Abstract
Malaria is still persisting as a complex geo-epidemiological challenge across sub-Saharan Africa, where transmission dynamics are mediated by interlinked climatic and environmental factors. In Nigeria’s Sahelian drylands, this challenge is amplified by ecological fragility and erratic climate regimes that complicate intervention planning. Thus, this study employed a geo-epidemiological approach to analyze malaria “cool” and “hot” spots across Yobe State, integrating epidemiological datasets and climatic indicators to unravel spatio-temporal risk heterogeneity. Using malaria incidence data (2014–2023) obtained from the Yobe State Disease Epidemic Repository, the study applied unsupervised machine learning algorithms to classify malaria risk zones across three micro-climatic settings. Findings revealed substantial temporal and spatial variability in malaria burden across ecological gradients. Temporal trend analysis indicated a weak but persistent rise in malaria incidence in the SuSZ (R² = 0.77), reflecting sustained environmental favorability for vector breeding, while the SaSZ and TZ showed fluctuating patterns shaped by rainfall variability and inconsistent intervention continuity. Seasonal decomposition highlighted a clear transmission cycle peaking in August – September, synchronized with the rainy season, underscoring the climatic sensitivity of malaria in dryland contexts. The clustered hotspots are observed to be around irrigation corridors and floodplains in the SuSZ, particularly around Fika and Gadaka, while coldspots were concentrated in the arid northern SaSZ around Dagona and Azaam Kura. These spatial patterns confirmed that malaria risk is strongly conditioned by hydrological systems, land use and settlement morphology. The study advances the understanding of malaria eco-epidemiology in semi-arid settings by demonstrating that climate variability and anthropogenic factors.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2026 Special Focus—Unseen Unsustainability: Addressing Hidden Risks to Long-Term Wellbeing for All
KEYWORDS
Malaria hotspots, Geo-epidemiology, Climate variability, Yobe State, Sahel drylands
