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Spatiotemporal Risk Assessment of H5 Avian Influenza in China: An Interpretable Machine Learning Approach to Uncover Multi-Scale Drivers.

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Název: Spatiotemporal Risk Assessment of H5 Avian Influenza in China: An Interpretable Machine Learning Approach to Uncover Multi-Scale Drivers.
Autoři: Wang, Xinyi, Xu, Yihui, Xi, Xi
Zdroj: Animals (2076-2615); Aug2025, Vol. 15 Issue 16, p2447, 20p
Témata: AVIAN influenza, RISK assessment, SCIENTIFIC observation, POULTRY farm management, MACHINE learning, CLIMATIC zones
Geografický termín: CHINA
Abstrakt: Simple Summary: Avian influenza, particularly the H5 subtypes, poses a continuous threat to poultry farming and public health. Predicting where and when outbreaks might occur is difficult, especially in a large and diverse country like China. To tackle this challenge, we developed a smart computer model using artificial intelligence (XGBoost) to analyze over two decades of H5 avian influenza outbreak data. We combined this with multi-source information, including satellite data on poultry density, climate zones, wild bird habitats, and daily weather conditions. Our model successfully identified the key factors driving outbreak risk with high accuracy. We found that the density of poultry farms and specific climate zones are the most important background factors, while daily weather changes act as triggers. Notably, we discovered a surprising interaction: hot summer temperatures, usually considered low-risk, can become a significant danger in areas with many poultry farms. These findings were used to create a high-resolution risk map of China, highlighting hotspots for targeted surveillance. This research provides a valuable tool for developing better early warning systems to protect both animal and human health from the threat of avian influenza. Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation R 2 = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Simple Summary: Avian influenza, particularly the H5 subtypes, poses a continuous threat to poultry farming and public health. Predicting where and when outbreaks might occur is difficult, especially in a large and diverse country like China. To tackle this challenge, we developed a smart computer model using artificial intelligence (XGBoost) to analyze over two decades of H5 avian influenza outbreak data. We combined this with multi-source information, including satellite data on poultry density, climate zones, wild bird habitats, and daily weather conditions. Our model successfully identified the key factors driving outbreak risk with high accuracy. We found that the density of poultry farms and specific climate zones are the most important background factors, while daily weather changes act as triggers. Notably, we discovered a surprising interaction: hot summer temperatures, usually considered low-risk, can become a significant danger in areas with many poultry farms. These findings were used to create a high-resolution risk map of China, highlighting hotspots for targeted surveillance. This research provides a valuable tool for developing better early warning systems to protect both animal and human health from the threat of avian influenza. Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation R 2 = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. [ABSTRACT FROM AUTHOR]
ISSN:20762615
DOI:10.3390/ani15162447