Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.
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.
Gouvidé Jean Gbaguidi, N. Topanou, Rock Aikpon et al.· Malaria Journal· 0 citations
Malaria remains a significant public health concern and the leading cause of death in children under five in Benin. A comprehensive understanding of malaria’s transmission patterns is essential for guiding targeted and effective interventions, such as seasonal malaria chemoprevention (SMC) and the RTS,S/AS01 vaccine toward sustainable control and elimination efforts. This study explores the temporal, spatial, and demographic patterns of malaria transmission and examines the relationship between malaria incidence and both climate factors and interventions in Benin. A comprehensive descriptive analysis was conducted to explore the seasonality of malaria transmission and the correlation between malaria incidence and climate factors and interventions. Seasonal decomposition by locally estimated scatterplot smoothing (LOESS) was applied to monthly malaria surveillance data to isolate and assess seasonal patterns and long-term trends in malaria incidence across geographic regions and population subgroups. Spatial distribution was analysed using regional incidence data and time series analysis to identify geographic variation in disease burden. Demographic subgroup analysis compared malaria burden across age groups, sex, and among pregnant women. The relationship between malaria incidence and climate factors was assessed using a cross-correlation analysis. Interrupted time series analysis using a generalized additive model framework was used to assess the impact of SMC on malaria incidence across multiple health zones. The analysis revealed a consistent clear bimodal (two-peak) pattern each year in malaria incidence per province with notable provincial differences in burden and timing. The first peak occurs in July while the second peak occurs in October in most of the provinces. Median incidence during the first and second annual transmission peaks across provinces was 22.9 (IQR: 16.3–35.1) and 20.4 (IQR: 12.6–29.8) cases per 1,000 population, respectively. Children under five bear a disproportionate share of the malaria burden, with median monthly incidences of 30.1 versus 10.2 cases per 1,000 population in individuals older than five years, respectively. They also experienced a markedly higher maximum monthly incidence (154.5 vs 39.0 cases per 1,000 population). Mann–Whitney U test revealed no gender differences in malaria incidence among children under five across all provinces, but significantly higher incidence among females older than five years in several provinces. The impact of SMC on malaria incidence varied across health zones, with statistically significant reductions ranging from 28% to 58% in Tanguiéta-Cobly-Matéri, Kandi-Gogounou-Ségbana, Banikoara, and Malanville-Karimama. Cross-correlation analysis revealed that, in most provinces of Benin, increases in average monthly temperature were significantly associated with decreases in malaria incidence at a one-month lag, while rainfall showed a positive temporal association with malaria incidence at a 1–2 month lag, highlighting the influence of climate factors on malaria transmission dynamics. This study provides critical insights into the temporal, spatial, and demographic dynamics of malaria in Benin. The findings support the need for geographically and seasonally tailored malaria interventions and underscore the importance of considering environmental and demographic factors in malaria early warning and response systems. These results lay the groundwork for future modelling studies assessing the impact and cost-effectiveness of malaria control tools such as RTS,S and SMC at a sub-national level in Benin.
S. V. Alohoutade, R. Hounsell, Codjo Dandonougbo et al.· PLOS Global Public Health· 0 citations
Malaria remains a major public health challenge in sub-Saharan Africa (SSA), where climatic variability continues to influence transmission dynamics despite ongoing control efforts. This study investigates the nonlinear and lagged effects of climatic factors on malaria incidence using a balanced monthly panel of 20 SSA countries covering the period 2015-2024 (N = 2400 observations). Monthly malaria incidence per 1000 population is modelled as a function of rainfall, 2-m air temperature, and vegetation greenness, while population density and elevation are included as demographic and topographic controls. Fixed-effects generalised additive models (GAMs) are employed to capture nonlinear exposure-response relationships, complemented by distributed lag nonlinear models (DLNMs) with lags of 0-3 months and a log-linear fixed-effects panel specification. Climatic and environmental variables are obtained from publicly accessible datasets, including CHIRPS, ERA5-Land, MODIS, WorldPop, and NASA SRTM, while malaria incidence data are compiled from national surveillance systems and World Health Organisation repositories. The results reveal significant nonlinear and temporally lagged effects of rainfall and temperature on malaria incidence, with evidence of threshold behaviour across climatic ranges. Vegetation greenness exhibits an increasing-then-saturating association with transmission. Population density is positively associated with malaria incidence, whereas elevation exerts a significant protective effect. The models explain approximately 70-73% of the observed variation in malaria incidence and satisfy key diagnostic requirements. These findings demonstrate the importance of accounting for nonlinear and delayed climatic influences when modelling malaria risk and provide evidence to support climate-informed early warning systems and adaptive malaria control strategies across SSA.
