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

Effective epidemic preparedness is critical for minimizing the health and societal impacts of viral respiratory infections. This study details the development of a data-driven early warning system (EWS) designed to improve outbreak detection and response utilizing the data integration and visualization capabilities of Microsoft Power BI.

This research utilized a structured three-phase approach to design a respiratory infections (RIs) management dashboard. Phase 1, focused on identifying critical variables through literature reviews and expert interviews. In Phase 2, Microsoft Power BI was employed for dashboard development, integrating data from diverse sources. Phase 3 involved usability testing with health professionals who evaluated navigation, data accuracy, decision-support features, providing feedback to enhance visualization clarity and filtering capabilities.

Key data categories include individual-level variables, such as age, symptoms, and vaccination records, alongside population-level metrics like infection rates and regional vaccination coverage enabling functionalities such as identifying high-risk individuals, tracking infection dynamics, and optimizing resource allocation. The dashboard, developed using Power BI visualizes epidemiological trends, intervention outcomes, and resource utilization. A relational database schema ensures efficient data retrieval, facilitating comprehensive analysis.

The prototype EWS represents a scalable and integrative framework aimed at enhancing public health applications, particularly in the context of respiratory infections. By incorporating data from diverse health sectors, the system offers decision-makers access to critical epidemiological indicators, supporting early outbreak detection and improved epidemic management. Its potential to unify health institutions underscores its value in fostering a more cohesive and effective approach to epidemic preparedness. Nevertheless, while the system demonstrates significant promise, further evaluation in real-world settings is essential to determine its practical impact on public health outcomes and its ability to mitigate health crises.