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<p>Avian influenza viruses (AIV) pose a major zoonotic threat with pandemic potential. Waterbirds facilitate AIV spillovers into farm animals and humans through exposure and virus reassortment. Here, we propose waterbird activity entropy (WAE), an indicator of waterbird activity intensity based on monthly distributions of 779 species worldwide. WAE demonstrated high explanative power (AUC = 0.87 ± 0.001) for global avian influenza cases, particularly for H5N1, revealing the potential of WAE for identifying AIV exposure hotspots which cover 14% of global land area. Notably, the AIV exposure hotspots in the USA, EU, China, and India contain 52% of the globally exposed human population, 41% cattle, and 51% poultry. Despite reporting &lt;1% of global cases, sub-Saharan Africa contains &gt;300 Mha of hotspots area (15% globally), highlighting considerable surveillance gaps. This WAE-based framework enhances AIV risk assessment by incorporating waterbird residency time, offering critical insights for anticipating AIV emergence and improving surveillance.</p>

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Mapping global avian influenza risk patterns through waterbird activity entropy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月7日

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  • Yuzhe Li,
  • Yuxin Qiao,
  • Yue Zhan,
  • Jinwei Dong,
  • Mariëlle van Toor,
  • Jonas Waldenström,
  • A. Townsend Peterson,
  • Qiang Zhang,
  • Zhichao Li,
  • Weipan Lei,
  • Fanshu Du,
  • Juan Pu,
  • Dayan Wang,
  • Xiangming Xiao

摘要

Avian influenza viruses (AIV) pose a major zoonotic threat with pandemic potential. Waterbirds facilitate AIV spillovers into farm animals and humans through exposure and virus reassortment. Here, we propose waterbird activity entropy (WAE), an indicator of waterbird activity intensity based on monthly distributions of 779 species worldwide. WAE demonstrated high explanative power (AUC = 0.87 ± 0.001) for global avian influenza cases, particularly for H5N1, revealing the potential of WAE for identifying AIV exposure hotspots which cover 14% of global land area. Notably, the AIV exposure hotspots in the USA, EU, China, and India contain 52% of the globally exposed human population, 41% cattle, and 51% poultry. Despite reporting <1% of global cases, sub-Saharan Africa contains >300 Mha of hotspots area (15% globally), highlighting considerable surveillance gaps. This WAE-based framework enhances AIV risk assessment by incorporating waterbird residency time, offering critical insights for anticipating AIV emergence and improving surveillance.