SARS-CoV-2 dynamics in New York City during March 2020–August 2023
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 va...
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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00826-6 |
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| author | Wan Yang Hilary Parton Wenhui Li Elizabeth A. Watts Ellen Lee Haokun Yuan |
| author_facet | Wan Yang Hilary Parton Wenhui Li Elizabeth A. Watts Ellen Lee Haokun Yuan |
| author_sort | Wan Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a6c3f850eab54b1094e1e62fd4ce3821 |
| institution | OA Journals |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-a6c3f850eab54b1094e1e62fd4ce38212025-08-20T02:12:07ZengNature PortfolioCommunications Medicine2730-664X2025-04-015111210.1038/s43856-025-00826-6SARS-CoV-2 dynamics in New York City during March 2020–August 2023Wan Yang0Hilary Parton1Wenhui Li2Elizabeth A. Watts3Ellen Lee4Haokun Yuan5Department of Epidemiology, Columbia UniversityNew York City Department of Health and Mental HygieneNew York City Department of Health and Mental HygieneNew York City Department of Health and Mental HygieneNew York City Department of Health and Mental HygieneDepartment of Epidemiology, Columbia UniversityAbstract 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.https://doi.org/10.1038/s43856-025-00826-6 |
| spellingShingle | Wan Yang Hilary Parton Wenhui Li Elizabeth A. Watts Ellen Lee Haokun Yuan SARS-CoV-2 dynamics in New York City during March 2020–August 2023 Communications Medicine |
| title | SARS-CoV-2 dynamics in New York City during March 2020–August 2023 |
| title_full | SARS-CoV-2 dynamics in New York City during March 2020–August 2023 |
| title_fullStr | SARS-CoV-2 dynamics in New York City during March 2020–August 2023 |
| title_full_unstemmed | SARS-CoV-2 dynamics in New York City during March 2020–August 2023 |
| title_short | SARS-CoV-2 dynamics in New York City during March 2020–August 2023 |
| title_sort | sars cov 2 dynamics in new york city during march 2020 august 2023 |
| url | https://doi.org/10.1038/s43856-025-00826-6 |
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