ONWUKA DAVID CHUKWUK

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.