通过机器学习研究印度高度致病性禽流感H5N1疫情的环境决定因素和时空动态
Suresh Kuralayanapalya Puttahonnappa1, Jayashree Anandakumar2, Nagendra Nath Barman3
1ICAR-National Institute of Veterinary Epidemiology & Disease Informatics, 4HG6+5QW, Ramagondanahalli, Unnamed Road, Yelahanka, Bengaluru, Karnataka, 560064, India. suresh.kp@icar.org.in.
Scientific reports
|October 16, 2025
概括
印度的高度致病性禽流感 (HPAI) 疫情在冬季和春季达到顶峰. 环境因素和机器学习预测HPAI风险,南部和东北部地区最脆弱.
科学领域:
- 兽医流行病学 兽医流行病学
- 地理空间分析的研究.
- 机器学习在公共卫生中的应用
背景情况:
- 高致病性禽流感 (HPAI) 对家禽和公众健康构成全球威胁.
- 了解HPAI的空间和时间动态对于有效的疾病控制至关重要.
研究的目的:
- 综合评估印度HPAI疫情的空间和时间动态.
- 确定关键的环境风险因素,并预测印度各地的HPAI脆弱性.
主要方法:
- 综合地理空间分析,机器学习 (随机森林,分类树组合) 和遥感.
- 评估的环境变量包括温度,植被指数 (EVI,LAI),蒸发透气 (PET),降水,湿度和风速.
- 估计的基本繁殖数 (R0) 和生成的HPAI风险地图.
主要成果:
- 印度的HPAI疫情在冬季和春季呈现出明显的季节性峰值.
- 温度,EVI,LAI,PET,降水,湿度和风速等环境变量是重要的HPAI风险预测因素.
- 印度南部和东北部地区被确定为HPAI高风险地区.
结论:
- 该研究提供了对印度HPAI动态的全面了解,这对于有针对性的监测和干预至关重要.
- 这些发现支持资源分配和政策制定的战略规划,以减轻HPAI对鸟类和人类的威胁.
- 综合建模方法提高了传染病爆发的预测和管理.
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