Community level variability in Bronx COVID-19 hospitalizations associated with differing viral variant adaptive strategies during the second year of the pandemic

The Bronx, New York, exhibited unique peaks in the number of COVID-19 cases and hospitalizations compared to national trends. To determine which features of the SARS-CoV-2 virus might underpin this local disease epidemiology, we conducted a comprehensive analysis of the genomic epidemiology of the four dominant strains of SARS-CoV-2 (Alpha, Iota, Delta and Omicron) responsible for COVID-19 cases in the Bronx between March 2020 and January 2023. Genomic analysis revealed similar viral fitness for Alpha and Iota variants in the Bronx compared to nationwide data. However, Delta and Omicron variants had increased fitness within the borough. While the transmission dynamics of most variants in the Bronx corresponded with mutational fitness-based predictions of transmissibility, the Delta variant presented as an exception. Epidemiological modeling confirms Delta’s advantages of higher transmissibility, and suggested pre-existing immunity within the community counteracted Delta virulence, contributing to unexpectedly low Bronx hospitalizations compared to preceding strains. There were few novel T-cell epitope mutations in Delta compared to Iota which suggests Delta had fewer immune escape mechanisms to subvert pre-existing immunity within the Bronx. The combination of epidemiological models and quantifying amino acid changes in T-cell and antibody epitopes also revealed an evolutionary trade-off between Alpha’s higher transmissibility and Iota’s immune evasion, potentially explaining why the Bronx Iota variant remained dominant despite the introduction of the nationwide dominant Alpha variant. Together, our study demonstrates that localized analyses and integration of orthogonal community-level datasets can provide key insights into the mechanisms of transmission and immunity patterns associated with regional COVID-19 incidence and disease severity that may be missed when analyzing broader datasets.