Marco Salemi
University of Florida, FL·Public Health & Epidemiology
Viruses mutate as they spread, and some changes make them more contagious or better at escaping immunity. Can scientists predict where an epidemic is heading, or which risky variants will emerge, before it happens?
- Fields
Public Health & Epidemiology
Microbiology & Immunology
Computer Science
- Topics
phylodynamics
phylogenetics
viral evolution
- Grants
- 2 known grants, through Jul 2031
What they work on
- Test whether SARS-CoV-2 evolves faster within immunocompromised people with HIV by using phylodynamic analysis, which reconstructs viral family trees from genetic sequences, to track variants that arise inside one person.
- Develop the PhAI-CoV framework, which combines phylodynamics with artificial intelligence algorithms, to predict how likely SARS-CoV-2 variants of concern are to emerge in people with HIV.
- Build an agentic AI system with NVIDIA that integrates the team's earlier phylodynamic and machine learning tools to track HIV transmission clusters and give public health guidance.
- Develop a conversational, causal AI model that personalizes HIV care, and set governance and safety rules for using agentic AI in clinical and public health settings.
More about the research
Two projects address this by combining phylodynamics with artificial intelligence (AI). Phylodynamics reads viral genetic sequences as a family tree to reconstruct how a virus evolves and spreads. One project looks at SARS-CoV-2, the coronavirus that causes COVID-19, in people with HIV. The other forecasts HIV transmission and aims to guide prevention and care.
Why it matters
These tools could help health departments spot dangerous SARS-CoV-2 variants arising in immunocompromised people with HIV before they spread widely, strengthening genomic surveillance and preparedness. For HIV, AI tools that anticipate where and in which patient groups transmission will accelerate could guide prevention efforts. They could also help clinicians personalize care to improve outcomes for people living with HIV.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework
NIH · May 2020 – Jul 2031
A Phylodynamic Artificial Intelligence framework to predict evolution of SARS-CoV-2 variants of concern in Immunocompromised persons with HIV (PhAI-CoV)
NIH · Jul 2022 – Jun 2027
Closest labs
Fuqing Wu
University of Texas Health Science Center at Houston, TX·Public Health & Epidemiology
Build models that connect how virus levels change inside one infected person with how the virus passes between people, so that SARS-CoV-2 levels in wastewater can be read as measures of community transmission.
Ying Fang
University of Illinois Urbana-Champaign, IL·Microbiology & Immunology
Generate and characterize SARS-CoV-2-specific antibody reagents and diagnostic standards for COVID-19 research and diagnostics.
Bryan Lau
Johns Hopkins University, MD·Public Health & Epidemiology
Assess how HIV status relates to COVID-19 hospitalization and post-acute sequelae of COVID-19 (lasting after-effects), using Medicaid claims from 27 states and Washington, DC.
The school
University of Florida: 65 labs on PI Pages, $1.3B a year on research, 795 research doctorates a year.
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