Suppr超能文献

法国高致病性禽流感的空间风险建模:育肥鸭场活动至关重要。

Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters.

作者信息

Artois Jean, Vergne Timothée, Fourtune Lisa, Dellicour Simon, Scoizec Axelle, Le Bouquin Sophie, Guérin Jean-Luc, Paul Mathilde C, Guinat Claire

机构信息

Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.

Unité Productions Végétales, Centre Wallon de Recherches Agronomiques (CRAW), Gembloux, Belgium.

出版信息

PLoS One. 2025 Feb 4;20(2):e0316248. doi: 10.1371/journal.pone.0316248. eCollection 2025.

Abstract

In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. Our findings indicate that the most explanatory spatial predictor variables were related to fattening duck movements prior to the epidemic, which should be considered as indicators of farm operational status, e.g., whether they are active or not. Moreover, we found that considering the operational status of duck houses in nearby municipalities is essential for accurately predicting the risk of future HPAI infection. Our results also show that the density of fattening duck houses could be used as a valuable alternative predictor of the spatial distribution of outbreaks per municipality, as this data is generally more readily available than data on movements between houses. Accurate data regarding poultry farm densities and movements is critical for developing accurate mathematical models of HPAI virus spread and for designing effective prevention and control strategies for HPAI. Finally, our study identifies the highest risk areas for HPAI infection in southwest and northwest France, which is valuable for informing national risk-based strategies and guiding increased surveillance efforts in these regions.

摘要

在本研究中,我们对法国高致病性禽流感(HPAI)感染的关键空间风险因素和预测风险地图进行了全面分析,重点关注2016 - 17年和2020 - 21年的疫情波。我们的研究结果表明,最具解释力的空间预测变量与疫情前育肥鸭的移动有关,这应被视为养殖场运营状况的指标,例如养殖场是否活跃。此外,我们发现考虑附近市镇鸭舍的运营状况对于准确预测未来HPAI感染风险至关重要。我们的结果还表明,育肥鸭舍的密度可作为每个市镇疫情空间分布的一个有价值的替代预测指标,因为该数据通常比鸭舍间移动的数据更容易获取。关于家禽养殖场密度和移动的准确数据对于建立HPAI病毒传播的精确数学模型以及设计有效的HPAI预防和控制策略至关重要。最后,我们的研究确定了法国西南部和西北部HPAI感染的最高风险区域,这对于制定基于国家风险的策略以及指导在这些地区加强监测工作具有重要价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0318/11793745/ff6f9a76ad2d/pone.0316248.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验