# Reconstructing HIV Epidemics from HIV Phylogenetics

> **NIH NIH R01** · TRIAD NATIONAL SECURITY, LLC · 2022 · $917,615

## Abstract

Project Summary
HIV continues to spread, episodically, among minority groups, and mostly from people unaware of their
infection. To more efficiently locate undiagnosed people living with HIV for treatment, as well as to monitor
prevention efforts, better epidemiological techniques are needed. Our project brings together a team of
experienced researchers from clinical, molecular biology, epidemiological, mathematical, and evolutionary
fields. We will develop innovative epidemiological methods by combining evolutionary theory, multi-scale
dynamic modeling, artificial intelligence, and large-scale clinical and sequence data. In this renewal, we will
expand on our previous work on how HIV within-host evolutionary processes interact with epidemiological
dynamics. Having quantified the link between transmission history and the resulting HIV phylogeny among
hosts, we conceptualize the relationship between the evolution and epidemiology of HIV into three levels:
within-host, at transmission, and on the population epidemic level. Because essential processes of HIV biology
and evolution have been largely ignored when modeling the epidemic level, in aim 1 we examine within-host
processes that affect diversification. We will include recombination, selection, and latency in a new coalescent
within-host model to evaluate the impact on the epidemiological level. We will also quantify potential within-
host multi-directional selection pressures. In aim 2, we focus on mechanisms that occur around the time of
transmission. We will develop a new maximum likelihood method based on a forward-time probabilistic model
of transmission that improves the inference of transmission direction and time of transmission among multiple
hosts, and develop a transmission heterogeneity detection method to both assess overall possible
transmission heterogeneity among infected persons, as well as to detect where in a phylogeny super-
spreading may have occurred. In aim 3, we will develop machine learning methods to handle very large data
sets (103-106 patients), and use additional clinical and demographic data to augment phylogenies in order to
reconstruct the underlying transmission history. All three aims will involve advancements aimed at developing
and improving methods for the next generation of phylodynamic applications.

## Key facts

- **NIH application ID:** 10462647
- **Project number:** 5R01AI087520-12
- **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:** $917,615
- **Award type:** 5
- **Project period:** 2010-06-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10462647

## Citation

> US National Institutes of Health, RePORTER application 10462647, Reconstructing HIV Epidemics from HIV Phylogenetics (5R01AI087520-12). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10462647. Licensed CC0.

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