Evidence map›Paper›PMID 35335111›Full record

ArticleVaccines2022

Evaluating COVID-19 Booster Vaccination Strategies in a Partially Vaccinated Population: A Modeling Study.

Clément R Massonnaud, Jonathan Roux, Vittoria Colizza, Pascal Crépey

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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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

4 authors at 2 institutions in 2 countries.

Clément R MassonnaudRSMS-U 1309, ARENES-UMR 6051, EHESP, CNRS, Inserm, Université de Rennes, 35043 Rennes, France.
Jonathan RouxRSMS-U 1309, ARENES-UMR 6051, EHESP, CNRS, Inserm, Université de Rennes, 35043 Rennes, France.ORCID 0000-0002-0158-2837
Vittoria ColizzaInstitut National de la Santé Et de la Recherche Médicale (INSERM), Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), Sorbonne Université, 75014 Paris, France.ORCID 0000-0002-2113-2374
Pascal CrépeyRSMS-U 1309, ARENES-UMR 6051, EHESP, CNRS, Inserm, Université de Rennes, 35043 Rennes, France.ORCID 0000-0003-1852-0752
Centre National de la Recherche Scientifique · FRInserm · FR

Funding

Agence Nationale de la Recherche SPHINX-17-CE36-0008-0
6 · The paper itself

Abstract

backgroundSeveral countries are implementing COVID-19 booster vaccination campaigns. The objective of this study was to model the impact of different primary and booster vaccination strategies.

methodsWe used a compartmental model fitted to hospital admission data in France to analyze the impact of primary and booster vaccination strategies on morbidity and mortality, assuming waning of immunity and various levels of virus transmissibility during winter.

resultsStrategies prioritizing primary vaccinations were systematically more effective than strategies prioritizing boosters. Regarding booster strategies targeting different age groups, their effectiveness varied with immunity and virus transmissibility levels. If the waning of immunity affects all adults, people aged 30 to 49 years should be boosted in priority, even for low transmissibility levels.

conclusionsIncreasing the primary vaccination coverage should remain a priority. If a plateau has been reached, boosting the immunity of younger adults could be the most effective strategy, especially if SARS-CoV-2 transmissibility is high.

Indexed as

boosterCOVID-19epidemiologymodelingSARS-CoV-2vaccinationvaccines

Identifiers

PMID35335111
PMCPMC8952850
OpenAlexW4221110401

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.