埃塞俄比亚阿姆哈拉地区疟疾发病率的混合预测建模：整合多输出回归与时间序列预测
Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
AI总结：
针对埃塞俄比亚阿姆哈拉地区疟疾传播难预测的问题，研究提出融合时间序列预测、多输出回归与传统回归的混合建模框架，利用环境、病例及人口数据训练，可同时预测多类结果，精度优于单一方法，能为疟疾防控的循证决策与资源优化提供支持。
摘要：
疟疾仍是埃塞俄比亚的重大公共卫生问题，在阿姆哈拉地区尤为突出，当地季节性且不可预测的传播模式使得防控工作极具挑战。准确预测疟疾暴发对于有效配置资源和及时开展干预至关重要。本研究提出一种混合预测建模框架，结合时间序列预测、多输出回归和传统基于回归的预测方法，对疟疾发病率进行预测。研究利用阿姆哈拉地区各卫生中心的环境变量、既往疟疾病例数据及人口统计信息，对模型进行训练与验证。多输出回归方法可同时预测多个结果，包括疟原虫（Plasmodium）虫种特异性病例数、时间趋势及空间差异，而混合框架则能捕捉季节性模式与预测变量间的相关性。所提出的模型比单一方法具备更高的预测精度，能够揭示隐藏模式，为公共卫生部门提供有价值的信息。本研究提供了一套有效且可重复的疟疾发病率预测框架，可为流行地区的循证决策、靶向干预及资源优化提供支持。
英文摘要：
Malaria remains a major public health concern in Ethiopia, particularly in the Amhara Region, where seasonal and unpredictable transmission patterns make prevention and control challenging. Accurately forecasting malaria outbreaks is essential for effective resource allocation and timely interventions. This study proposes a hybrid predictive modeling framework that combines time-series forecasting, multi-output regression, and conventional regression-based prediction to forecast the incidence of malaria. Environmental variables, past malaria case data, and demographic information from Amhara Region health centers were used to train and validate the models. The multi-output regression approach enables the simultaneous prediction of multiple outcomes, including Plasmodium species-specific cases, temporal trends, and spatial variations, whereas the hybrid framework captures both seasonal patterns and correlations among predictors. The proposed model exhibits higher prediction accuracy than single-method approaches, exposing hidden patterns and providing valuable information to public health authorities. This study provides a valid and repeatable malaria incidence prediction framework that can support evidence-based decision-making, targeted interventions, and resource optimization in endemic areas.
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