Operationalizing real time outbreak detection using agile delivery models for public health analytics teams

Abstract

Outbreak detection programs must adapt quickly to evolving threats while maintaining reliability and explainability for decision makers. This research describes an agile delivery model tailored to public health analytics teams building dashboards, alerting logic, and data pipelines for near real-time surveillance. The model introduces validation sprints, definition peer review, and joint acceptance testing with epidemiologists as mechanisms to prevent misleading signals. We also present a readiness checklist for production releases that includes drift monitoring, lineage verification, and documented alert thresholds. The proposed operating model improves responsiveness without sacrificing accountability or trust.