Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** avian influenza viruses (AIV) are…

Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** avian influenza viruses (AIV) are endemic in certain wild bird populations, and from there, spill over into domestic birds and human populations where they cause outbreaks of severe disease. Some strains of AIV pose a greater threat than others, because of a combination of their pathogenicity, geographic and host range; exemplified in recent years by four major incursions of H5 and H7 subtype viruses. Surveillance backed up by laboratory assessment of these traits underpin attempts to risk assess and predict behaviour of a highly mutable virus. This surveillance effort, coupled with the development of sequencing technology has led to an exponential rise inthe amount of viral sequence data, both at consensus level and (via next generation sequencing [NGS]) at quasi-species level. In parallel, recent advances in phylodynamic modelling techniques (including structured coalescents, multi-species birth-death models and integration of time-dependent predictors in generalised linear model phylogeographic approaches have provided methods to fully use this wealth of data. Accordingly, we hypothesise that, for the first time, properly parameterised mathematical models of viral evolution with genuinepredictive value can now be constructed. We will generate these models and produce spread and host range risk maps for specific AIV strains that can be used to inform vaccination and other control strategies.