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.

Public Health Relevance

Despite the advent of combined antiretroviral therapy (cART), the ongoing HIV epidemic still defies prevention strategies designed to reduce significantly incidence worldwide. Integrated statewide surveillance programs have been generating and linking big amasses of molecular, demographic, and clinical data. Advanced artificial intelligence algorithms coupled with molecular epidemiology tools can exploit these complex data to generate accurate predictions on epidemic spread that can be used to identify actionable sociodemographic factors guiding precision public health intervention.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Mckaig, Rosemary G
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University of Florida
Schools of Medicine
United States
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