Whole viral genome sequences (WGS) derived from clinical material and cultured isolates are frequently determined during an outbreak. To support biosecurity and public health actions, what is lagging is the epidemiological insight inferred through WGS across many samples and other features inc. wind dispersion. Outbreak genomes are reported diagrammatically using a phylogenetic tree. This is problematic as branches arising from closely related isolates with few SNP differences are rarely informative. A method gaining acceptance is “median joining” (MJ), a network of common descent created using observed, and inferred SNPs. This enables epidemiological clusters to be identified with closely related sequences being interpreted as direct infections between patients or farms. However, generating MJ networks is based purely on the observed genetic changes and not epidemiological data. One method that incorporates epidemiology data is the BEAST extension SCOTTI that uses patient and/or infected premise sampling times and periods of infectiousness to provide probabilities of direct and indirect genetic descent (De Maio et al., 2018). In this presentation we illustrate the use of SCOTTI to generate networks from WGS (>80) from a highly pathogenic avian influenza (HPAI) outbreak on poultry sheds across three poultry farms Victoria, 2020. The resulting network enabled us to infer there was only a single spillover event – most probably from wildbirds - and to define the spread of the virus between sheds on the three affected farms. Furthermore, we were able to show that the network was consistent with field epidemiological inference and atmospheric dispersion modelling to demonstrate that transmission between the first and second IP occurred via farm-to-farm wind dispersion (FFWD). This is the first time that FFWD has been independently corroborated and shows the potential for Bayesian genomic networks to advances the understanding of the potential of HPAI transmission via infected dust aerosols.