Development of an original algorithm to characterize serological antibody response that improve infectious diseases surveillance

Interpreting serological data remains challenging, particularly in low-prevalence or cross-reactive contexts, where antibody responses often show substantial overlap between exposed and unexposed individuals and may depart from normal distributional assumptions. Conventional cutoff-based approaches often yield inconsistent or biased estimates of seroprevalence. Here, we present a decisional framework based on finite mixture models (FMMs) that enhances the robustness and interpretability of serological analyses. Beyond simply applying mixture models, our framework integrates multiple methodological innovations : (i) systematic comparison of Gaussian and skew-normal mixture models to accommodate asymmetric antibody distributions; (ii) rigorous model selection using the Cramer-von Mises test (p > 0.01) combined with a parsimonious score (APS) to prioritize models with well-separated clusters; and (iii) hierarchical clustering of posterior probabilities to collapse latent components into biologically meaningful seronegative and seropositive groups. Applied to chikungunya virus (CHIKV) data from Bangladesh, the framework produced prevalence estimates consistent with ROC-based methods while probabilistically identifying borderline cases. Validation on SARS-CoV-2 and dengue datasets further demonstrated its generalizability: for SARS-CoV-2, the approach identified up to five latent clusters with high sensitivity (up to 100%) and specificity (up to 100%), enabling discrimination by disease severity. For dengue, it revealed interpretable subgrouping consistent with background exposure and subclinical infection, despite limited confirmed cases. By integrating distributional flexibility, robust goodness-of-fit testing, and biologically guided cluster consolidation, this decisional FMM framework provides a reproducible and scalable method for serological interpretation across pathogens and epidemiological settings, addressing key limitations of threshold-based classification.