Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that integrates a cubic baseline with a damped oscillatory kernel, estimated via an affine-invariant ensemble Markov Chain Monte Carlo sampler. The framework accommodates limited data, models parameter uncertainty, and generates probabilistic forecasts for children under five years and individuals aged five years or more. Results show strong empirical adequacy ($R^2 = 0.9958$ for $<5$ years; $R^2 = 0.9956$ for $\geq 5$ years) with residual errors below $2\%$ and well-mixed posteriors confirming convergence. District-level analysis reveals pronounced spatial heterogeneity, with coefficients of variation ranging from $<0.07$ in urban centres such as Kumasi to $>3.3$ in peripheral districts such as Mpohor and Bia East. Forecasts for 2024-2026 indicate a gradual resurgence: from 137,000 to 149,000 cases among children under five years and from 348,000 to 375,000 cases among older individuals, with uncertainty widening over time. By producing probabilistic forecasts, this Bayesian framework provides a principled tool for anticipating malaria fluctuations and strengthening data-driven decision-making in Ghana's national malaria control strategy.

翻译：对撒哈拉以南非洲疟疾动态的可靠量化受到短时、含噪且空间异质性强的监测记录的限制。在加纳，2014年至2023年的医疗机构数据显示，住院人数呈现非线性和年龄特异性波动，然而现有方法难以捕捉随机变异性或提供可靠的置信区间。本研究开发了一个贝叶斯非线性推断框架，该框架将立方基线函数与阻尼振荡核相结合，并通过仿射不变集成马尔可夫链蒙特卡洛采样器进行估计。该框架能够适应有限数据，建模参数不确定性，并为五岁以下儿童及五岁及以上个体生成概率性预测。结果显示其经验充分性很强（五岁以下儿童组R²=0.9958；五岁及以上组R²=0.9956），残差误差低于2%，且充分混合的后验分布确认了收敛性。区域层面分析揭示了显著的空间异质性，变异系数从城市中心（如库马西）的<0.07到外围区域（如姆波霍尔和东比亚）的>3.3不等。2024-2026年的预测表明疟疾将逐步回升：五岁以下儿童病例数将从137,000例增至149,000例，年长个体病例数将从348,000例增至375,000例，且不确定性随时间推移而增加。通过生成概率性预测，该贝叶斯框架为预测疟疾波动并加强加纳国家疟疾防控战略中基于数据的决策提供了有原则的工具。