Evidence map›Paper›PMID 37554154›Full record

ArticleLancet regional health. Americas2023

Modelling the impact of a high-uptake bivalent booster scenario on the COVID-19 burden and healthcare costs in New York City.

Abhishek Pandey, Meagan C Fitzpatrick, Seyed M Moghadas, Thomas N Vilches, Charles Ko, Ashwin Vasan, Alison P Galvani

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Optimal Annual COVID-19 Vaccine Boosting Dates Following Previous Booster Vaccination or Breakthrough Infection.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abhishek PandeyCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Meagan C FitzpatrickCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Seyed M MoghadasAgent-Based Modelling, York University, Toronto, Ontario, Canada.
Thomas N VilchesCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Charles KoBureau of Epidemiology Services, Integrated Surveillance and Data, New York City Department of Health & Mental Hygiene, NY, USA.
Ashwin VasanOffice of Commissioner, New York City Department of Health & Mental Hygiene, NY, USA.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.

Funding

Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial dataR01AI151176 · NIAID · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2020 to 2024
$3.9M
NIAID NIH HHS R01 AI151176
6 · The paper itself

Abstract

Background: Uptake of the COVID-19 bivalent booster vaccine (targeting the original SARS-CoV-2 strain and subvariants BA.4 and BA.5 of the Omicron variant) among eligible residents of New York City (NYC) has been modest and declining. Assessing the impact of improved population-level booster coverage with bivalent vaccines in NYC can help inform investment towards vaccination and potential cost-savings. Methods: We calibrated an agent-based model of disease transmission to confirmed and probable cases of COVID-19 in NYC and simulated it to project outcomes under two scenarios. In the base case scenario, we assumed that vaccination continued with the average daily rate of 92 vaccine doses per 100,000 administered during December 2022. In the counterfactual scenario, we modeled a high-uptake scenario between January 1, 2023 and March 31, 2023, with an average daily rate of 296 vaccine doses per 100,000 population that increased bivalent coverage in NYC to match the age-specific influenza vaccine coverage of the 2020-2021 season. Vaccination rate outside the campaign duration remained the same as the base case scenario. Findings: Compared to the base case, the high-uptake scenario averted 88,274 (95% Confidence Interval [CI]: 77,097-100,342) cases, and prevented 2,917 (95% CI: 2,557-3,267) hospitalizations between January 1 through the end of June 2023. Averted outcomes resulted in net savings of $217.2 (95% CI: 190.0-242.2) million in direct healthcare costs. We estimated that the high-uptake scenario would avert 72,879 (95% CI: 63,894-82,228) days of student absenteeism from schools due to COVID-19 illness. Interpretation: Our results illustrate the continued benefits of COVID-19 vaccines in preventing severe health outcomes, averting healthcare costs, and maintaining educational continuity in NYC. Funding: The Canadian Institutes of Health Research, The Natural Sciences and Engineering Research Council of Canada, NIH, Centers for Disease Control and Prevention (CDC), NSF, The Commonwealth Fund, and The Notsew Orm Sands Foundation.

Indexed as

Bivalent boosterCOVID-19New York CitySchool absenteeism

Identifiers

PMID37554154
PMCPMC10405058

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.