O. A. Okeke, S. Adebanjo· Scientific Reports· 0 citations
Climate variability is increasingly altering the distribution and seasonality of malaria in Africa, yet evidence to guide climate-informed control strategies remains limited. Despite having previously been on course for elimination, Zambia has experienced a resurgence of malaria cases alongside intensifying weather events, underscoring the urgency of adapting interventions to shifting transmission dynamics.
An ecological time-series analysis was conducted using 15 years (2009–2023) of district-level malaria surveillance data from the National Malaria Elimination Centre, together with meteorological and satellite derived climate data from the Zambia Meteorological Department. Spatial hotspot analysis, Spearman correlation, vector autoregressive modeling, Granger-causality tests, forecast error variance decomposition and seasonal autoregressive integrated moving average (SARIMA) models were used to examine climate-malaria relationships and forecast malaria incidence through 2030.
Malaria incidence increased despite intensified control interventions, with a marked geographic shift from historically high-burden provinces (Luapula, Northern, Eastern) toward emerging hotspots in Northwestern, Copperbelt, and Western provinces. The transmission season extended from the previous traditional January-April peak to December–June, reflecting more extended periods of conducive climatic conditions. Forecasting analyses suggest that, if historical climate–malaria relationships persist, malaria incidence will continue increasing through 2030, with relative humidity remaining the strongest climatic predictor (p<0.001).
Malaria transmission in Zambia persists, becoming more prolonged, spatially dynamic, and increasingly climate-sensitive. Integrating real-time climate surveillance, adaptive geographical targeting of malaria interventions and climate informed deployment of malaria vaccines into malaria programming could strengthen elimination efforts and build resilience in climate-vulnerable settings.
T. Nzayisenga, N. Mbewe, K. Mwangilwa et al.· Frontiers in Tropical Diseas...· 0 citations
Background Malaria is a significant public health problem in Cameroon where spatial and temporal heterogeneities in transmission make early-warning and control complicated. The traditional forecasting methods have the limitation that they are not always able to model both the spatial dependency and the uncertainty; therefore, restricting their functionality. Methods We used a spatio-temporal bayesian model that incorporates a Stochastic Partial Differential Equation (SPDE) that models the spatial correlations and an autoregressive time prior AR(1), solved by Integrated Nested Laplace Approximation (INLA). The analysis of severe malaria cases in 2015–2024 was done separately for children <5 years, adults and pregnant women, and rainfall as a climatic covariate and population adjusted offsets for at-risk individuals. Exceedance probabilities were calculated considering historical risk to identify hotspots. Model performance was evaluated through DIC, WAIC and calibration diagnostics. Results Severe malaria risk was positively related to rainfall and this relationship was observed for all demographic groups. The Far North had long-term high risk foci; secondary risks were noted in the North, Centre and Littoral. The most intense hotspots were observed in pregnant women and children. Conclusions The SPDE-AR(1) model offers a strong and uncertainty-based early warning system for severe malaria in Cameroon, which allows interventions to be targeted in areas and populations at high risk.
Akindeh M. Nji, T. M. Adegoke, S. Silal et al.· Parasite Epidemiology and Co...· 0 citations