A National-Scale Public Health Intelligence Platform for Canada: End-to-End Data Engineering, Analytics, Alerting, and Business Intelligence

Abstract

Public health decision-making increasingly depends on timely, explainable, and reproducible analytics. However, national health data pipelines are often fragmented, retrospective, and inaccessible to decision-makers. This paper presents a top-tier, end-to-end Public Health Intelligence Platform designed for Canada that integrates automated Extract–Transform–Load (ETL) pipelines, a dimensional PostgreSQL data warehouse, a FastAPI-based analytics and alerting layer, and an executive Power BI dashboard. The platform supports longitudinal health surveillance, explainable alerting, and interactive analytics with full auditability. Experimental evaluation demonstrates efficient query performance, reliable alert execution, and effective visualization for policy-oriented decision support. The system is production-ready, extensible, and suitable for adoption by public health agencies and research institutions.

Introduction

Public health decision-making in Canada is hindered by fragmented data sources, manual reporting workflows, and static analytics that limit timely insight generation. Health indicators are published by multiple agencies in formats that are poorly suited for rapid analysis or operational use. This research addresses these challenges by developing a unified Public Health Intelligence Platform that converts raw health data into structured, explainable, and decision-ready intelligence.

The proposed system uses a layered architecture that integrates automated ETL pipelines, a PostgreSQL analytical data warehouse, a FastAPI-based analytics and alerting service, and a Power BI visualization layer. A star-schema data model enables efficient aggregation, trend analysis, and drill-down across geographic and temporal dimensions. Power BI dashboards, supported by custom DAX measures, provide interactive views of key indicators, national trends, and alert summaries for executive decision support.

A key contribution of the platform is its explainable and auditable alerting framework, where deterministic threshold-based rules generate alerts that are stored with full contextual metadata. This ensures transparency, traceability, and governance in automated analytics. Evaluation using Canadian health indicator data demonstrated low-latency query performance, reliable alerting, and effective translation of complex datasets into actionable insights.

Overall, the study shows that combining modern data engineering, analytics APIs, and business intelligence tools can significantly improve public health surveillance and responsiveness. While current limitations include batch ingestion and rule-based alerting, the platform establishes a strong foundation for future enhancements such as machine learning–based anomaly detection, predictive modeling, and real-time data integration.

Conclusion

This paper presented a top-tier Public Health Intelligence Platform for Canada. By combining automated data engineering, explainable analytics, and executive dashboards, the system delivers actionable insights for policymakers and researchers. The platform demonstrates a scalable, reproducible, and transparent approach to national health surveillance and decision support.

References

[1] World Health Organization, \"Public Health Surveillance,\" WHO, 2023. [2] Microsoft, \"Power BI Documentation,\" 2024. [3] PostgreSQL Global Development Group, \"PostgreSQL 16 Documentation,\" 2024. [4] IEEE, \"IEEE Access Author Guidelines,\" 2024.

Copyright

Copyright © 2026 Sahil Hareshbhai Patel, Princy Hareshbhai Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.