Prediction models show differences in highly pathogenic avian influenza outbreaks in Japan and South Korea compared to Europe

Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Org...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 6783 - 14
Hlavní autoři:	Kjær, Lene Jung, Kirkeby, Carsten Thure, Boklund, Anette Ella, Hjulsager, Charlotte Kristiane, Fox, Anthony D., Ward, Michael P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 25.02.2025 Nature Publishing Group Nature Portfolio
Témata:	631/114 692/699 Animal welfare Animals Asia Avian flu Birds - virology Disease occurrence Disease Outbreaks - veterinary Early-warning modelling Endemic-epidemic modelling Epidemic models Epidemics Europe - epidemiology Highly pathogenic avian influenza Humanities and Social Sciences Influenza Influenza A Virus, H5N1 Subtype - pathogenicity Influenza A Virus, H5N8 Subtype - pathogenicity Influenza in Birds - epidemiology Influenza in Birds - virology Japan - epidemiology multidisciplinary Outbreaks Poultry - virology Prediction models Public health Republic of Korea - epidemiology Science Science (multidisciplinary) Seasonal variations Seasons Time series Warning systems Wild and domestic birds Japan Europe Republic of Korea South Korea Highly pathogenic avian influenza Disease occurrence Wild and domestic birds Asia Endemic-epidemic modelling Early-warning modelling
ISSN:	2045-2322, 2045-2322
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Abstract	Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020–2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world.
AbstractList	Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020-2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world.Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020-2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world. Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020–2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world. Abstract Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020–2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world.
ArticleNumber	6783
Author	Kirkeby, Carsten Thure Fox, Anthony D. Ward, Michael P. Boklund, Anette Ella Hjulsager, Charlotte Kristiane Kjær, Lene Jung
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Snippet	Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to... Abstract Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks...
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StartPage	6783
SubjectTerms	631/114 692/699 Animal welfare Animals Asia Avian flu Birds - virology Disease occurrence Disease Outbreaks - veterinary Early-warning modelling Endemic-epidemic modelling Epidemic models Epidemics Europe - epidemiology Highly pathogenic avian influenza Humanities and Social Sciences Influenza Influenza A Virus, H5N1 Subtype - pathogenicity Influenza A Virus, H5N8 Subtype - pathogenicity Influenza in Birds - epidemiology Influenza in Birds - virology Japan - epidemiology multidisciplinary Outbreaks Poultry - virology Prediction models Public health Republic of Korea - epidemiology Science Science (multidisciplinary) Seasonal variations Seasons Time series Warning systems Wild and domestic birds
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Title	Prediction models show differences in highly pathogenic avian influenza outbreaks in Japan and South Korea compared to Europe
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Volume	15
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