确定荷兰野生鸟类中的禽流感热点
Ronald Petie1, Eduardo de Freitas Costa1, Christian Kampichler2
1Department of Epidemiology, Bioinformatics and Animal Studies, Wageningen Bioveterinary Research, The Netherlands.
PloS one
|February 12, 2026
概括
一个新的模型使用死亡率数据预测野生鸟类的高致病性禽流感 (HPAI) 热点. 这有助于及时检测和有针对性的监测,以减轻HPAI的传播和保护鸟类种群.
科学领域:
- 兽医流行病学 兽医流行病学
- 野生动物疾病生态学 野生动物疾病生态学
- 传染病建模传染病建模
背景情况:
- 高致病性禽流感 (HPAI) 对包括人类在内的鸟类和哺乳动物群体构成重大威胁.
- 在野生鸟类中检测HPAI爆发受到有限的机会性采样的阻碍,导致空间和时间感染数据不足.
- 有效的HPAI监测需要准确,及时预测野生鸟群的感染热点.
研究的目的:
- 开发和验证荷兰境内野生鸟类HPAI热点的预测模型.
- 用野生鸟类死亡报告和确认的HPAI事件用于模型开发.
- 为了提供有针对性的监测信息,并加强对HPAI的预防措施.
主要方法:
- 采用了贝叶斯双项模型,并采用了案例交叉设计.
- 野生鸟类死亡率数据和确认的HPAI事件 (2016-2022) 被用作主要数据.
- 统计调整器包括人类,野生鸟类和采样密度;通过十倍交叉验证测试了24种模型.
主要成果:
- 性能最好的模型实现了AUC为0.68,灵敏度为0.47,特异性为0.79.
- 该模型结合了野生鸟类的死亡率和密度,准确地预测了HPAI的空间爆发区域,特别是在2020年以后.
- 预测的疫情主要发生在10月至3月之间,与季节性模式保持一致.
结论:
- 开发的HPAI热点预测模型有效地识别了野生鸟类的高风险区域.
- 该模型可以指导采样工作,提高HPAI监测的效率和及时性.
- 通过预测模型加强监测对于减轻HPAI对野生鸟类种群的影响和防止进一步传播至关重要.
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