融合多种模型预测埃塞俄比亚阿姆哈拉地区疟疾发病率，提升防控精准度。
Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
- 结合时间序列与多输出回归，同时预测不同疟原虫种类和时空趋势。
- 相比单一模型，预测准确率显著提升，捕捉到隐藏传播模式。
- 适合公共卫生部门用于资源调配与精准干预决策。
疟疾仍是埃塞俄比亚的重大公共卫生问题，尤其在阿姆哈拉地区，其季节性和不可预测的传播模式给防控带来挑战。准确预测疟疾暴发对合理配置资源和及时干预至关重要。本研究提出一种混合预测建模框架，整合时间序列预测、多输出回归与传统回归方法，基于阿姆哈拉地区各卫生中心的环境变量、历史疟疾病例数据及人口信息进行模型训练与验证。多输出回归可同时预测不同疟原虫种类病例、时间趋势与空间差异；混合框架能有效捕捉季节性规律及预测因子间的关联。所提模型预测精度高于单一方法，揭示了隐藏传播模式，为公共卫生机构提供有价值决策支持。该研究构建了一个可复现的疟疾发病率预测框架，有助于实现基于证据的决策、靶向干预与资源优化。
原文摘要 · Abstract (English)
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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