GEOSPATIAL INTELLIGENCE FOR PUBLIC HEALTH: USING BIG DATA AND GIS CLUSTERING TO DRIVE TARGETED HIV RESPONSES IN NIGERIA
DOI:
https://doi.org/10.60787/tnamp.v24.666Keywords:
Geospatial Intelligence, Public Health, GIS Clustering, Big-data analytics, Real-time monitoring, HIVtransmission hotspotsAbstract
This study demonstrates the application of geospatial intelligence by integrating Big Data analytics with Geographic Information System (GIS) clustering to transform HIV surveillance and response in Delta State, Nigeria. Facing persistent new infections and spatial gaps in intervention, the research leverages the 5Vs of Big Data, Volume, Velocity, Variety, Veracity, Value [1] to process multi-source datasets: HIV recency testing (HTS_RECENT_RITA), Electronic Medical Records (EMR), and high-resolution geographic/population data from GRID3 and WorldPop. The methodology involves geocoding recent infection cases and applying a 2km buffer clustering analysis to delineate active transmission hotspots and estimate at-risk populations. The analysis yields precise spatial intelligence, identifying 37 high-risk settlements and an estimated 327,193 individuals at risk, with significant concentrations in urban-centric Local Government Areas like Uvwie and Sapele. The intelligence is validated with high statistical confidence (99.12% overall accuracy, Kappa 0.989). The study culminates in proposing a scalable GIS-based Management Information System (MIS) framework designed to institutionalize this geospatial intelligence, training public health officials in data-driven decision-making for real-time monitoring, resource optimization, and precision interventions. This work provides a replicable model for converting complex data into actionable public health strategies to directly combat active HIV transmission zones.
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