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H5N1 highly pathogenic avian influenza (HPAI) has been recently circulating in previously unseen patterns. As the underlying causes are uncertain, we need a better understanding of the drivers of virus circulation, as they underpin the spread, and inform about the impact of HPAI. We combine infection and bird population data, using Random Forest models to quantify the relationship between spatially aggregated values of possible infection risk factors and viral phylogenetic data from HPAI outbreaks to define scales of interactions that influence virus circulation. As genetic proximity is correlated to transmission proximity, the genetic relationships of sequences and relevant variables indicate the scale and resolution of interactions driving transmission. We used H5N1 Sequences from Great Britain divided into two periods: Dec 2020-May 2022, and Jun-Oct 2022. Final model with selected spatial scales shows that for the first period game bird abundance and geographical distance were the strongest predictors of genetic distance, and for the second period, these were geographical distance, collection date and farm count. Variables related to infections have different spatial scales for analysed periods, while variables describing the environment have more consistent scales. The differences in predictors for the periods bears further investigation, however may be related to differences in the virus, areas of spread, or seasonal factors. This is the first phylogenetic study indicating possible role for game birds in the circulation of HPAI and supports experimental studies but must be viewed with caution as our study does not indicate direct causation. We show that the scale of spatial aggregation is variable dependent and reflects the scale of underlying processes. In the future our findings may be used to study dynamics of HPAI and design targeted surveillance and intervention strategies.

Diagnostic tests which can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been therefore paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. Here, we follow other recent proposals to further refine this concept, by using machine learning to assess the situational risk under which a diagnostic test is applied to augment its interpretation . We develop this to predict the occurrence of breakdowns of cattle herds due to bovine tuberculosis, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected by the skin test, improves by over 16 percentage points. While many risk factors are associated with increased risk of becoming infected, of note are several factors which suggest that, in some herds there is a higher risk of infection going undetected, including effects that are correlated to the veterinary practice conducting the test, and number of livestock moved off the herd.