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基于野生鸟类栖息地适宜性动态的禽流感爆发时空风险评估

Spatiotemporal risk assessment for avian influenza outbreak based on the dynamics of habitat suitability for wild birds.

作者信息

Yoo Dae-Sung, Lee Kyuyoung, Beatriz Martínez López, Chun Byung Chul, Belkhiria Jaber, Lee Kwang-Nyeong

机构信息

Department of Public Health, Graduate School, Korea University, Seoul, Republic of Korea.

Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, California, USA.

出版信息

Transbound Emerg Dis. 2022 Jul;69(4):e953-e967. doi: 10.1111/tbed.14376. Epub 2021 Nov 17.

Abstract

Highly pathogenic avian influenza (HPAI) has predominantly damaged the poultry industry worldwide. The fundamental prevention and control strategy for HPAI includes early detection and timely intervention enforcement through a systematic surveillance system for wild birds based on the ecological understanding of the dynamics of wild birds' movements. Our study aimed to develop a spatiotemporal risk assessment model for avian influenza (AI) infection in wild birds to empower surveillance information for a contingency strategy. For this purpose, first, we predicted the monthly habitat suitability of seven waterfowl species, using 227,671 Global Positioning System (GPS) tracking records of 562 birds from 2014 to 2018 in the Republic of Korea (ROK). Then, that predicted habitat suitability and 421 coordinates of AI detection sites in wild birds were used to build the risk assessment model. Subsequently, we compared the monthly predicted risk of avian influenza virus (AIv) identification in wild birds between case and non-case poultry farms with HPAI H5N6 outbreak in the ROK between 2016 and 2017. The results reported considerable variation of monthly habitat suitability of seven waterfowls and the impact of predicting AI occurrences in wild birds. The high habitat suitability for spot-billed ducks (contribution rate in November = 40.9%) and mallards (contribution rate in January = 34.3%) significantly contributed to predicting the average risk of AIv identification in wild birds, with high predictive performance [the monthly mean of area under the curve (AUC) = 0.978]. Moreover, our model showed that the averaged risk of identification AI in wild birds was significantly higher in HPAI infected premises, with infected domestic duck holdings exhibiting a significantly higher risk than the chicken farms in November. This study suggests that animal health authority establishes a risk-based HPAI surveillance system grounded on the ecological nature of wild birds to improve the effectiveness of prevention and preparedness of emerging epidemics.

摘要

高致病性禽流感(HPAI)已对全球家禽业造成了主要损害。HPAI的基本防控策略包括通过基于对野生鸟类迁徙动态的生态理解而建立的野生鸟类系统监测系统进行早期检测和及时干预执法。我们的研究旨在开发一种野生鸟类禽流感(AI)感染的时空风险评估模型,以增强用于应急策略的监测信息。为此,首先,我们利用2014年至2018年韩国562只鸟类的227,671条全球定位系统(GPS)跟踪记录,预测了七种水禽物种的月度栖息地适宜性。然后,将预测的栖息地适宜性和野生鸟类AI检测地点的421个坐标用于构建风险评估模型。随后,我们比较了2016年至2017年韩国发生HPAI H5N6疫情的病例和非病例家禽养殖场之间野生鸟类中禽流感病毒(AIv)识别的月度预测风险。结果报告了七种水禽月度栖息地适宜性的显著差异以及预测野生鸟类中AI发生情况的影响。斑嘴鸭(11月贡献率 = 40.9%)和绿头鸭(1月贡献率 = 34.3%)的高栖息地适宜性对预测野生鸟类中AIv识别的平均风险有显著贡献,具有较高的预测性能[曲线下面积(AUC)的月度平均值 = 0.978]。此外,我们的模型表明,在HPAI感染场所,野生鸟类中识别AI的平均风险显著更高,受感染的家鸭养殖场在11月的风险显著高于养鸡场。本研究表明,动物卫生当局应基于野生鸟类的生态特性建立基于风险的HPAI监测系统,以提高新发疫情预防和准备工作的有效性。

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