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

University Of Florida, Gainesville FL

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Abstract

Modified Project Summary/Abstract Section The United States (US) is the most affected country worldwide by the ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV 2) pandemic. By the beginning of 2021, incidence of severe coronavirus disease 2019 (COVID 19) cases, hospitalization burden, and deaths appeared to be decreasing. Unfortunately, the emergence of new, highly transmissible variants of concern (VOCs), such as the Delta variant that rapidly became dominant in the US, caused a renewed epidemic surge in mid 2021 and continues to pose challenges for disease control. Although evidence on increased mortality and worse clinical outcomes among people with HIV (PWH) infected with SARS CoV 2 remains mixed, several studies suggest that PWH may have a higher likelihood of severe disease or death compared with individuals without immune dysfunction. While most individuals clear SARS CoV 2 infection within 2–4 weeks, persistent infection in immunosuppressed individuals has been associated with intra host emergence of multi mutational viral variants, including mutations at sites linked to immune evasion. The overarching goal of this project is to investigate SARS CoV 2 intra host genomic evolution in the context of HIV infection by developing a phylodynamic and artificial intelligence framework (PhAI CoV) to assess the emergence and likelihood of SARS CoV 2 variants of concern in immunocompromised PWH. We hypothesize that SARS CoV 2 infection in PWH can result in enhanced viral evolution that can be efficiently characterized using phylodynamic methods and predicted using artificial intelligence algorithms.