Enhancing epidemic preparedness: a data-driven system for managing respiratory infections

This research presents a prototype data-driven Early Warning System (EWS) designed to strengthen epidemic preparedness—particularly for viral respiratory infections like influenza and COVID-19—using Microsoft Power BI. Developed in the context of Golestan Province, Iran—a region vulnerable to compounding disasters (e.g., floods, mine explosions, and pandemics)—the system integrates real-time data from hospitals, labs, primary care, and emergency services to:

• Enable early detection of outbreaks using statistical control charts (CUSUM and EWMA);
• Support rapid decision-making through interactive dashboards and geospatial mapping;
• Optimize healthcare resource allocation during surges;
• Identify high-risk populations for targeted interventions (e.g., vaccination campaigns);
• The tool exemplifies how digital innovation and data integration can enhance health system resilience in low- and middle-income settings, aligning with global frameworks such as the Sendai Framework for Disaster Risk Reduction, particularly Priority 2: Strengthening disaster risk governance and Priority 4: Enhancing preparedness for effective response.

This work contributes directly to preventing biological hazards from escalating into full-scale health emergencies—a critical but often under-emphasized dimension of disaster risk management.