使用自适应建模框架来识别野生-家禽界面的禽流感溢出风险
Diann J Prosser1, Cody M Kent2,3,4, Jeffery D Sullivan5
1U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD, 20708, USA. dprosser@usgs.gov.
Scientific reports
|June 20, 2024
概括
高致病性禽流感 (HPAI) 传播模式对于准备工作至关重要. 这项研究为连接的美国开发了高分辨率的空间和时间风险模型,整合了野生鸟类迁移和家禽养殖数据.
科学领域:
- 兽医流行病学 兽医流行病学
- 疾病建模 疾病建模
- 野生动物生态学野生动物生态学
背景情况:
- 野生和家禽的接口是高度致病性禽流感 (HPAI) 病毒出现和传播的关键点.
- 最近大规模的HPAI H5N1 Clade 2.3.4.4b入侵北美突出了迫切需要数据驱动的预测模型.
- 现有的模型往往缺乏高分辨率和全面的数据集成,这是有效的应急响应和规划所需的.
研究的目的:
- 开发高分辨率的空间和时间传输风险模型,用于连接美国的HPAI.
- 将野生鸟类迁徙模式,特有禽流感病毒流行率和家禽养殖数据纳入风险评估.
- 为适应性建模提供框架,为新型疫情的准备和应对策略提供信息.
主要方法:
- 模拟HPAI传播风险,使用每周物种级别野生水禽 (Anatidae) 丰富度和特有低致病性禽流感病毒流行率.
- 按商品类型和相对生物安全风险在3公里和县空间尺度上的合并家禽养殖场密度.
- 在2022-2023年HPAI禽类疫情中使用野生鸟类引入事件的植物遗传学分析验证模型性能.
主要成果:
- 确定了HPAI溢出风险的显著空间和时间变化,受水禽迁徙周期和家禽生产密度的影响.
- 由于集中家禽业务,一些地点在全年表现出持续的高风险.
- 模型验证证实在预测与野生鸟类移动相关的HPAI引入事件方面表现强.
结论:
- 开发的模型提供了一个强大的,数据驱动的方法来评估和预测整个美国的HPAI传播风险.
- 模块化设计允许适应不断变化的条件,新数据和定制风险评估.
- 这项研究提供了必要的工具,以加强对新出现的禽流感威胁的准备和应对策略.
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