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Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry

机译:水禽发生和停留时间作为北美家禽H5和H7禽流感的指标

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Avian influenza (AI) affects wild aquatic birds and poses hazards to human health, food security, and wildlife conservation globally. Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. The study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks.
机译:禽流感(AI)影响野生水生鸟,并在全球对人类健康,粮食安全和野生动物保护造成危害。因此,有人识别需要新的方法和工具,以帮助量化野生鸟宿主和商业家禽之间的动态相互作用。我们使用卫星标记的水禽,我们在综合动物运动和疾病分布建模框架下施加贝叶斯联合等级模型,以同时模拟物种分布,居住时间,迁移时间和疾病发生概率。我们的结果表明,迁徙的水禽与北美的AI发生阳性相关,这就像水禽发生的概率或停留时间在给定的位置一样,商业家禽AI爆发的可能性也是如此。分析还表明,在我们观察到的水禽北方迁移期间,AI发生概率最大,在南方迁移期间较少。在方法上,我们发现,当在野生动物农业界面上建模疾病系统的不同方面时,必须解决多尺度空间模式,以避免从不正确的空间尺度评估的模式中错误地推断出疾病过程或疾病环境关系。该研究提供了对迁徙水禽生态和AI疾病动态的重要见解,这有助于更好地为未来爆发做准备。

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