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.
