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整合深度学习方法和基于网络的数据源,用于对禽流感的监测,预测和早期预警

Zahra Movahedi Nia^1,2,3,4, Nicola Bragazzi⁵, Itlala Gizo^6,7

¹Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.

Journal of the Royal Society, Interface

|January 15, 2026

概括

这项研究表明,将Facebook活动等网络数据与使用机器学习的历史病例数据相结合,可以准确预测高度致病性禽流感 (HPAI) 爆发,改善早期检测.

科学领域:

兽医公共卫生兽医公共卫生
流行病学流行病学
数据科学数据科学数据科学

背景情况:

高致病性禽流感 (HPAI),特别是H5N1亚型,在鸟类中引起了重大的全球爆发,并对人类健康和经济构成风险.
加强监测对于减轻HPAI疫情影响至关重要.
当前的监控方法可以从与新数据源和预测建模的整合中受益.

研究的目的:

评估基于网络的新型数据源在预测加拿大HPAI爆发的有效性.
为了比较各种机器学习模型在预测HPAI发病率方面的表现.
确定有助于准确预测HPAI爆发的关键数据特征.

主要方法:

从七个基于网络的来源 (谷歌趋势,谷歌新闻,GDELT,Reddit,Facebook,最低温度,空气质量) 通过一个API驱动的管道收集数据.
整合网络数据与历史HPAI病例数据进行预测.
使用深度学习模型 (GRU,LSTM,CNN) 和经典机器学习模型 (RF,SVM,NB) 来进行预测.
通过RMSE和相关性评估模型性能,并通过排列方法和曼-惠特尼U测试评估特征重要性.

主要成果:

门式复发单元 (GRU) 深度学习模型在预测HPAI爆发方面表现出最高的准确性.
历史HPAI病例数据成为最重要的预测因素 (p < 0.01).
脸书活动和最低温度被确定为其他重要的预测因素.

结论:

将各种基于网络的数据与历史病例信息相结合,大大提高了对HPAI疫情的早期检测.
机器学习模型,特别是GRU,显示出对实时HPAI监控的希望.
改善早期检测有助于及时进行公共卫生干预,并减少与HPAI相关的经济损失.

关键词:

加拿大加拿大加拿大加拿大加拿大加拿大加拿大深度学习是一种深度学习.疾病预测疾病预测高致病性禽流感禽流感是一种高度致病性的疾病.机器学习是机器学习.基于网络的数据.

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