Sign in Subscribe
Avian influenza

Investigating Environmental Determinants and Spatiotemporal Dynamics of Highly Pathogenic Avian Influenza H5N1 Outbreaks in India

Strategic Insights

— 2 min read

A new study published in Scientific Reports offers a comprehensive evaluation of the spatial and temporal dynamics of Highly Pathogenic Avian Influenza (HPAI) outbreaks in India. The research employed a multidisciplinary approach that integrated geospatial analysis, machine learning modeling, remote sensing, and environmental risk factor assessment. The findings identified critical environmental variables such as air temperature, enhanced vegetation index (EVI), leaf area index (LAI), potential evapotranspiration (PET), rain precipitation rate, specific humidity, and wind speed as significant predictors of HPAI risk. The study also generated a risk map and estimated basic reproduction numbers (R) to indicate that the southern and north-eastern regions of India are vulnerable to HPAI.

Key Takeaways:

  • The study investigated the spatiotemporal dynamics of HPAI outbreaks in India using a multidisciplinary approach that integrated geospatial analysis, machine learning modeling, remote sensing, and environmental risk factor assessment.
  • Critical environmental variables such as air temperature, EVI, LAI, PET, rain precipitation rate, specific humidity, and wind speed were identified as significant predictors of HPAI risk.
  • The study generated a risk map and estimated basic reproduction numbers (R) to indicate that the southern and north-eastern regions of India are vulnerable to HPAI.
  • The findings provide a holistic perspective essential for effective surveillance, strategic planning for resource allocation, and policy development for disease management to safeguard both avian and human populations from the looming threat of influenza outbreaks.
  • The study adopted ensemble technology by integrating high-performing random forest (RF) and classification tree (CT) models for HPAI risk assessment.
  • The incidence map provides a powerful visual representation, offering valuable insights into the distribution and concentration of HPAI outbreaks across the country.
  • The study identifies a peak in HPAI outbreaks during the winter and spring seasons.

Statistics:

  • 2025: The year the study was published.
  • 15(1): The volume and issue number of Scientific Reports where the study was published.
  • 36132: The article number of the study in Scientific Reports.
  • 95.6%: The accuracy of the ensemble model in predicting HPAI risk.
  • 85%: The accuracy of the RF model in predicting HPAI risk.
  • 88%: The accuracy of the CT model in predicting HPAI risk.
  • 25-30°C: The optimal temperature range for the activity of HPAI viruses.
  • 30-60%: The optimal relative humidity range for the activity of HPAI viruses.

Sources:

  • Investigating environmental determinants and spatiotemporal dynamics of highly pathogenic avian influenza H5N1 outbreaks in India through machine learning. Scientific Reports, 2025;15(1):36132.
  • Nature Publishing Group - www.nature.com/
  • Scientific Reports - www.nature.com/srep/

Read more