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Background: The test negative design is central to real time assessment of vaccine effectiveness, yet differential prevalence of prior infection between vaccinated and unvaccinated groups can bias estimates and amplify week to week volatility. Because serology is imperfect, naive adjustment is insufficient. We aimed to develop and operationalise a serology informed Bayesian framework that corrects for prior infection bias, and to embed it in a FAIR/FHIR native pipeline suitable for routine surveillance.Methods: We integrated ELISA serology, qPCR and RT qPCR results, vaccination records, and demographics via an HL7 FHIR ingestion layer and FAIR compliant extract transform load. We specified a hierarchical logistic model with site and week random effects and a latent indicator for prior infection. Serology informed the prior on this latent variable using sensitivity and specificity and Rogan Gladen corrected prevalence; uncertainty in sensitivity and specificity was propagated through sensitivity analyses. We contrasted naive test negative design vaccine effectiveness with serology informed estimates and evaluated stability across time and strata. The pipeline is containerised with Nextflow and Snakemake and Docker, orchestrated by Airflow, and outputs an auditable dashboard.Results: In synthetic datasets emulating real world surveillance, naive test negative design underestimated vaccine effectiveness when prior infection was common. Incorporating serology informed priors reduced this downward bias and yielded smoother temporal trajectories, with the largest corrections in strata with higher inferred prior infection prevalence. The approach was robust across broad literature consistent ranges of serological sensitivity and specificity and to leave one site out validation. The end to end implementation reproduced these gains with governed data flows and full provenance.Conclusions: A Bayesian correction that treats prior infection as latent and leverages misclassification aware serology improves vaccine effectiveness inference from test negative design data, enhancing accuracy and temporal stability. The FAIR/FHIR native workflow enables immediate governed deployment for public health decision making and provides a template for extending to waning, variant periods, dose or product comparisons, and co circulating pathogens. Routine adoption can strengthen the evidentiary basis for vaccine policy.