Aug 2026· PLOS Global Public Health· Vol 6· 0 citations· 34 references
Medicine
TL;DR
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.
Abstract
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.
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.
Gouvidé Jean Gbaguidi, N. Topanou, Rock Aikpon et al.· Malaria Journal· 0 citations
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
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 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
This study investigated the temporal trends of malaria, tuberculosis, and cholera in Nigeria from 2010 to 2023 using epidemiological and statistical modelling approaches and provides evidence-based recommendations for targeted disease control strategies in Nigeria.
V. O. Tobi, T. J. Oluwafemi· Journal of Applied Sciences...· 0 citations
Understanding how malaria incidence and mortality vary across space and time is important for strengthening malaria surveillance and supporting evidence-based planning of control interventions. This study examined the joint spatial and temporal patterns of malaria incidence and mortality in Kenya, identified high-risk counties and assessed whether the two outcomes shared common distributions. County-level malaria incidence and mortality data for 2013 to 2024 were analysed using a joint Bayesian hierarchical model based on a spatio-temporal conditional autoregressive framework, which accounted for spatial dependence among neighbouring counties and temporal correlation across successive years. Model performance was compared with a multivariate spatio-temporal modelling framework to evaluate shared and outcome-specific variation. Integrated Nested Laplace Approximation was used for Bayesian inference. Weighted adjacency matrices were constructed, and structured and unstructured spatial effects were incorporated to account for spatial heterogeneity, temporal dependence and unexplained variability. The results showed persistent spatial clustering of both outcomes throughout the study period. Counties in the lake-endemic region, particularly Kisumu, Siaya and Busia, consistently had higher spatial risks for malaria incidence and mortality than most other counties. The spatial distributions of incidence and mortality were positively associated, indicating that counties with higher incidence tended to have higher mortality. Temporal patterns differed between the outcomes, with incidence showing broader fluctuation and mortality showing less pronounced change. The findings indicate that joint spatio-temporal modelling can support malaria surveillance and guide spatially targeted control interventions in Kenya.
Polycarp Nyabuto, A. Wanjoya, Thomas Magetto et al.· Asian Journal of Probability...· 0 citations