Background
The spillover of avian influenza viruses (AIVs) presents a significant global public health threat, leading to unpredictable and recurring pandemics. Current pandemic assessment tools suffer from deficiencies in terms of timeliness, capability for automation, and ability to generate risk estimates for multiple subtypes in the absence of documented human cases.
Methods
To address these challenges, we created an integrated database encompassing global AIV-related data from 1981 to 2022. This database enabled us to estimate the rapid expansion of spatial range and host diversity for specific AIV subtypes, alongside their increasing prevalence in hosts that have close contact with humans. These factors were used as early-warning signals for potential AIV spillover. We analyzed spillover patterns of AIVs using machine learning models, spatial Durbin models, and phylogenetic analysis.
Findings
Our results indicate a high potential for future spillover by subtypes H3N1, H4N6, H5N2, H5N3, H6N2, and H11N9. Additionally, we identified a significant risk for re-emergence by subtypes H5N1, H5N6, H5N8, and H9N2. Furthermore, our analysis highlighted 12 key strains of H9N2 as internal genetic donors for human adaptation in AIVs, demonstrating the crucial role of H9N2 in facilitating AIV spillover.
Conclusions
These findings provide a foundation for rapidly identifying high-risk subtypes, thus optimizing resource allocation in vaccine manufacture. They also underscore the potential significance of reducing the prevalence of H9N2 as a complementary strategy to mitigate chances of AIV spillovers.