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.

翻译：准确的疟疾预测在撒哈拉以南非洲仍是一项重大挑战，该地区强烈的季节性、报告不确定性以及非平稳的传播动态降低了传统模型的可靠性。加纳的区级疟疾监测需要具有概率严谨性且在数据有限条件下保持鲁棒性的预测框架。本研究提出了一种混合框架，将高斯过程回归与霍尔特-温特斯指数平滑法相结合，用于模拟月度五岁以下儿童疟疾住院病例。高斯过程回归捕捉非线性行为与预测不确定性，而霍尔特-温特斯法则稳定长期预测并保留季节结构。基于十年（2014-2023年）的区级数据，采用滚动起点扩展窗口验证方法评估性能。混合模型实现了$R^2 = 0.9906$（同期霍尔特-温特斯法为$0.8213$），且$94.2\%$的残差位于$\pm 2σ$边界内。2024-2028年的预测显示，月平均住院病例数约为8,000至12,200例。时空分析揭示了显著的生态异质性：尽管绝对波动较大，但北部高负担地区的相对模式保持稳定。该框架为疟疾流行地区的早期预警与运营规划提供了可扩展的概率方法，支持加纳国家疟疾控制战略。