Evidence map›Paper›PMID 38545093›Full record

ArticleEClinicalMedicine2024

The potential epidemiologic, clinical, and economic value of a universal coronavirus vaccine: a modelling study.

Sarah M Bartsch, Kelly J O'Shea, Danielle C John, Ulrich Strych, Maria Elena Bottazzi, Marie F Martinez, Allan Ciciriello, Kevin L Chin, Colleen Weatherwax, Kavya Velmurugan and 4 more

Abstract read
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

14 authors.

Sarah M BartschPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Kelly J O'SheaPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Danielle C JohnPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Ulrich StrychNational School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA.
Maria Elena BottazziNational School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA.
Marie F MartinezPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Allan CicirielloNational School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA.
Kevin L ChinPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Colleen WeatherwaxPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Kavya VelmuruganPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Jessie HeneghanPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Sheryl A ScannellPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Peter J HotezNational School of Tropical Medicine, Department of Pediatrics, and Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA.
Bruce Y LeePublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.

Funding

Project 4: Virtual Public Health Precision Nutrition LaboratoryU54TR004279 · NCATS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI Bruce Y Lee · 2022 to 2026
$5.4M
Simulating the Spread and Control of Multiple MDROs Across a Network of Different Nursing HomesP01AI172725 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI HUANG, SUSAN S. · 2023 to 2023
$2.9M
Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2R01GM127512 · NIGMS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2020 to 2023
$2.1M
MOdeling Nursing homes to Affect Response to COVID-19 (MONARC)R01HS028165 · AHRQ · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2021 to 2022
$993k
AHRQ HHS R01 HS028165NCATS NIH HHS U54 TR004279NIAID NIH HHS P01 AI172725NIGMS NIH HHS R01 GM127512
6 · The paper itself

Abstract

Background: With efforts underway to develop a universal coronavirus vaccine, otherwise known as a pan-coronavirus vaccine, this is the time to offer potential funders, researchers, and manufacturers guidance on the potential value of such a vaccine and how this value may change with differing vaccine and vaccination characteristics. Methods: Using a computational model representing the United States (U.S.) population, the spread of SARS-CoV-2 and the various clinical and economic outcomes of COVID-19 such as hospitalisations, deaths, quality-adjusted life years (QALYs) lost, productivity losses, direct medical costs, and total societal costs, we explored the impact of a universal vaccine under different circumstances. We developed and populated this model using data reported by the CDC as well as observational studies conducted during the COVID-19 pandemic. Findings: A pan-coronavirus vaccine would be cost saving in the U.S. as a standalone intervention as long as its vaccine efficacy is ≥10% and vaccination coverage is ≥10%. Every 1% increase in efficacy between 10% and 50% could avert an additional 395,000 infections and save $1.0 billion in total societal costs ($45.3 million in productivity losses, $1.1 billion in direct medical costs). It would remain cost saving even when a strain-specific coronavirus vaccine would be subsequently available, as long as it takes at least 2-3 months to develop, test, and bring that more specific vaccine to the market. Interpretation: Our results provide support for the development and stockpiling of a pan-coronavirus vaccine and help delineate the vaccine characteristics to aim for in development of such a vaccine. Funding: The National Science Foundation, the Agency for Healthcare Research and Quality, the National Institute of General Medical Sciences, the National Center for Advancing Translational Sciences, and the City University of New York.

Indexed as

CoronavirusEconomicModellingUniversalVaccine

Identifiers

PMID38545093
PMCPMC10965405

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.