Modelling malaria environmental suitability and population exposure using the analytic hierarchy process among local government areas in Bayelsa State, Nigeria

Persistent malaria transmission in Africa underscores the need for spatially explicit tools that identify highly endemic areas for targeted control. Although multi-criteria decision analysis (MCDA) offers a structured approach, its application has been limited by outdated environmental inputs and inconsistent factor aggregation methods. This study developed an ecology-informed Malaria Vulnerability Index (MVI) for Bayelsa State, Nigeria, using up-to-date, open-source geospatial datasets and a transparent weighting framework. Thirteen environmental predictors were sourced from OpenStreetMap, Google Earth Engine, WorldPop, and GRID3. Using the Analytical Hierarchy Process (AHP), a 13 × 13 pairwise comparison matrix was constructed and solved using the eigenvalue method to derive criterion weights. Weighted predictors were combined to generate the MVI, which was overlaid with gridded population data to quantify population exposure. Associations between population counts across low, medium, and high vulnerability zones and reported malaria cases were assessed using correlation analysis. Population exposure reflected these patterns: 3.63% of residents lived in high-vulnerability zones, 74.66% in medium, and 21.70% in low zones. Contrary to expectations, the high-vulnerability population showed no significant correlation with reported 2024 malaria cases ( r = − 0.069, p = 0.870), while the low-vulnerability population showed a strong positive correlation ( r = 0.914, p = 0.001). Total population also correlated positively with cases ( r = 0.719, p = 0.044). These findings are interpreted as ecological associations at the LGA level and reflect a combination of health-system factors, reporting completeness, and the scale dependence of count-based measures. Malaria vulnerability in Bayelsa State is primarily driven by hydrological and hydroclimatic conditions, especially proximity to streams and wetlands, rainfall, and microtopographic wetness. The AHP-based MCDA framework provides a rigorous and transparent approach for integrating environmental factors, supporting hydrology-focused targeting of malaria surveillance and vector control, and enabling reproducible MVI mapping using open-source geospatial data.