Evidence map›Paper›PMID 41098725›Full record

ArticleFrontiers in immunology2025

Personalized prediction of SARS-CoV-2 vaccine-induced immunity after boost: a longitudinal analysis using joint modeling.

Iraklis Papadopoulos, Anh Nguyet Diep, Joey Schyns, Claire Gourzones, Frédéric Minner, Germain Bonhomme, M Paridans, Nicolas Gillain, Eddy Husson, Mutien Garigliany and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

16 authors.

Iraklis PapadopoulosBiostatistics and Research Method Center - Public Health Department, Liège University, Liège, Belgium.
Anh Nguyet DiepBiostatistics and Research Method Center - Public Health Department, Liège University, Liège, Belgium.
Joey SchynsLaboratory of Cellular and Molecular Immunology, GIGA Institute, Liège University, Liège, Belgium.
Claire GourzonesCOVID-19 Platform, Liège University, Liège, Belgium.
Frédéric MinnerCOVID-19 Platform, Liège University, Liège, Belgium.
Germain BonhommeDepartment of Pathology, FARAH, Liège University, Liège, Belgium.
M ParidansFrom Biostatistics to Health Promotion Research Unit, Public Health Department, Liège University, Liège, Belgium.
Nicolas GillainBiostatistics and Research Method Center - Public Health Department, Liège University, Liège, Belgium.
Eddy HussonBiostatistics and Research Method Center - Public Health Department, Liège University, Liège, Belgium.
Mutien GariglianyDepartment of Pathology, FARAH, Liège University, Liège, Belgium.
Gilles DarcisInfectious Diseases Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium.
Daniel DesmechtDepartment of Pathology, FARAH, Liège University, Liège, Belgium.
Michèle GuillaumeFrom Biostatistics to Health Promotion Research Unit, Public Health Department, Liège University, Liège, Belgium.
Fabrice BureauLaboratory of Cellular and Molecular Immunology, GIGA Institute, Liège University, Liège, Belgium.
Anne-Françoise DonneauBiostatistics and Research Method Center - Public Health Department, Liège University, Liège, Belgium.
Laurent GilletCOVID-19 Platform, Liège University, Liège, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The SARS-CoV-2 pandemic has revealed substantial inter-individual variability in immune responses, particularly following widespread primary vaccination and booster campaigns. These differences affect the durability of protective immunity and the need for additional booster doses. To optimize the management of current and future epidemics, there is a critical need for predictive tools that personalize immune monitoring and guide targeted booster strategies for vulnerable populations. Methods: In this study, we conducted a 15-month longitudinal analysis of a cohort of 1,000 individuals to identify key determinants of serological response following the first SARS-CoV-2 vaccine booster. We investigated how these factors influenced the risk of subsequent infection, and we developed statistical models to predict individual trajectories of anti-spike (S) IgG and neutralizing antibody (NAb) levels. Results-discussion: Our findings show that joint models (JMs), which integrate longitudinal antibody measurements with infection outcomes, significantly outperform traditional modeling approaches in predicting immune trajectories. This work underscores the potential of joint modeling to enable personalized immune surveillance, supporting strategies to sustain protective immunity in high-risk populations. In the future, this approach may be adapted for monitoring long-term immunity against other infectious diseases.

Indexed as

COVID-19COVID-19 VaccinesImmunization, SecondarySARS-CoV-2AdultAgedAntibodies, NeutralizingAntibodies, ViralFemaleHumansImmunoglobulin GLongitudinal StudiesMaleMiddle AgedModels, StatisticalPrecision MedicineAntibodies, NeutralizingAntibodies, ViralCOVID-19 VaccinesImmunoglobulin GSpike Glycoprotein, Coronavirusbreakthrough infectionimmune responsejoint modellingpredictionSARS-CoV-2

Identifiers

PMID41098725
PMCPMC12518370

What OpenQuestion holds

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Registered trials

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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.