Predicting Avian Flu Outbreaks in Europe Using Machine Learning

Strategic Insights -

Researchers at Heidelberg University have developed a machine learning model that can predict highly pathogenic avian influenza outbreak patterns in Europe with great accuracy. The model, led by epidemiologist Prof. Dr. Joacim Rocklov, combines various indicators such as local temperature, precipitation conditions, and water levels to identify high-risk areas and seasons for bird flu outbreaks. The model was trained using data from 2006 to 2021 and achieved an accuracy of up to 94 percent.

Key Takeaways:

  • The researchers identified local factors such as seasonal temperature, water and vegetation index, and animal density as indicators of avian flu outbreaks in Europe.
  • The machine learning model was trained using data from 2006 to 2021 and achieved an accuracy of up to 94 percent in predicting highly pathogenic avian influenza outbreak patterns.
  • The model combines complex interdependent seasonal and regional variables, including temperature and precipitation conditions, wild bird species, poultry farm density, vegetation composition, and water levels.
  • The research results can be used to design regional surveillance programs throughout Europe and improve early detection of bird flu outbreaks.
  • The model was developed by Prof. Dr. Joacim Rocklov and his team, who stress the importance of combining modeling approaches and targeted data collection for proactive prevention measures.
  • The research was funded by the Alexander von Humboldt Foundation within the Horizon Europe Program of the European Union.
  • The findings were published in the journal "Scientific Reports" on July 17, 2025 (DOI: 10.1038/s41598-025-04624-x).

Statistics:

  • Up to 94 percent accuracy in predicting highly pathogenic avian influenza outbreak patterns in Europe using the machine learning model.
  • 15 years of data from 2006 to 2021 were used to train the machine learning model.
  • December to March were identified as the peak months for highly pathogenic avian influenza outbreaks in Europe.
  • The model combines 7 complex interdependent seasonal and regional variables, including:

+ Temperature and precipitation conditions.

+ Wild bird species.

+ Poultry farm density.

+ Vegetation composition.

+ Water levels.

+ Animal density.

  • The research results highlight the importance of regional surveillance programs in Europe to improve early detection of bird flu outbreaks.

Sources:

  • Heidelberg University news release.
  • M. R. Opata et al., "Predictiveness and drivers of highly pathogenic avian influenza outbreaks in Europe", Scientific Reports (17 July 2025) DOI: 10.1038/s41598-025-04624-x.