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降低风险:利用鸟类迁徙经验模型预测H5N1禽流感传播

Mitigating Risk: Predicting H5N1 Avian Influenza Spread with an Empirical Model of Bird Movement.

作者信息

McDuie Fiona, T Overton Cory, A Lorenz Austen, L Matchett Elliott, L Mott Andrea, A Mackell Desmond, T Ackerman Joshua, De La Cruz Susan E W, Patil Vijay P, Prosser Diann J, Takekawa John Y, Orthmeyer Dennis L, Pitesky Maurice E, Díaz-Muñoz Samuel L, Riggs Brock M, Gendreau Joseph, Reed Eric T, Petrie Mark J, Williams Chris K, Buler Jeffrey J, Hardy Matthew J, Ladman Brian S, Legagneux Pierre, Bêty Joël, Thomas Philippe J, Rodrigue Jean, Lefebvre Josée, Casazza Michael L

机构信息

U.S. Geological Survey Western Ecological Research Center, Dixon Field Station, 800 Business Park Drive Ste D, Dixon, CA, USA.

San Jose State University Research Foundation Moss Landing Marine Laboratories, Moss Landing, CA, USA.

出版信息

Transbound Emerg Dis. 2024 Jul 18;2024:5525298. doi: 10.1155/2024/5525298. eCollection 2024.

Abstract

Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (December 2023), poses a serious global threat to animals and public health. We combined a large, long-term waterfowl GPS tracking dataset (16 species) with on-ground disease surveillance data (county-level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted nonmigratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multispecies tracking to highlight potential high-risk disease spread locations and more effectively manage outbreaks.

摘要

了解病毒传播的时间和分布对于全球商业和野生动物生物安全管理至关重要。一种高致病性禽流感病毒(HPAIv)在全球范围内爆发,影响了全球约600种鸟类和哺乳动物物种,以及北美地区超过8300万只鸟类(2023年12月),对动物和公共卫生构成了严重的全球威胁。我们将一个大型的长期水鸟GPS跟踪数据集(16个物种)与地面疾病监测数据(县级HPAIv检测)相结合,创建了一个新的实证模型,该模型评估了时空暴露情况,并预测了截至2022年HPAIv通过GPS跟踪的迁徙水鸟的未来传播和潜在到达情况。我们的模型对野生水鸟有效,但预测滞后于家禽养殖场和一些受影响严重的非迁徙物种中的HPAIv检测。我们的结果为应用生物安全管理和规划提供了关键的提前预警,并证明了广泛的多物种跟踪对于突出潜在的高风险疾病传播地点和更有效地管理疫情的重要性和实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bebe/12016750/948c303fe636/TBED2024-5525298.001.jpg

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