We did not encounter common problems in MCMC estimation like slow convergence and slow mixing (O'Neill [27] ). There were some correlations between parameters, mostly having biological explanations (cattle and sheep infectivity for instance), but a careful parameterization lowers them. We verified parameter estimates were not dependent on parameterization choices -e.g. no difference was seen whether we fitted species infectivity individually, or just fitted sheep infectivity and then the ratio of cattle-to-sheep infectivity. Table 1 lists the parameter estimates we obtained for a set of fitted models conditioned only on the first infection whereas Table 2 presents the estimates for models conditioned on infections occurring up to 23 rd February. The posterior deviances for each set of models are plotted in Figure 2a and Figure 2b , respectively. Figure 2a illustrates some clear conclusions. Of the two models without time variation in parameters, the interaction model fits significantly better than the baseline model without heterogeneous mixing between species. However, fitting the interaction model broadened the credible intervals of the infectivity parameter estimates (Table 1) , indicating (unsurprisingly) slight confounding between the 4 infectivity and susceptibility parameters.