New model pinpoints bird flu hotspots with 87% accuracy, revealing major surveillance gaps
Researchers have developed a predictive tool that maps where avian influenza is most likely to emerge by tracking waterbird movement patterns. The framework identifies 14% of global land area as high-risk zones—including regions in Africa with virtually no surveillance infrastructure—offering governments and livestock producers a roadmap for targeted disease prevention.
Originaltitel: Mapping global avian influenza risk patterns through waterbird activity entropy
<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 <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.</p>