Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands
Highly pathogenic avian influenza viruses’ (HPAIVs) transmission from wild birds to poultry occurs globally, threatening animal and public health. To predict the HPAI outbreak risk in relation to wild bird densities and land cover variables, we performed a case‐control study of 26 HPAI outbreaks (ca...
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ftunivwagenin:oai:library.wur.nl:wurpubs/597152 2024-04-28T08:14:52+00:00 Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands Schreuder, J. de Knegt, Henjo Velkers, F.C. Elbers, A.R.W. Stahl, Julia Slaterus, Roy Stegeman, Arjan de Boer, Fred 2022 application/pdf https://research.wur.nl/en/publications/wild-bird-densities-and-landscape-variables-predict-spatial-patte https://doi.org/10.3390/pathogens11050549 en eng https://edepot.wur.nl/569551 https://research.wur.nl/en/publications/wild-bird-densities-and-landscape-variables-predict-spatial-patte doi:10.3390/pathogens11050549 https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research Pathogens 11 (2022) 5 ISSN: 2076-0817 Life Science Article/Letter to editor 2022 ftunivwagenin https://doi.org/10.3390/pathogens11050549 2024-04-03T14:51:38Z Highly pathogenic avian influenza viruses’ (HPAIVs) transmission from wild birds to poultry occurs globally, threatening animal and public health. To predict the HPAI outbreak risk in relation to wild bird densities and land cover variables, we performed a case‐control study of 26 HPAI outbreaks (cases) on Dutch poultry farms, each matched with four comparable controls. We trained machine learning classifiers to predict outbreak risk with predictors analyzed at different spatial scales. Of the 20 best explaining predictors, 17 consisted of densities of water‐associated bird species, 2 of birds of prey, and 1 represented the surrounding landscape, i.e., agricultural cover. The spatial distribution of mallard (Anas platyrhynchos) contributed most to risk prediction, followed by mute swan (Cygnus olor), common kestrel (Falco tinnunculus) and brant goose (Branta bernicla). The model successfully distinguished cases from controls, with an area under the receiver operating characteristic curve of 0.92, indicating accurate prediction of HPAI outbreak risk despite the limited numbers of cases. Different classification algorithms led to similar predictions, demonstrating robustness of the risk maps. These analyses and risk maps facilitate insights into the role of wild bird species and support prioritization of areas for surveillance, biosecurity measures and establishments of new poultry farms to reduce HPAI outbreak risks. Article in Journal/Newspaper brant goose Branta bernicla Wageningen UR (University & Research Centre): Digital Library Pathogens 11 5 549 |
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Open Polar |
collection |
Wageningen UR (University & Research Centre): Digital Library |
op_collection_id |
ftunivwagenin |
language |
English |
topic |
Life Science |
spellingShingle |
Life Science Schreuder, J. de Knegt, Henjo Velkers, F.C. Elbers, A.R.W. Stahl, Julia Slaterus, Roy Stegeman, Arjan de Boer, Fred Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
topic_facet |
Life Science |
description |
Highly pathogenic avian influenza viruses’ (HPAIVs) transmission from wild birds to poultry occurs globally, threatening animal and public health. To predict the HPAI outbreak risk in relation to wild bird densities and land cover variables, we performed a case‐control study of 26 HPAI outbreaks (cases) on Dutch poultry farms, each matched with four comparable controls. We trained machine learning classifiers to predict outbreak risk with predictors analyzed at different spatial scales. Of the 20 best explaining predictors, 17 consisted of densities of water‐associated bird species, 2 of birds of prey, and 1 represented the surrounding landscape, i.e., agricultural cover. The spatial distribution of mallard (Anas platyrhynchos) contributed most to risk prediction, followed by mute swan (Cygnus olor), common kestrel (Falco tinnunculus) and brant goose (Branta bernicla). The model successfully distinguished cases from controls, with an area under the receiver operating characteristic curve of 0.92, indicating accurate prediction of HPAI outbreak risk despite the limited numbers of cases. Different classification algorithms led to similar predictions, demonstrating robustness of the risk maps. These analyses and risk maps facilitate insights into the role of wild bird species and support prioritization of areas for surveillance, biosecurity measures and establishments of new poultry farms to reduce HPAI outbreak risks. |
format |
Article in Journal/Newspaper |
author |
Schreuder, J. de Knegt, Henjo Velkers, F.C. Elbers, A.R.W. Stahl, Julia Slaterus, Roy Stegeman, Arjan de Boer, Fred |
author_facet |
Schreuder, J. de Knegt, Henjo Velkers, F.C. Elbers, A.R.W. Stahl, Julia Slaterus, Roy Stegeman, Arjan de Boer, Fred |
author_sort |
Schreuder, J. |
title |
Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
title_short |
Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
title_full |
Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
title_fullStr |
Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
title_full_unstemmed |
Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
title_sort |
wild bird densities and landscape variables predict spatial patterns in hpai outbreak risk across the netherlands |
publishDate |
2022 |
url |
https://research.wur.nl/en/publications/wild-bird-densities-and-landscape-variables-predict-spatial-patte https://doi.org/10.3390/pathogens11050549 |
genre |
brant goose Branta bernicla |
genre_facet |
brant goose Branta bernicla |
op_source |
Pathogens 11 (2022) 5 ISSN: 2076-0817 |
op_relation |
https://edepot.wur.nl/569551 https://research.wur.nl/en/publications/wild-bird-densities-and-landscape-variables-predict-spatial-patte doi:10.3390/pathogens11050549 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
op_doi |
https://doi.org/10.3390/pathogens11050549 |
container_title |
Pathogens |
container_volume |
11 |
container_issue |
5 |
container_start_page |
549 |
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1797580744858533888 |