# A Phylodynamic Artificial Intelligence framework to predict evolution of SARS-CoV-2 variants of concern in Immunocompromised persons with HIV (PhAI-CoV)

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $738,604

## Abstract

Summary
The United States (US) is the most affected country worldwide by the ongoing Severe Acute Respiratory
Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. The availability of effective vaccines had initially slowed
down new infections, reducing incidence of severe coronavirus disease 2019 (COVID-19) cases, hospitalization
burden, and deaths. Unfortunately, vaccine hesitancy, and the emergence of new, highly transmissible variants
of concern (VOCs), such as the Delta variant that has rapidly become the dominant one in the US among both
non-vaccinated and vaccine breakthrough cases, have caused a new dramatic epidemic surge in July-August
2021, and are likely to be an ongoing problem hindering epidemic eradication efforts. Although data on increased
mortality and worse clinical outcome in people with HIV (PWH) with COVID-19 is somewhat equivocal, recent
surveys indicate that PWH has a higher likelihood of severe disease or death than patients without immune
dysfunction. Moreover, while most people effectively clear SARS-CoV-2 in 2-4 weeks, several reports of infection
in immunosuppressed individuals have shown intra-host emergence of multi-mutational variants, some at sites
linked to immune evasion, especially in case of persistent infection. The overarching goal of the proposed project
is to investigate SARS-CoV-2 genomes intra-host evolution in the context of HIV infection by developing a
phylodynamic and artificial Intelligence framework to assess the emergence and likelihood of SARS-CoV-2 VOC
(PhAI-CoV) in immunocompromised PWH. The hypothesis is that SARS-CoV-2 infection in PWH can result in
enhanced evolution of viral variants that can efficiently be tracked by phylodynamic analysis and predicted to be
VOCs by artificial intelligence algorithms. To test such a hypothesis, we developed three specific aims that will
investigate three complementary, albeit independent, issues. We will use a well-characterized cohort of PWH
and rigorously collected longitudinal data and samples from patients with SARS-CoV-2 co-infection in Miami,
Florida, one of the cities with the highest HIV and SARS-CoV-2 infection burden in the US. In Specific Aim 1 we
will recruit and retain n=120 PWH with acute SARS-CoV-2 infection, as well as n=120 matching controls with
acute SARS-CoV2 infection but without HIV, and study how COVID-19 disease severity differs by HIV status,
depending on SARS-CoV-2 vaccination history and infecting variant. In Specific Aim 2, we will Investigate intra-
host SARS-CoV-2 evolution throughout the duration of infection to assess the likelihood of SARS-CoV-2 infection
in PWH to result in sustained intra-host evolution leading to the emergence of novel viral variants. In specific
Aim 3, we will develop an artificial intelligence algorithm that can predict the likelihood of new variants to be
VOCs. Understanding the evolutionary scenarios of SARS-CoV-2 variants emergence within HIV infection and
evaluating the probability for increased strain inf...

## Key facts

- **NIH application ID:** 10857191
- **Project number:** 5R01AI170187-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** MARIA LUISA ALCAIDE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $738,604
- **Award type:** 5
- **Project period:** 2022-07-12 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10857191

## Citation

> US National Institutes of Health, RePORTER application 10857191, A Phylodynamic Artificial Intelligence framework to predict evolution of SARS-CoV-2 variants of concern in Immunocompromised persons with HIV (PhAI-CoV) (5R01AI170187-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10857191. Licensed CC0.

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