SARS-CoV-2 dynamics in New York City during March 2020-August 2023.

Area of Science:

• Epidemiology
• Infectious Disease Modeling
• Public Health

Background:

• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused widespread epidemics since 2020.
• Understanding SARS-CoV-2 infection burden and dynamics, especially post-Omicron emergence, remains challenging.
• This study reconstructs SARS-CoV-2 dynamics in New York City (NYC) through August 2023 using extensive surveillance data.

Purpose of the Study:

• To reconstruct and analyze SARS-CoV-2 infection burden and transmission dynamics in NYC.
• To understand the impact of the Omicron variant on SARS-CoV-2 epidemiology.
• To provide detailed estimates informing future public health planning.

Main Methods:

• A metapopulation network SEIRSV model was fitted to age- and neighborhood-specific COVID-19 data (cases, emergency visits, deaths) in NYC from March 2020 to August 2023.
• Model-inference estimates were validated using independent SARS-CoV-2 wastewater viral load data.
• The model accounts for Susceptible, Exposed, Infectious, (re)Susceptible, and Vaccination states.

Main Results:

• Total infections in the first 3.5 years were estimated to be over twice the NYC population, exceeding 5 times the documented case count.
• Virus transmissibility increased approximately 3-fold, while infection-fatality risk (IFR) decreased by over 10-fold during the study period.
• Complex variant dynamics, immune landscape, and higher winter infection risk in NYC were revealed.

Conclusions:

• This study provides detailed epidemiological estimates of SARS-CoV-2 transmission dynamics and drivers in NYC over 3.5 years.
• Key findings include high infection burden, increased transmissibility, and decreased IFR.
• The identified dynamics and drivers are relevant for other urban populations and can inform future mitigation strategies.