Develops a framework to enhance medical supply distribution in flood-prone areas, indicating strategic planning benefits.
BACKGROUND: Nigeria faces recurring flood disasters that severely disrupt its healthcare systems and delays medical relief efforts. Current medical supply prepositioning by the National Emergency Management Agency (NEMA) remains inadequate due to infrastructure limitations, funding constraints, and poor coordination. OBJECTIVE: To develop and validate a Strategic Medical Hub Locator Framework that integrates Geographic Information Systems (GIS) analysis with qualitative local knowledge to optimise medical supply prepositioning for flood emergencies in Nigeria. METHODS: We employed a mixed-methods approach, combining qualitative analysis of 22 GIS-based flood risk studies in Nigeria and structured expert survey responses from 34 professionals with flood response experience, including five with direct NEMA warehouse experience. The framework was grounded in Disaster Risk Reduction theory and supported by Protection Motivation Theory and Theory of Planned Behaviour. RESULTS: The framework identified eight strategic medical hub locations: six existing NEMA warehouses (Abuja, Lagos, Kano, Gombe, Maiduguri, Kaduna) with the strongest performance, and two new strategic sites (Akure and Uyo) to address service gaps in South-South and South-East regions. Abuja emerged as the highest-performing hub with a composite score reflecting good centrality, governance capacity, and operational readiness. The framework successfully bridged data silos between spatial flood risk patterns and operational constraints. CONCLUSIONS: Integration of GIS thematic analysis with qualitative local data provides a viable, replicable method for enhancing humanitarian medical logistics in flood-prone regions. The Strategic Medical Hub Locator Framework offers a structured decision-support tool that can inform evidence-based investment in Nigeria's disaster health infrastructure, while being adaptable to other Global South contexts.
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Massironi et al. (2026) studied this question.
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