Post-vaccination immune persistence varies significantly across booster regimens, particularly between homologous (e.g., mRNA/mRNA) and heterologous (e.g., VV/mRNA) strategies. This heterogeneity poses challenges for public health planning and long-term immunity forecasting. In this study, we developed a novel statistical framework integrating Bayesian survival analysis with linear mixed-effects regression to jointly model longitudinal IgG dynamics and time-to-waning of protective immunity in a cohort of 334 individuals—206 previously infected and 128 infection-naïve—from real-world data collected between December 2022 and September 2023. Plasma optical density (OD) values from ELISA assays served as a proxy for anti-SARS-CoV-2 IgG levels. Participants were categorized by booster type (homologous vs. heterologous), prior infection status, and number of doses (2-4). Our integrated model revealed that heterologous boosting was associated with significantly slower IgG decay (hazard ratio HR = 0.62, 95% credible interval [0.48-0.79]) compared to homologous regimens. Moreover, prior SARS-CoV-2 infection independently enhanced both humoral and cellular immune persistence, with infected individuals showing 1.8-fold higher median OD values at 6+ months post-boost. The joint modeling approach successfully captured inter-individual variability through random slopes and intercepts while accounting for censoring in immune waning via a Weibull-based survival component. This framework provides a flexible, predictive tool for evaluating future booster strategies—not only for SARS-CoV-2 but also for other pathogens requiring durable immunity. Our findings support the immunological advantage of heterologous prime-boost schedules, especially when combined with natural infection, and underscore the value of methodological integration in longitudinal immunology research.
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Qurashi et al. (2026) studied this question.