Quality engineering for surveillance analytics using validation gates and reproducible reporting pipelines at scale

Abstract

Surveillance analytics must remain consistent across releases despite changing sources, evolving definitions, and shifting reporting requirements. This study proposes a quality engineering approach for surveillance analytics that combines automated validation gates, peer review of indicator definitions, and reproducible reporting pipelines. We describe baseline comparisons, anomaly detection for key measures, drift monitoring, and controlled rollouts for dashboards used by leadership. The proposed process strengthens credibility by reducing silent data defects and enabling faster investigation when discrepancies occur. Practical checklists are provided to help teams implement quality controls without slowing routine publication cycles.