Veterinary Medicine International surveillance programme. The model showed that, despite intensiﬁcation of possum control strategies over time, prox- imity to forest parks (a principal possum habitat in this area) remained a signiﬁcant predictor of the number of conﬁrmed cases of TB detected per farm per year. Their analyses showed a signiﬁcant, threefold increase in bovine TB risk in dairy cattle relative to beef, conditional on the size of the local possum habitat. Other factors identiﬁed included the cattle population size and the presence of previous infection. Ram´ırez-Villaescusa et al. [49] examined herd- and animal-level risks associated with bovine TB tuberculin test positivity in cattle in 148 herds in RBCT areas of SW England. Data on cattle on these farms were sourced from the bovine TB VetNet database from 1996 to 2004 and from the British Cattle Movement Scheme database. Results showed that cattle were more likely to react to the bovine TB tuberculin test when they had been present at a previous bovine TB herd test(s) where other cattle had reacted. This positively correlated with age and number of tests. Cattle on restocked farms were less likely to react to the tuberculin test compared with cattle on continuously stocked farms. These results highlight the likely importance of exposure to infected cattle present at a previous test as a source of infection to cattle that subsequently became reactors. This suggests that there was a lower risk of exposure to bovine TB to cattle in newly formed herds. Further analysis [50] examined herd and individual animal risks associated with tuberculin test positivity. Farms restocked for <12 months after-FMD had a signiﬁcantly reduced risk compared to continuously stocked farms. The feeding of mineral licks and vitamin supplements was associated with reduced risk. Storing manure and slurry indoors or in a closed container, spreading manure all year, possession of dairy cattle, increased herd size, and purchase of cattle from markets as well as farm location were associated with increased risk. The authors concluded that whole herd removal might have reduced the infectious load on these premises, but this did not continue once cattle were reintroduced. The method of slurry storage or spread might has allowed M. bovis to persist in the environment in some cases. The increased risk associated with cattle purchase and continuous stocking versus restocking supports a role for undetected infection in cattle as a risk to other cattle. 6. Farm-Scale Studies Most of the studies outlined above operate over quite large areas, larger than the individual farm scale, where model predictions would be even more useful. However, a recent study, using GB RBCT data from one year into treatment to one year after treatment, presents an analysis of spatial farm level, herd-based risk factors associated with the probability of a conﬁrmed bovine TB breakdown [51]. Within reactive and survey-only areas, the risk of a conﬁrmed bovine TB breakdown was associated with two factors; increasing numbers of active badger setts and having cattle herds within 1.5 km. For proactive areas, the strongest predictor of bovine TB risk was the number of M. bovis-positive badgers culled initially within 1.5 km, suggesting that a risk remained for 7 those herds, which was not removed by badger culling. They provide further evidence that the local infection in cattle and badgers is linked. Within the RBCT data they found that dairy herds were more at risk than beef herds and tended to rely on one particular breed of cattle, whereas beef farms tended to use a mixture of breeds and crossbreeds. Whilst acknowledging the complex interaction of risk factors, they indicated that a breed eﬀect might operate [52]. Further, recent, mixed modelling and event history analysis were used to investigate individual risk factors in RBCT data analysis, again at the individual farm level. Farm characteristics, in particular herd and farm size, number of land parcels and being contiguous to other breakdowns were signiﬁcant and consistent risks. They also identiﬁed increased risks for those herds subjected to reactive badger culling and those with increased herd size and increased and fragmented farms [53]. In areas with previously undisturbed badger populations, risks were reduced for herds within the proactive zones, but the authors point out that they did not evaluate the eﬀect at the cull edges or within 2 km of the cull. Risk was actually greater in reactive and survey-only areas by 23% and 18%, respectively, indicating that localized reactive culling was associated with a higher risk than not culling, and this was felt at the local farm level. Whether this risk was sustained over time since last cull remains to be reported. Farm and herd size, number of land parcels and contiguous neighbours were the most consistent risk factors, and no consistent risk due to badger- or habitat-related variables was identiﬁed at the farm level. 7. Summary In summary, the risk factors that have been most consistently identiﬁed in relation to bovine TB, particularly in recent UK and Ireland studies include historic incidence, farm area, cattle movement, occurrence of TB on contiguous premises and/or the level of bovine TB in surrounding areas (infection pressure or force of infection), and herd size (Table 1) [54]. Other factors identiﬁed in some studies include indicators of badger density/activity, use of multiple premises, housing type, herd type, farmland habitat, fertiliser usage, mineral deﬁciencies, and use of silage clamps (Table 2). Herd-keeper behaviour is also likely to change during an outbreak due to increased risk perception, leading to improved biosecurity measures and risk aversion [55]. In general, the most consistently identiﬁed risk factors are biologically plausible and consistent with known transmis- sion routes involving cattle-cattle and badger-cattle spread. It is important to note that epidemiological studies diﬀer in the variables analysed, the exact measures used (e.g., in relation to association with badgers), and study size and power. Not all risk factors would be expected to be identiﬁed equally across diﬀerent studies. Risk factors will vary across regions due to factors such as diﬀering farm structures, farm management practices, local TB control, and the relative importance of speciﬁc risk factors within individual areas. After extensive and iterative risk factor studies on RBCT data the Independent Scientiﬁc Group (ISG) concluded that important risk factors diﬀered between regions and hence