中国H5禽流感的时空风险评估:一种可解释的机器学习方法来发现多级驱动因素
Xinyi Wang1, Yihui Xu1, Xi Xi2
1College of Veterinary Medicine, China Agricultural University, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Animals : an open access journal from MDPI
|August 28, 2025
概括
在中国,禽流感的风险取决于家禽的密度和气候. 在人口密集的家禽地区,高温增加了风险,强调了需要有针对性的监测.
科学领域:
- 兽医流行病学
- 环境科学
- 机器学习
背景情况:
- 禽流感 (尤其是H5亚型) 是一个全球性威胁.
- 了解中国人工智能季节性的多层次驱动因素是有限的.
研究的目的:
- 通过可解释的机器学习框架,分析中国的时空H5AI风险.
- 确定关键驱动因素,并量化它们对人工智能爆发的非线性和协同效应.
主要方法:
- 开发了一个可解释的机器学习框架 (XGBoost与SHAP).
- 综合多源数据:家禽密度,科气候,IBA和气象变量.
- 在中国大陆 (2000-2023) 分析了1800起H5 AI疫情.
主要成果:
- 该模型实现了高预测精度 (R2 = 0.776).
- 主要驱动因素包括家禽密度,Cwa气候区和季节性.
- 确定了非线性效应 (例如逆温度关系) 和协同作用 (高温+高家禽密度).
- 高风险的热点集中在中国东部和南部.
结论:
- H5人工智能风险是由多层次环境驱动因素的层次交互决定的.
- 可解释的建模辅助针对人工智能缓解的监控和预警系统.
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