CommSight – Visualization of Community-Based Communication Networks for Disease Surveillance Using Network Analysis

Abstract

This study presents the development and implementation of CommSight, a webbased tool for visualizing community-based communication networks to support disease surveillance in urban areas. Using datasets collected from Manila and Pasay, the system enabled the upload, analysis, and visualization of social network data through advanced network analysis techniques. Centrality measures—including degree, weighted degree, betweenness, closeness, and eigenvector centrality—were computed to identify key individuals positioned as information hubs and bridges within the networks. The Leiden algorithm was utilized for community detection, revealing groups with shared attributes and identifying connector nodes critical to information flow. Results demonstrated that certain individuals consistently ranked highly across centrality metrics, making them strategic points for early outbreak detection and targeted intervention. Communities often clustered by affiliation, with bridge nodes facilitating cross-community communication. These findings highlight the value of combining centrality analysis and community detection to inform more effective public health surveillance strategies. CommSight provides a practical tool for public health practitioners to identify priority individuals and communities, supporting proactive and targeted responses to emerging health threats in complex urban environments. Future research may expand this approach by incorporating temporal data and applying it to additional urban contexts.