使用LSTM在巴西预测和检测登革热疫情:整合人类流动性和气候因素
1Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Infectious Disease Modelling
|December 1, 2025
概括
将人类流动数据集成到深度学习模型中,显著改善了巴西登革热预测和疫情检测. 这种增强的方法为公共卫生规划和疫情准备提供了更准确的预测.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 登革热是一个重大的全球健康威胁,巴西因气候,社会环境因素和流动性而面临严重的疫情.
- 传统的预测模型往往忽视了人类运动在登革热传播中的作用.
- 准确的登革热预测对于早期预警系统和有效的公共卫生干预至关重要.
研究的目的:
- 开发和评估用于登革热预测的深度学习框架,其中包括人类流动性数据.
- 为了提高登革热病例预测和巴西城市疫情检测的准确性.
- 在预测模型中评估人类流动对登革热传播动态的影响.
主要方法:
- 开发了一个基于LSTM的深度学习模型,以预测每周的登革热病例和检测疫情.
- 该模型整合了历史登革热病例,滞后的气候变量 (温度,湿度) 和根据人类移动性调整的进口病例.
- 对基线模型的性能进行了评估,使用了MAE,MAPE,CRPS,精度,灵敏度,特异性和F1得分等指标.
主要成果:
- 移动性增强的LSTM模型在登革热病例预测和疫情检测方面始终优于基线模型.
- 拟议的模型实现了较低的MAE和MAPE,表明更高的准确性,以及对精确校准的不确定性估计的优越CRPS.
- 该模型在疫情分类方面显示出更高的敏感性和F1分数,在识别疫情期间方面证明有效.
结论:
- 将人类移动数据纳入深度学习模型提供了一个可扩展的框架,用于增强登革热早期预警系统.
- 开发的模型提供了更准确的登革热病例预测和疫情分类,有助于公共卫生规划和资源分配.
- 这种方法可适应预测受移动性和气候影响的其他载体传播疾病,改善全球流行病准备.
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