Aug 2026· Frontiers in Tropical Diseases· 0 citations· 27 references
TL;DR
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.
Abstract
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.
The results reveal significant nonlinear and temporally lagged effects of rainfall and temperature on malaria incidence, with evidence of threshold behaviour across climatic ranges and evidence to support climate-informed early warning systems and adaptive malaria control strategies across SSA.
O. A. Okeke, S. Adebanjo· Scientific Reports· 0 citations
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
Climate change is intensifying the threat of malaria in Africa by altering the geographical range, seasonality, and transmission intensity of the disease. Rising temperatures promote mosquito breeding and parasite development, whereas extreme weather events, particularly flooding, disrupt control efforts and create breeding sites, enabling their spread into previously unaffected highland areas. These factors pose an evolving public health challenge. Malaria-control strategies, often designed for stable climatic conditions, risk being outpaced by rapid environmental changes. However, little is known regarding how national policies integrate climate adaptation into strategic planning. We qualitatively analysed national malaria strategic plans and related policy documents from 11 west and central African countries, published between 2015 and 2025. We assessed incorporation of climate and weather information into malaria surveillance, early-warning systems, and vector-control planning. Although most countries recognise the influence of climate and seasonality on malaria transmission, explicit integration of climate data into planning and surveillance remains scarce. Ghana and Nigeria use advanced data-driven approaches, including predictive agent-based modelling and subnational stratification, to guide interventions, whereas Togo, Benin, Senegal, and The Gambia show emerging progress. However, most national malaria strategic plans remain reactive rather than predictive because of data, technical, and coordination gaps. This Review highlights policy and implementation gaps in integrating climate adaptation within malaria-control programmes. We recommend climate-resilient malaria strategies centred on enhanced surveillance, integrated meteorological forecasting, and strengthened institutional capacity to support adaptive, evidence-based interventions.
W. Leal Filho, U. Okafor, Gouvidé Jean Gbaguidi et al.· Lancet Planetary 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.
This study examines the impact of climatic variables temperature, rainfall, and humidity on malaria transmission in Bauchi State, Nigeria, from 2015 to 2025. Time series and regression analyses were employed to assess the influence of climate factors on malaria incidence. Secondary data on malaria cases were obtained from the Bauchi State Ministry of Health, while climatic data were sourced from the Nigerian Meteorological Agency (NiMet). The Autoregressive Integrated Moving Average (ARIMA) (2,0,1) model revealed strong seasonal patterns in malaria transmission, with cases peaking during the rainy season and declining during the dry months. Regression results indicated that rainfall and humidity had significant positive effects on malaria incidence, whereas temperature exhibited a significant negative relationship. The model demonstrated a high explanatory power (R² = .969), suggesting that climatic factors account for approximately 96.9% of the variation in malaria cases. Forecasting outcomes predict that malaria incidence will stabilize around 1,000 cases per period, implying sustained transmission unless preventive measures are intensified. The study concludes that climate variability significantly influences malaria dynamics in Bauchi State and recommends integrating climate monitoring into public health planning, enhancing seasonal interventions, and strengthening community awareness to mitigate the impacts of climate change on malaria transmission.
YUSUF BALA, MAS’UD SHUAIBU, ABDUL HARUNA BALA· International Journal of Nat...· 0 citations
Malaria transmission in Ghana remains perennial and climate-sensitive, yet the long-term transmission phases underlying disease progression and their differential climatic sensitivities are poorly understood. This study delineated latent, accelerated, and delayed phases of under-five malaria transmission and evaluated the phase-specific influence of rainfall and temperature on malaria incidence, severity, and mortality. Monthly under-five malaria incidence, severe admissions, and malaria-attributed deaths recorded in Tarkwa-Nsuaem Municipality, Ghana, from January 2013 to December 2023 (132 months) were linked with municipality-wide rainfall and temperature data. Epidemic phases were identified using logistic growth modelling and changepoint detection. Incidence, severity, and mortality trajectories were characterized using logistic, exponential–quadratic, and saturating epidemic functions, respectively. Climatic associations were evaluated using phase-specific negative binomial generalized additive models (NB-GAMs) with cyclic seasonal smooths. Model robustness was assessed through bootstrap resampling, cross-validation, basis-dimension diagnostics, and extreme-rainfall sensitivity analyses. SHapley Additive exPlanations (SHAP) were used to quantify predictor importance. The cumulative incidence trajectory exhibited an asymptotic burden of 115,052 cases (95% CI 107,390–125,844), with an inflection point at approximately 75 months and a growth scale of 23.9 months. Severe malaria declined rapidly, reaching functional decline around month 44, whereas mortality exhibited a slower decline, with functional reduction occurring near month 90. Climatic effects varied across transmission phases. Incidence was most climate-sensitive during the accelerated phase, where both temperature and rainfall were strongly associated with malaria transmission during this phase. Sensitivity analyses demonstrated that the accelerated-phase temperature association remained statistically significant after exclusion of extreme-rainfall months, indicating a robust climatic signal, whereas rainfall associations remained statistically detectable in the expanded-k incidence model but demonstrated greater sensitivity to extreme-rainfall observations than the corresponding temperature associations. Severe malaria showed no consistent climatic associations across alternative model specifications. Mortality exhibited temperature sensitivity primarily during the accelerated phase, with limited evidence of rainfall effects. SHAP analyses identified transmission phase, rainfall, and temperature as the most influential predictors of model-predicted incidence. Under-five malaria transmission in Tarkwa-Nsuaem follows distinct latent, accelerated, and delayed phases characterized by differing climatic sensitivities. Temperature demonstrated a stable association with transmission during the accelerated phase, whereas rainfall effects were strongly dependent on extreme hydrological conditions. The integration of epidemic phase delineation, phase-specific NB-GAMs, and explainable machine learning provides a robust framework for identifying climate-sensitive transmission periods and may inform phase-targeted malaria surveillance, preparedness, and intervention planning.
E. M. Baah, Senyefia Bosson-Amedenu, Anafo Abdulzeid Yen et al.· Scientific Reports· 0 citations