Malaria hotspots and risk factors among children under-five years of age across eight West African countries: A geospatial analysis of DHS data.

Background: Malaria remains a significant public health challenge in sub-Saharan Africa, disproportionately affecting children under five years of age. Understanding the spatial distribution of malaria and its associated risk factors is essential for implementing effective, targeted control strategies. In this study, we investigated spatial variation and key determinants of malaria prevalence among children under five in eight West African countries. Methods: The study used the most recent Demographic and Health Surveys Data from eight West African countries where malaria infection status was determined by microscopy. Generalised Linear Mixed Models were first used to explore associations between malaria infection and sociodemographic predictors, accounting for survey design. These models were extended into Generalised Linear Geostatistical Models to incorporate spatial random effects. Malaria prevalence was predicted at a 10 x 10 km resolution, and exceedance probability maps were generated to identify high-burden areas with prevalence exceeding 30%. Model validation was done using empirical variograms, PIT histograms, and residual spatial analyses. Results: The use of insecticide-treated mosquito nets was significantly associated with reduced odds of malaria infection in four of the eight countries included in the study, while younger child age (<2 years) was consistently associated with lower risk across all countries. In addition, marked spatial heterogeneity in malaria prevalence was observed, with high predicted prevalence in Benin and Cote d'Ivoire and lower prevalence in Ghana and Liberia. Conclusion: This study highlights the importance of geospatial approaches for understanding malaria transmission dynamics in order to tailor malaria control measures to local context. The findings underscore the need to strengthen the effective use of insecticide-treated nets and community-level vector control, while improving spatial surveillance and data integration to support context-specific malaria interventions.