A Gis-Based Big Data Framework for Mapping Hiv Recency Hotspots and Targeting Interventions in Delta State, Nigeria

HIV remains a significant public health challenge in Nigeria, particularly in Delta State, where new infections continue to emerge despite various control efforts [1], [2]. A major issue hindering targeted interventions is the absence of spatial data analysis tools to identify settlements and populations at risk of recent infections [3]. This study addresses this gap by employing Geographic Information System (GIS) clustering techniques alongside the HIV Testing Services (HTS) key performance indicator on recent infection (HTS_RECENT_RITA) to analyse and visualize high-risk areas. Utilizing spatial analysis and population risk clustering, the research identifies geographic hotspots of recent HIV infections, with a focus on high-risk settlements and an estimate of the affected population [4]. The methodology incorporates data from local health facility registers, Electronic Medical Records (EMR), and the GRID3 and WorldPop databases. The findings, which demonstrate high spatial accuracy (Overall Accuracy: 99.12%, Kappa: 0.989), are expected to inform public health officials on more effective resource allocation and intervention strategies, improving the precision of HIV prevention efforts. This study contributes to advancing public health strategies through the integration of spatial analysis and recency testing, providing a robust framework for identifying specific geographic hotspots and addressing HIV transmission risk areas in Delta State.