# Next-generation algorithms using multiple biomarkers for precise estimation of HIV infection duration and population level incidence

> **NIH NIH R01** · TRIAD NATIONAL SECURITY, LLC · 2022 · $704,381

## Abstract

SUMMARY
Given the central role of HIV incidence estimation in both surveillance and evaluation of HIV
prevention programs, it is essential to have reliable, inexpensive, and usable methods for
quickly estimating incidence in near-real time. This proposal aims to advance that goal by
capitalizing on our recent developments in using a spectrum of biomarker data and HIV
surveillance data to compute the complete posterior probability distribution of the time since
infection for recently diagnosed persons. We posit that such estimates contain not only more
accurate information about time of infection than standard binary classification (recent/long-
term), but that it also gives realistic confidence bounds on HIV incidence estimates as it
appropriately takes biomarker measurements and patient variation into account. We further
posit that an accurate HIV incidence estimate must take three components into account: 1) The
multi-assay algorithm (MAA)-adjusted time of infection (rather than the date of diagnosis or
simple recent/non-recent binary classification); 2) The number of undiagnosed persons living
with HIV (PLHIV) at a certain time (which may be diagnosed later); and 3) The number of HIV
positive individuals that move into the study population. Specifically, we will 1) Develop
laboratory protocols and algorithms for measuring and modeling individual biomarkers for
probabilistic estimation of time of HIV infection; 2) Combine biomarkers into a generalized MAA
for improved estimation of time of HIV infection and HIV incidence; and 3) Apply new MAA to
determine the duration of infection and estimate HIV incidence in different human populations.
Using large data, in total >13,000 patients with up to 7 different biomarkers determined, from
Sweden, USA, South Africa and Zimbabwe, we will establish longitudinal and single-time point
training data, as well as validation data, to develop validated, publicly available, methods to
estimate: 1) the full distribution of time since infection for individual PLHIV; and 2) population-
level HIV incidence based on either study-based samples or existing surveillance systems, and
changes thereof over time, using new biomarkers, algorithms and models.

## Key facts

- **NIH application ID:** 10399653
- **Project number:** 5R01AI152897-02
- **Recipient organization:** TRIAD NATIONAL SECURITY, LLC
- **Principal Investigator:** Thomas K. Leitner
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $704,381
- **Award type:** 5
- **Project period:** 2021-05-03 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10399653

## Citation

> US National Institutes of Health, RePORTER application 10399653, Next-generation algorithms using multiple biomarkers for precise estimation of HIV infection duration and population level incidence (5R01AI152897-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10399653. Licensed CC0.

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