predicted prevalent, undiagnosed HIV cases in their transmission cluster. Conclusions. If
we observe an interaction between past cluster growth and public health outcomes resulting from standard
prevention services, this will suggest that real-time network based prevention should become standard of care
in NYC and beyond. Through ...

## Key facts

- **NIH application ID:** 9850925
- **Project number:** 5R01AI135992-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Joel Okrent Wertheim
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $716,440
- **Award type:** 5
- **Project period:** 2018-01-24 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850925

## Citation

> US National Institutes of Health, RePORTER application 9850925, Automation and Evaluation of Real-Time Transmission Network-Based HIV Prevention Services in New York City (5R01AI135992-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9850925. Licensed CC0.

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