🤖 AI Summary
Malaria forecasting in sub-Saharan Africa faces significant challenges due to strong seasonality, reporting biases, and non-stationary transmission dynamics. This study proposes a novel hybrid probabilistic forecasting framework that integrates Gaussian process regression (GPR) with Holt-Winters exponential smoothing to predict monthly malaria admissions among children under five in Ghana. The approach effectively captures nonlinear patterns while preserving seasonal structure and ensuring long-term stability, and it provides rigorous quantification of predictive uncertainty. The model achieves an R² of 0.9906, with 94.2% of residuals falling within ±2σ, and forecasts monthly admissions between 8,000 and 12,200 cases from 2024 to 2028. These results reveal stable relative patterns amid regional ecological heterogeneity, offering high-precision decision support for national malaria control programs.
📝 Abstract
Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary transmission dynamics reduce the reliability of conventional models. In Ghana, district-level malaria surveillance requires forecasting frameworks that are probabilistically rigorous and robust under limited data. This study proposes a hybrid framework integrating Gaussian Process Regression (GPR) with Holt-Winters exponential smoothing for modelling monthly under-five malaria admissions. GPR captures non-linear behaviour and predictive uncertainty, while Holt-Winters stabilises long-horizon forecasts and preserves seasonal structure. Using ten years of district-level data (2014-2023), performance was evaluated via rolling-origin expanding-window validation. The hybrid model achieved $R^2 = 0.9906$ versus $0.8213$ for Holt-Winters alone, with $94.2\%$ of residuals within $\pm 2σ$ bounds. Forecasts for 2024-2028 project average monthly admissions from approximately 8{,}000 to 12{,}200 cases. Spatio-temporal analysis revealed pronounced ecological heterogeneity: northern high-burden districts exhibited stable relative patterns despite large absolute fluctuations. The framework provides a scalable probabilistic approach for malaria early warning and operational planning in endemic settings, supporting Ghana's national malaria control strategy.