Abstract
The emergence of zoonotic and epizootic diseases 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 linked viral genetic distance, 20,103 pairwise comparisons between 214 unique genomes from 172 dyads of 20 host species, to co-occurrence network metrics for those species. Both relative species association and raw co-occurrence frequency predicted lower maximum viral genetic divergence, more similar viruses between more associated species, beyond what would be expected through random mixing and independently of sequencing effort. Time and space between samples were also strong predictors of genetic similarity. Our results suggest that network models can be used to detect pathogen transmission through communities of wild birds, offering real prospects for wildlife disease surveillance and prediction.
Competing Interest Statement
The authors have declared no competing interest.
Source:
Link: https://www.biorxiv.org/content/10.1101/2025.06.17.659947v5
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