This is a simplification, as in reality the delay from infection to report almost certainly depends on the size and species mix on a farm, but the result is nevertheless indicative of the expected level of infection in proactive culling. Also, at this stage, the calculations are made as if culling was a non-informative censoring process. This is a reasonable assumption for all proactively culled farms except for DCs (which by definition had been identified by veterinarian as having had a high risk of exposure) but our method may underestimate the infection rate. In calculating the infection to report delay distributions, we divided the epidemic after 23 rd February into 3 time periods: 23 rd February-31 st March, 31 st March-1 st May and 1 st May-5 th October. In these intervals a total of 1332, 4498 and 1627 farms were slaughtered, respectively. Our best fit model conditioned on 23 rd February predicts different infectivity regimes before and after 31 st March (see Parameter Estimates and Table 2 ) but we split further the second period of time due to different delays in reporting to culling. The infection to report delay is 8.6 and 8.8 days for the last two periods of time respectively but the infection to cull delay drops from 9.4 and 8.8 days respectively. Applying this approach to the interaction model with time varying cattle infectivity which conditioned on the 23 rd of February, we calculated the expected proportion of proactively culled farms which were infected. We estimate that approximately 1.3% (1%, 1.6%) of 7457 culled non-IP farms may have been infected -97 in total (Figure 6a ). Of the 1332 farms culled between 23 rd February and 31 st March, 1.7% (1%, 2.4%) may have been infected (23 farms). Of the 4498 farms culled between 31 st March and 1 st May, we estimate 0.7% (0.5%, 1%) were infected (34 farms). In the period 1 st May to 5 th October, we estimate that 1.6% (1%, 2.3%) of 1627 farms culled were infected (27 farms).