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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ftpubmed:oai:pubmedcentral.nih.gov:9143584 2023-05-15T15:46:03+02:00 Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands Schreuder, Janneke de Knegt, Henrik J. Velkers, Francisca C. Elbers, Armin R. W. Stahl, Julia Slaterus, Roy Stegeman, J. Arjan de Boer, Willem F. 2022-05-06 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143584/ http://www.ncbi.nlm.nih.gov/pubmed/35631070 https://doi.org/10.3390/pathogens11050549 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143584/ http://www.ncbi.nlm.nih.gov/pubmed/35631070 http://dx.doi.org/10.3390/pathogens11050549 © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). CC-BY Pathogens Article Text 2022 ftpubmed https://doi.org/10.3390/pathogens11050549 2022-06-05T00:50:40Z 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. Text brant goose Branta bernicla PubMed Central (PMC) Olor ENVELOPE(88.531,88.531,69.600,69.600) Pathogens 11 5 549 |
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English |
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Article Schreuder, Janneke de Knegt, Henrik J. Velkers, Francisca C. Elbers, Armin R. W. Stahl, Julia Slaterus, Roy Stegeman, J. Arjan de Boer, Willem F. Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands |
topic_facet |
Article |
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 |
Text |
author |
Schreuder, Janneke de Knegt, Henrik J. Velkers, Francisca C. Elbers, Armin R. W. Stahl, Julia Slaterus, Roy Stegeman, J. Arjan de Boer, Willem F. |
author_facet |
Schreuder, Janneke de Knegt, Henrik J. Velkers, Francisca C. Elbers, Armin R. W. Stahl, Julia Slaterus, Roy Stegeman, J. Arjan de Boer, Willem F. |
author_sort |
Schreuder, Janneke |
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 |
publisher |
MDPI |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143584/ http://www.ncbi.nlm.nih.gov/pubmed/35631070 https://doi.org/10.3390/pathogens11050549 |
long_lat |
ENVELOPE(88.531,88.531,69.600,69.600) |
geographic |
Olor |
geographic_facet |
Olor |
genre |
brant goose Branta bernicla |
genre_facet |
brant goose Branta bernicla |
op_source |
Pathogens |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143584/ http://www.ncbi.nlm.nih.gov/pubmed/35631070 http://dx.doi.org/10.3390/pathogens11050549 |
op_rights |
© 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/pathogens11050549 |
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Pathogens |
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11 |
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5 |
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549 |
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