Using the example of highly pathogenic avian influenza (HPAI) in Great Britain, we combine a deterministic model of disease spread with empirical data of epidemiologically relevant on-to-farm movements, to investigate the importance of considering coupled interactions across population scales. A deterministic S-E-I-R model was used to simulate the within-flock transmission of HPAI, enabling the simulated build-up of infectious faeces within the poultry house to be tracked over time. A measure of the relative transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with temporally explicit catching-team movement data. Larger flocks tended to have greater levels of on-to-farm movement. However, where density-dependent contact was assumed, faster detection led to a decreased opportunity for catching-team visits to coincide with an outbreak, and thus maximum TR-levels were found for mid-range flock sizes (~25,000-35,000 birds). When assessing all factors simultaneously using multivariable linear regression, those related to the pattern of on-to-farm movements had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network data to inform a measure of between-farm connectivity identified a large fraction of farms (28%) that had both a high transmission risk and a high potential impact at the between farm level. These results highlight the importance of considering cross-scale dynamics at the farm-level; the interactions considered have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone. Together with further knowledge of the relative importance of transmission risk and impact, these results have implications for improved targeting of control measures.