Evidence map›Paper›PMID 41057458›Full record

ArticleScientific reports2025

Assessing the effect of social contact structure on the impact of pneumococcal conjugate vaccines.

Anabelle Wong, Sarah C Kramer, Daniel M Weinberger, Matthieu Domenech de Cellès

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Anabelle WongMax Planck Institute for Infection Biology, Charitéplatz 1, 10117, Berlin, Germany. wong@mpiib-berlin.mpg.de.
Sarah C KramerMax Planck Institute for Infection Biology, Charitéplatz 1, 10117, Berlin, Germany.
Daniel M WeinbergerPublic Health Modeling Unit, Yale School of Public Health, New Haven, CT, 06511, USA.
Matthieu Domenech de CellèsMax Planck Institute for Infection Biology, Charitéplatz 1, 10117, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although pneumococcal conjugate vaccines (PCVs) have greatly reduced diseases caused by vaccine-targeted serotypes (VT) of Streptococcus pneumoniae, vaccine impact may be eroded by the increase in rates of disease caused by non-vaccine serotypes (NVT). Here, we investigated the effect of social contact patterns on the dynamics of vaccine impact in carriage. We developed a neutral, age-structured, Susceptible-Colonized model incorporating VT-NVT co-colonization and verified it against real-world carriage data in children. Using social contact matrices from 34 countries, we assessed the impact of contact patterns on the time required to eliminate VT in a given age group (defined here as the time needed for the relative proportion of VT in overall carriage to drop by 95% after the introduction of vaccination). Finally, we quantified the contribution of various parameters-such as vaccine efficacy, coverage, immunity waning, and population susceptibility-to the dynamics of VT elimination. Our model recapitulated the observed decline of VT carriage and showed that varying the contact structure alone led to different time-to-elimination (3.8-6 years). We found that higher total contact rate and assortativity in children under 5 accelerated VT elimination. Additionally, higher vaccine efficacy and coverage, and slower immunity waning led to shorter time-to-elimination. These findings illuminate the mechanisms controlling the dynamics of vaccine impact and may help predict the impact of PCVs in communities with different contact patterns.

Indexed as

Pneumococcal InfectionsPneumococcal VaccinesStreptococcus pneumoniaeVaccines, ConjugateAdolescentCarrier StateChildChild, PreschoolHumansInfantSerogroupVaccinationVaccine EfficacyPneumococcal VaccinesVaccines, ConjugatePneumococcal carriageSerotype replacementSocial contactStreptococcus pneumoniaeVaccine impact

Identifiers

PMID41057458
PMCPMC12504593

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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.