Guiding malaria elimination interventions: a data-driven approach to resource optimization in Benin, West Africa.

Background

Malaria remains a major public health challenge in Benin, where environmental conditions strongly influence its transmission dynamics. Understanding the spatial heterogeneity of malaria risk is essential for targeting interventions more effectively.

Methods

This study applied high-resolution environmental covariates and a robust Stochastic Partial Differential Equation (SPDE) model to investigate the distribution of malaria prevalence across Benin. Remote sensing-derived variables, including temperature and vegetation indices, as well as information about soil, built-up areas, and bare surfaces, were integrated into the model. Malaria prevalence was estimated using rapid diagnostic test (RDT) data from national Demographic and Health Surveys (DHS), and model performance was assessed through receiver operating characteristic (ROC) analysis.

Results

The findings reveal pronounced spatial dependence in malaria prevalence, with transmission patterns strongly associated with temperature and vegetation cover. Distinct hotspots were identified in the northern and central regions of the country, indicating areas of elevated risk. The model demonstrated satisfactory predictive accuracy (with a sensitivity of 0.619 and a specificity of 1.000), underscoring the utility of environmental covariates in capturing the spatial variability of malaria transmission.

Conclusion

Malaria in Benin exhibits marked spatial heterogeneity shaped by environmental factors. The identification of high-risk hotspots highlights priority areas for intensified intervention. Integrating spatial modelling with environmental data offers a powerful framework for refining malaria control strategies and accelerating progress towards elimination targets.