Bayesian joint modelling of antibody kinetics and test-negative vaccine effectiveness to characterise hybrid immunity across epidemic waves

Vaccine effectiveness against symptomatic SARS-CoV-2 infection varies over time and across epidemic waves. This variation can reflect waning immunity, immune escape by emerging variants, exposure heterogeneity, and differences in previous infection history. Test-negative case-control designs are widely used to monitor vaccine effectiveness, while longitudinal serological studies describe antibody trajectories after vaccination and infection. These evidence streams are often analysed separately. This manuscript presents a simulation-based Bayesian joint modelling framework that links individual-level antibody kinetics to test-negative vaccine effectiveness estimates across successive epidemic waves. Hybrid immunity is represented as the combined effect of vaccination and infection history, with latent antibody titres following a boost-and-decay process after each immunising event. A variant-specific titre-protection curve maps latent antibody levels to the risk of symptomatic infection. The framework is intended to illustrate how apparent changes in vaccine effectiveness may be decomposed into components related to waning, immune escape, and exposure heterogeneity. Using fully synthetic data calibrated to plausible vaccination schedules, infection histories, assay variability, and epidemic-wave structures, the model is evaluated in three simulation studies. The simulations illustrate that joint modelling can recover broad features of the assumed titre-protection relationship under idealised conditions and can separate waning from variant-specific shifts when the data-generating process is correctly specified. The results are not presented as validation on real-world surveillance data. Instead, they provide a transparent methodological proof of concept and identify assumptions that would need to be assessed before applying the framework to linked serological and test-negative datasets. Author declarationsThis manuscript reports a methodological simulation study. All individual-level data used in the manuscript are synthetic. No human participants, patient records, biological samples, or identifiable data were used. No ethics approval was required for the analyses presented here. The author declares no competing interests. This study did not receive external funding.