High pathogenicity avian influenza virus transmission is shaped by inter-specific social network structure.
Dunning, J.; Gamza, A.; Firth, J.; Ashton-Butt, A.; Kao, R. R.; Brown, I.; Ward, A.
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The emergence of zoonotic and epizootic disease has had devastating consequences for human and animal health, including wildlife conservation. Yet, surveillance of multi-host disease systems is particularly challenging due to complex transmission pathways across many species. Social network analysis has been applied to simple transmission systems, but empirical applications to wild, multi-species systems are scarce. Here, we combined high pathogenicity avian influenza (HPAI) viral genomes, a zoonotic virus of pandemic potential, with a large citizen-science database of wild bird co-occurrence to test how multi-species social network structure predicts transmission dynamics. We calculated 1,687 pairwise genetic distances from 356 viral genomes across 33 host species and related these to network metrics. Species pairs with greater co-occurrence frequency showed significantly lower viral genetic distances, over and above relative species association. Our results demonstrate that social network models can predict zoonotic pathogen transmission in wild multi-species systems, enabling targeted disease surveillance.
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