Abstract
Introduction: Highly pathogenic avian influenza (HPAI), caused by Influenza A virus, is a contagious disease of birds with zoonotic potential. While all bird species can be affected, poultry are highly susceptible to infection. Poultry outbreaks cause significant economic losses, raising concerns for animal welfare and public health. This highlights a need for early warning systems to prevent
outbreaks, for instance by increasing biosecurity especially during periods of high risk.
Methods: Recently we utilized data from the World Organization for Animal Health (WOAH-WAHIS) and developed a time-series model predicting HPAI detections in Europe. The model considers endemic and epidemic components, including within-country, between-country, short-distance, and long-distance transmission. The model and forecasts are public accessible, enabling takeholders to visualize the risk in their respective countries.
Results: Analysis of WOAH-WAHIS data reveals a shift in seasonality, prompting the creation of different models for distinct time periods. The public model has been fitted to more recent data and incorporates long-range transmission and seasonality. The model suggests that 12.2% of HPAI detections are endemic and 87.8% epidemic in nature, with ongoing adjustments based on additional
data.
Discussion: The model's reliance on accurate data underscores the importance of consistent reporting. The decision to make the model public aims to enhance global preparedness for avian influenza, enabling countries in Europe to inform decision-makers and implement preventive measures based on the regularly updated data from WOAH-WAHIS. Similar research focused on Asia is ongoing.
Conclusion: The here described model offers predictive insights into HPAI detections in Europe, supporting outbreak prevention. Adaptations to seasonality and transparent data reporting are crucial for the model's accuracy, emphasizing the need for ongoing collaboration and regular updates to reflect the latest conditions of HPAI cases in Europe.
outbreaks, for instance by increasing biosecurity especially during periods of high risk.
Methods: Recently we utilized data from the World Organization for Animal Health (WOAH-WAHIS) and developed a time-series model predicting HPAI detections in Europe. The model considers endemic and epidemic components, including within-country, between-country, short-distance, and long-distance transmission. The model and forecasts are public accessible, enabling takeholders to visualize the risk in their respective countries.
Results: Analysis of WOAH-WAHIS data reveals a shift in seasonality, prompting the creation of different models for distinct time periods. The public model has been fitted to more recent data and incorporates long-range transmission and seasonality. The model suggests that 12.2% of HPAI detections are endemic and 87.8% epidemic in nature, with ongoing adjustments based on additional
data.
Discussion: The model's reliance on accurate data underscores the importance of consistent reporting. The decision to make the model public aims to enhance global preparedness for avian influenza, enabling countries in Europe to inform decision-makers and implement preventive measures based on the regularly updated data from WOAH-WAHIS. Similar research focused on Asia is ongoing.
Conclusion: The here described model offers predictive insights into HPAI detections in Europe, supporting outbreak prevention. Adaptations to seasonality and transparent data reporting are crucial for the model's accuracy, emphasizing the need for ongoing collaboration and regular updates to reflect the latest conditions of HPAI cases in Europe.
Originalsprog | Engelsk |
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Publikationsdato | 14 nov. 2024 |
Status | Udgivet - 14 nov. 2024 |
Begivenhed | 17th International Symposium on Veterinary Epidemiology and Economics - ICC Sydney, Sydney, Australien Varighed: 11 nov. 2024 → 15 nov. 2024 Konferencens nummer: 17 https://isvee17.com.au/ |
Konference
Konference | 17th International Symposium on Veterinary Epidemiology and Economics |
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Nummer | 17 |
Lokation | ICC Sydney |
Land/Område | Australien |
By | Sydney |
Periode | 11/11/2024 → 15/11/2024 |
Internetadresse |