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.
Abstract
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.
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
Malaria transmission is strongly influenced by climatic conditions that regulate Anopheles mosquito survival and parasite development. However, existing studies in Bangladesh are largely short-term or region-specific, limiting understanding of long-term climate–malaria dynamics. This study addresses this gap by examining national-level, long-term associations between malaria incidence and climatic factors. A retrospective ecological time-series study was conducted using monthly malaria surveillance data from Bangladesh between January 2008 and December 2023. Climatic variables—rainfall, temperature, relative humidity, and sunshine duration—were obtained from the Bangladesh Meteorological Department for the same period ensuring full temporal alignment with malaria data. Descriptive analyses were used to assess long-term trends and seasonality. Pearson correlation was used to assess linear relationships between variables, while Spearman correlation was applied to capture potential monotonic but non-linear associations and reduce sensitivity to outliers. Multiple linear regression models incorporating one-month lagged variables were developed to identify independent climatic predictors of transmission. A total of 192 monthly observations were analyzed. Malaria incidence showed a marked long-term decline, yet a consistent seasonal pattern persisted, with transmission peaking during the monsoon months (July–September). Mean relative humidity demonstrated the strongest positive correlation with malaria incidence (p < 0.001), followed by lagged rainfall (p < 0.01). In multivariate analysis, mean humidity, lagged rainfall, and lagged sunshine duration remained significant predictors, while temperature was not independently associated. The regression model accurately captured seasonal timing but underestimated peak magnitude during outbreak years. Despite declining incidence, malaria in Bangladesh retains a stable, climate-driven seasonal signature. Climatic variables are effective in predicting transmission timing but insufficient to explain outbreak intensity. Integrating climate indicators into surveillance systems may strengthen preparedness and support malaria elimination efforts in residual transmission settings.
Sania Akter, Zesan Ahmed, Ragib Abid et al.· Tropical Medicine and Health· 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
Background: Malaria mortality studies in Ghana and sub-Saharan Africa have commonly relied on traditional regression approaches or generalised additive models, which may not fully represent delayed transmission effects, nonlinear exposure-response patterns, or uncertainty in mortality estimates. This study modelled the delayed and nonlinear dynamics of malaria mortality in Ghana and examined incidence-related thresholds associated with cumulative mortality effects.
Objectives: The study applied a Distributed Lag Nonlinear Model (DLNM) to assess temporal malaria mortality dynamics in Ghana using a climate-sensitive health modelling framework that incorporated uncertainty-adjusted estimation.
Methods: An ecological time-series analysis was conducted using annual age-standardised malaria mortality and incidence data for Ghana from 1990 to 2023 obtained from the Global Burden of Disease database. DLNMs with nonlinear spline functions, distributed lag structures and inverse-variance weighting were fitted separately for males, females and the combined population. Model performance was assessed using adjusted R², deviance explained, Akaike Information Criterion and residual diagnostics.
Results: Malaria mortality and incidence declined over the study period, while mortality remained consistently higher among males. The DLNM performed well, with adjusted R² values ranging between 0.9936 and 0.9945, and deviance explained over 99.6%. Nonlinear temporal effects were observed in all sex groups (p < 0.05). Delayed mortality amplification was mainly observed at lags 2-4, with higher cumulative delayed effects among males. The maximum cumulative mortality effects occurred at incidence thresholds around 21,000 cases per 100,000 population, with the highest cumulative effect estimated for males (49.28).
Conclusions: The findings indicate that malaria mortality dynamics in Ghana are nonlinear, delayed and threshold-sensitive. The combined use of DLNM, sex-specific lag modelling and uncertainty-adjusted weighting provides a useful framework for strengthening malaria surveillance, early-warning modelling and targeted public health planning.
Francis Ayiah-Mensah, Emmanuel Harris, Mohammed Frempong et al.· Journal of Advances in Medic...· 0 citations
Background: Dengue remains a major public health challenge in Eastern Visayas, Philippines, yet climate-driven transmission dynamics remain underexplored in the region. Methods: This retrospective ecological time-series study analyzed weekly surveillance and meteorological data from 2019 to 2023. Nonlinear exposure–response relationships and lagged associations between rainfall, temperature, and dengue incidence were modeled using a Distributed Lag Nonlinear Model (DLNM) within a Bayesian framework fitted via Integrated Nested Laplace Approximation (INLA), assessing lag effects up to 10 weeks. Results: Weekly rainfall strongly influenced dengue risk, peaking markedly at 200 mm with a cumulative Relative Risk (RR) of 19.4 (95% CrI: 10.3–36.5). Higher precipitation levels showed a downward risk trajectory, consistent with larval flushing. Temperature displayed a suggestive bimodal pattern with elevated risk at 24°C and 28–29°C across 4–6-week lags, though near-average temperatures (e.g., 28.5°C) showed no statistically strong cumulative risk changes.Conclusion: Dengue risk in Eastern Visayas is driven by precise rainfall thresholds and thermal lag windows. Incorporating 200 mm precipitation and 28–29°C temperature triggers into proactive climate-based surveillance—pending external validation—can enhance regional public health preparedness and local outbreak response.
N. Milla· Recoletos Multidisciplinary...· 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.
