Spatial patterns of avian influenza in wild birds from Denmark, 2006-2020
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning
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Spatial patterns of avian influenza in wild birds from Denmark, 2006-2020. / Kjær, Lene Jung; Hjulsager, Charlotte; Larsen, Lars; Boklund, Anette; Halasa, Tariq; Ward, Michael P.; Kirkeby, Carsten.
Society for Veterinary Epidemiology and Preventive Medicine: Proceedings: Online, 24-26 March 2021. Society for Veterinary Epidemiology and Preventive Medicine, 2021.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning
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TY - GEN
T1 - Spatial patterns of avian influenza in wild birds from Denmark, 2006-2020
AU - Kjær, Lene Jung
AU - Hjulsager, Charlotte
AU - Larsen, Lars
AU - Boklund, Anette
AU - Halasa, Tariq
AU - Ward, Michael P.
AU - Kirkeby, Carsten
PY - 2021/3/11
Y1 - 2021/3/11
N2 - We investigated factors affecting avian influenza virus (AIV) detections in Danish wild birds using data from the passive and active AIV surveillance in wild birds from 2006-2020. We used this data and machine learning (ML) algorithms along with landscape and environmental variables to develop predictive models of AIV occurrence in Denmark. We furthermore assessed potential accessibility bias in the passive AIV surveillance data submitted by the public. The passive AIV surveillance data was biased regarding accessibility to areas compared to random locations within Denmark. ML models differed in their predictive power and were used to predict the risk of AIV presence throughout Denmark. Our results suggest that landscape variables may affect AIV presence and enabled us to create risk maps of AIV occurrence in Danish wild birds. This may aid future targeted surveillance efforts within Denmark.
AB - We investigated factors affecting avian influenza virus (AIV) detections in Danish wild birds using data from the passive and active AIV surveillance in wild birds from 2006-2020. We used this data and machine learning (ML) algorithms along with landscape and environmental variables to develop predictive models of AIV occurrence in Denmark. We furthermore assessed potential accessibility bias in the passive AIV surveillance data submitted by the public. The passive AIV surveillance data was biased regarding accessibility to areas compared to random locations within Denmark. ML models differed in their predictive power and were used to predict the risk of AIV presence throughout Denmark. Our results suggest that landscape variables may affect AIV presence and enabled us to create risk maps of AIV occurrence in Danish wild birds. This may aid future targeted surveillance efforts within Denmark.
M3 - Article in proceedings
SN - 0948073608
BT - Society for Veterinary Epidemiology and Preventive Medicine
PB - Society for Veterinary Epidemiology and Preventive Medicine
ER -
ID: 339127251