The findings reveal pronounced spatial dependence in malaria prevalence, with transmission patterns strongly associated with temperature and vegetation cover, and Integrating spatial modelling with environmental data offers a powerful framework for refining malaria control strategies and accelerating progress towards elimination targets.
Abstract
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.
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, and the need for geographically and seasonally tailored malaria interventions.
S. V. Alohoutade, R. Hounsell, Codjo Dandonougbo et al.· PLOS Global Public Health· 0 citations
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.
Abstract Background Malaria distribution in sub-Saharan Africa is becoming increasingly spatially heterogeneous. Pre-elimination areas, high-transmission settings and urban areas with increasing transmission may all coexist within the same country. Effective intervention planning requires identifying high-risk areas and their drivers—an area in which geospatial models can provide valuable insights. Yet these models often assume stationary risk factors across transmission levels and frequently rely on prevalence survey data, which are unreliable in low-transmission settings. This study uses routine public-sector health facility data (ie, DHIS2) to model malaria incidence in Senegal from 2017 to 2021, accounting for transmission levels, and introduces novel fine-scale estimates of reported malaria incidence derived from dasymetric disaggregation. Methods Health facility catchment populations were estimated using a probabilistic approach, allowing individuals to attend multiple facilities based on relative travel time. Malaria incidence per catchment was modelled using high-resolution environmental and socioeconomic covariates within a Bayesian hierarchical modelling framework. Separate models were fitted by season and endemicity-urbanisation level to assess the heterogeneity of risk factors. We adapted dasymetric disaggregation methods from population mapping to redistribute facility-level cases to 1 km pixels. Results Estimated malaria incidence increased from 60 cases per 1000 people in 2017 to 71 cases in 2021. Key risk factors varied across endemicity-urbanisation levels; some factors were uniquely associated with low (eg, distance to roads), moderate (eg, cropland) and high-transmission settings (eg, flooded vegetation). Fine-scale maps represent malaria cases that the public-sector health system routinely reports, rather than the total malaria burden, and revealed potential underdiagnosis in remote areas. Conclusions Our findings emphasise the importance of considering endemicity levels when evaluating malaria risk factors in heterogeneous transmission contexts. As health system data quality continues to improve, this approach offers a scalable alternative for mapping reported malaria incidence, supporting better targeted interventions and the identification of underdiagnosed areas.
Camille Morlighem, Corentin Visée, C. Nnanatu et al.· BMJ Global Health· 0 citations
Background and aims Malaria remains a public health concern in Bangladesh, despite a notable decline in reported cases since 2012 and a brief resurgence in 2014. Aligned with the United Nations Sustainable Development Goal (SDG) 3.3, which targets the elimination of malaria by 2030, Bangladesh has implemented multiple control and prevention strategies. This study assesses the significance of the decline in malaria cases and applies Bayesian hierarchical models to capture spatial dynamics of malaria-related vulnerability, map district-level risk, and inform targeted interventions. Methods A nationwide spatial analysis was conducted using district-level malaria data from the Bangladesh Disaster-related Statistics (BDRS) 2021, which report cumulative counts of “population suffering from malaria due to disaster” for 2015–2020. These data were used as a proxy indicator of relative malaria vulnerability. National malaria time-series data were obtained from Bangladesh’s National Strategic Plan for Malaria Elimination (2021–2025) published by the Asia Pacific Malaria Elimination Network (APMEN), together with malaria surveillance summaries from the World Malaria Reports (2024,2025) published by the World Health Organization (WHO). District-level rainfall data were obtained from the Bangladesh Water Development Board. Bayesian hierarchical disease mapping models were used to assess spatial dependence and district-level malaria risk. Spatial visualization and autocorrelation analyses (Global Moran’s I, Geary’s C, and Local Moran’s I) were performed using R (version 4.4.0), and Bayesian model estimation was carried out using WinBUGS via Markov Chain Monte Carlo methods. Results The analysis showed evidence of a trend in decreasing malaria cases by a value of −0.691 in the Mann-Kendall trend test. However, spatial analysis revealed significant clustering and geographic heterogeneity. Persistent high-risk clusters were identified in the southeastern hilly regions. Additionally the presence of excess zeros in the data justified the use of zero-inflated models. Conclusion The findings show significant national decline and offer valuable insights into the geographical variability in malaria-related vulnerability in Bangladesh. Rather than being direct indicators of malaria transmission, the results should be understood as representing relative risk patterns based on data related to disasters. Under current data limitations, these results provide evidence to boost spatially informed malaria control methods and assist geographically focused interventions.
Ummehani Mimi, M. Karim, Sultana Begum et al.· PLoS ONE· 0 citations
Background: Malaria remains a major health issue in Jayapura, where climatic and landscape variability creates uneven transmission risks. Early identification of vulnerable areas is essential for supporting targeted control strategies.Objective: This study aims to develop a Random Forest model integrating remote-sensing environmental variables to identify malaria-prone areas in Jayapura District and Jayapura City.Methods: Environmental predictors including rainfall, land surface temperature, slope, NDVI, humidity, land use, and population density were linked to confirmed malaria cases. Data were split into 70% training and 30% testing datasets. Model performance was evaluated using accuracy, sensitivity, macro-sensitivity, and macro-specificity, and the outputs were used to generate a spatial malaria risk map.Results: The Random Forest model achieved an overall accuracy of 0.667, sensitivity of 0.833, macro-sensitivity of 0.800, and macro-specificity of 0.867, indicating good capability in identifying areas with higher malaria vulnerability. Feature importance analysis showed that rainfall, land surface temperature, and slope were the most influential predictors of malaria risk. High-risk areas were concentrated in coastal and urban zones, while peri-urban agricultural areas showed moderate risk and high-elevation regions exhibited lower vulnerability.Conclusion: Integrating remote-sensing environmental data with epidemiological information allows the Random Forest model to capture key malaria-risk patterns in Jayapura. The resulting spatial risk map can support targeted vector control strategies, improved surveillance, and more efficient allocation of public health resources toward Indonesia’s Malaria Elimination 2030 goals.
M. I. Rumbiak, P. Widayani, B. Widartono· Jurnal Kesehatan Vokasional· 0 citations
A deterministic climate-driven SEAIR-SEI malaria transmission model that includes the impact of temperature, rainfall, and humidity on mosquito biology and endogenous community awareness is developed that can serve as an efficient and affordable framework for malaria control in sub-Saharan Africa.
R. N. Nyabwanga, L. K. Ketter, A. N. Osogo et al.· medRxiv· 0 citations