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混合模型将趋势和季节性组件与机器学习算法相结合,可以准确预测疟疾发病率

Syed Shah Areeb Hussain^1,2, Sanchit Bedi³, Chander Prakash Yadav^1,4

¹Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.

PLOS global public health

|October 17, 2025

概括

准确的疟疾预测对于消除疟疾的努力至关重要. 结合机器学习和时间序列分析的混合模型显著提高了预测准确度和精度,为公共卫生规划提供了强大的工具.

科学领域:

流行病学流行病学
环境健康环境健康
数据科学数据科学数据科学

背景情况:

有效的疟疾消除策略依赖于准确的发病率预测.
气候因素显著影响疟疾传播动态.
现有的预测模型在平衡准确性和精度方面存在局限性.

研究的目的:

开发和评估可靠的模型,用于预测印度戈阿的疟疾发病率.
确定影响疟疾传播的关键气候预测因素.
通过整合机器学习和时间序列方法来提高预测的准确性和精度.

主要方法:

利用了印度戈阿 (2010-2019) 的气候和疟疾发病率数据.
采用多线性和沙普利增量解释 (SHAP) 来识别重要的气候预测因素.
经过训练和测试的支持矢量机器 (SVM),随机森林 (RF),极端梯度增强 (XGB),ARIMA,SARIMA,SARIMAX以及混合模型 (例如,RF-ARMA).

主要成果:

极端气候,而不是平均值,对疟疾传播的影响更大.
机器学习模型提供了高精度但更低的准确性 (RMSE: 13-37).
时间序列模型提供了更高的精度,但精度较低 (RMSE:5-41).
混合模型将时间序列组件集成到机器学习中,在保持精度的同时显著提高了准确性 (RMSE:0.5-15).

结论:

结合机器学习和时间序列分析的混合模型为疟疾发病率预测提供了卓越的准确性和精度.
这种综合方法提高了疟疾消除计划预测的可靠性.
开发的方法有可能在流行病学数据分析和公共卫生干预中得到更广泛的应用.

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