ORIGINAL RESEARCH article
Front. Immunol.
Sec. Vaccines and Molecular Therapeutics
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1619631
Personalized Prediction of SARS-CoV-2 Vaccine-Induced Immunity after Boost: A Longitudinal Analysis Using Joint Modelling
Provisionally accepted
Laurent Gillet*
Iraklis Papadopoulos
Anh Nguyet Diep
Joey Schyns
Claire GourzonesFrédéric MinnerGermain BonhommeM ParidansNicolas GillainEddy Husson
Mutien Garigliany
Gilles Darcis
Daniel DesmechtMichèle GuillaumeAnne-Françoise Donneaufabrice bureau- University of Liège, Liège, Belgium
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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. In this study, we conducted a 15-month longitudinal analysis of a cohort of 1,000 individuals to identify key determinants of the serological response following the first SARS-CoV-2 vaccine booster. We investigated how these factors influenced the risk of subsequent infection and developed statistical models to predict individual trajectories of anti-spike (S) IgG and neutralizing antibody (NAb) levels. 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.
Keywords: Joint modelling, SARS-CoV-2, immune response, Breakthrough infection, prediction
Received: 28 Apr 2025; Accepted: 02 Sep 2025.
Copyright: © 2025 Gillet, Papadopoulos, Diep, Schyns, Gourzones, Minner, Bonhomme, Paridans, Gillain, Husson, Garigliany, Darcis, Desmecht, Guillaume, Donneau and bureau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Laurent Gillet, University of Liège, Liège, Belgium
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.