SARS-CoV-2 dynamics in New York City during March 2020–August 2023 - Wan Yang; Hilary Parton; Wenhui Li; Elizabeth A. Watts; Ellen Lee; Haokun Yuan (Communications Medicine)
Abstract Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein through August 2023. Methods We fit a metapopulation network SEIRSV (Susceptible-Exposed-Infectious-(re)Susceptible-Vaccination) model to age- and neighborhood-specific data of COVID-19 cases, emergency department visits, and deaths in NYC from the pandemic onset in March 2020 to August 2023. We further validate the model-inference estimates using independent SARS-CoV-2 wastewater viral load data. Results The validated model-inference estimates indicate a very high infection burden—the number of infections (i.e., including undetected asymptomatic/mild infections) totaled twice the population size ( > 5 times documented case count) during the first 3.5 years. Estimated virus transmissibility increased around 3-fold, whereas estimated infection-fatality risk (IFR) decreased by >10-fold during this period. The detailed estimates also reveal highly complex variant dynamics and immune landscape, and higher infection risk during winter in NYC over the study period. Conclusions This study provides highly detailed epidemiological estimates and identifies key transmission dynamics and drivers of SARS-CoV-2 during its first 3.5 years of circulation in a large urban center (i.e., NYC). These transmission dynamics and drivers may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2.