Community-level variability in Bronx COVID-19 hospitalizations associated with differing population immunity during the second year of the pandemic.

Abstract

The Bronx, New York, exhibited unique peaks in the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations compared to national trends. To determine which features of the severe acute respiratory syndrome coronavirus 2 (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 despite nationwide data showing higher cases of Alpha. 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 in Manhattan and Queens, but not the Bronx; wastewater analysis suggests underdetection of cases in the Bronx. The Alpha variant had slightly faster growth but a lower carrying capacity compared to Iota and Delta in all four boroughs, suggesting stronger limitations on Alpha's growth in New York City (NYC). The founder effect of Iota varied between higher vaccinated and lower vaccinated boroughs with longer delay, shorter duration, and lower fitness of the Alpha variant in lower vaccinated boroughs. Amino acid changes in T-cell and antibody epitopes revealed Delta and Iota having larger antigenic variability and antigenic profiles distant from local previously circulating lineages compared to Alpha. In concert with transmission modeling, our data suggest that the limited spread of Alpha may be due to a lack of adaptation to immunity in NYC. Overall, 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.