# Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2021 · $705,136

## Abstract

SUMMARY
Despite the advent of combined antiretroviral therapy, the ongoing HIV epidemic still defies prevention and
intervention strategies designed to reduce significantly both prevalence and incidence worldwide. In order to
achieve the 2020 UNAIDS 90-90-90 goal (90% of people living with HIV diagnosed, 90% of people diagnosed
to be on sustained antiretroviral treatment, and 90% of people on treatment to maintain viral suppression), it is
necessary to develop innovative tools that can be used for predicting the growth and trajectory of localized
sub-epidemics driven by specific transmission clusters. Phylodynamic analysis has extensively been used in
the HIV field to track the origin and reconstruct the virus demographic history both at local, regional and global
level. However, such studies have been so far only retrospective, with little or no power to make predictions
about future epidemic trends. The overarching goal of the prosed project is to develop an innovative
computational framework coupling phylodynamic inference and behavioral network data with artificial
intelligence algorithms capable of predicting HIV transmission clusters future trajectory, and informing on key
determinants of new infections. We propose to achieve this goal by carrying out three specific aims: 1. Develop
a phylodynamic-based PRIDE module to forecast HIV infection hotspots [the infected]; 2. Develop a behavioral
network-based PRIDE module for risk of HIV infection [the uninfected], and 3. Carry out focus groups for
deploying the new PRIDE forecasting technology into public health, and implement prevention through the
peer change agent model. In particular, through a close partnership with the Florida Department of Health
(FLDoH), we will analyze existing databases that the FLDoH has assembled over the past twelve years
including extensive HIV molecular sequence, clinical and behavioral network data. Florida had an HIV case
rate of 24.0 per 100,000 people in 2016, and it is currently the third state in the USA in terms of yearly
incidence. Our partnership with the FLDoH will ensure that the results of the proposed research will be used to
curtail the HIV epidemic by optimizing public health based surveillance programs, informing targeted
intervention strategies, and implementing more effective prevention measures.

## Key facts

- **NIH application ID:** 10155407
- **Project number:** 5R01AI145552-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Mattia Prosperi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $705,136
- **Award type:** 5
- **Project period:** 2020-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10155407

## Citation

> US National Institutes of Health, RePORTER application 10155407, Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework (5R01AI145552-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10155407. Licensed CC0.

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