Evidence map›Paper›PMID 40195487›Full record

ArticleCommunications medicine2025

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

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Wan YangDepartment of Epidemiology, Columbia University, New York, NY, USA. wy2202@cumc.columbia.edu.ORCID http://orcid.org/0000-0002-7555-9728
Hilary PartonNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Wenhui LiNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Elizabeth A WattsNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Ellen LeeNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Haokun YuanDepartment of Epidemiology, Columbia University, New York, NY, USA.

Funding

Disease Persistence and Population Dynamics: Modeling Measles under Mass VaccinationR01AI145883 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2019 to 2024
$2.5M
Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomesR21AI175747 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2023 to 2024
$452k
Council of State and Territorial Epidemiologists (CSTE) NU38OT00297National Science Foundation (NSF) DMS-2027369NIAID NIH HHS R01 AI145883NIAID NIH HHS R21 AI175747U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) AI145883
6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsThe 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.

conclusionsThis 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.

Identifiers

PMID40195487
PMCPMC11977191

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.