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A dynamic model of bovine tuberculosis spread and control in Great Britain


Bovine tuberculosis (TB) is one of the most complex, persistent and controversial problems facing the British cattle industry, costing the country an estimated £100 million per year1. The low sensitivity of the standard diagnostic test leads to considerable ambiguity in determining the main transmission routes of infection, which exacerbates the continuing scientific debate2,3,4,5,6. In turn this uncertainty fuels the fierce public and political disputes on the necessity of controlling badgers to limit the spread of infection. Here we present a dynamic stochastic spatial model for bovine TB in Great Britain that combines within-farm and between-farm transmission. At the farm scale the model incorporates stochastic transmission of infection, maintenance of infection in the environment and a testing protocol that mimics historical government policy. Between-farm transmission has a short-range environmental component and is explicitly driven by movements of individual cattle between farms, as recorded in the Cattle Tracing System2. The resultant model replicates the observed annual increase of infection over time as well as the spread of infection into new areas. Given that our model is mechanistic, it can ascribe transmission pathways to each new case; the majority of newly detected cases involve several transmission routes with moving infected cattle, reinfection from an environmental reservoir and poor sensitivity of the diagnostic test all having substantive roles. This underpins our findings on the implications of control measures. Very few of the control options tested have the potential to reverse the observed annual increase, with only intensive strategies such as whole-herd culling or additional national testing proving highly effective, whereas controls focused on a single transmission route are unlikely to be highly effective.

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Figure 1: Spatio-temporal comparison of model and data.
Figure 2: Mechanisms driving transmission.
Figure 3: Effect of different interventions assumed to begin in 2005 compared to a baseline of standard testing.


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This work was funded by BBSRC, the Wellcome Trust and EPSRC. We would like to thank G. Medley, L. Green, O. Courtenay, A. Ramirez-Villaescusa, J. Wood and L. Danon for helpful discussions on bovine TB dynamics. Thanks to A. Conlan and T. J. McKinley for advice on implementing SMC-ABC and to A. Conlan to setting up the Marx Bros cluster. The breakdown and reactor data was supplied by the AHVLA team (particularly A. Mitchell and R. Blackwell), the RADAR team and DEFRA.

Author information

Authors and Affiliations



M.J.K. and E.B.-P. developed the model structure; E.B.-P. and G.O.R. developed the statistical methodology; all authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Matt J. Keeling.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Historic testing patterns and identification of infected cattle.

a, Number of single intradermal comparative cervical tuberculin (SICCT) tests carried out on cattle according to the reason for the test (test type). b, Number of reactors (cattle testing positive) by test type. Tests shown are: 12 month follow-up test (12M), 6 month follow-up test (6M), contiguous test (CON), check test (CT), pre-movement testing (PRMT), routine herd test (RHT), follow-up tests at sixty-day intervals (SI) and whole-herd tests (WHT).

Extended Data Figure 2 Prior and posterior distributions for the model parameters.

Prior distributions (dashed lines) reflect captures uncertainty in the seven different parameters; only the test sensitivity, ρ, has a relatively informative prior based on estimates in the literature. Red curves show the posterior distribution as given by the ABC-SMC algorithm (see Supplementary Material).

Extended Data Figure 3 Data–model comparisons.

a, The number of reactors per year; b, the number of failed herd tests; c, observed and expected number of reactors per county and year; d, the observed and expected number of failed tests (at the herd level, per county and year); e, the observed and expected number of reactors and failed tests using logarithmic binning; f, the number of reactor cattle found per failed test. The error bars and red shaded regions denote the 95% prediction intervals; the yellow region in a and b shows the range from 5,000 simulations.

Extended Data Figure 4 Observed and expected number of reactors per county per year.

The expected value for each county and year is calculated as the (weighted) mean number of reactors produced by simulations using the posterior parameter sets, from 5,000 simulations.

Extended Data Figure 5 A comparison of testing strategies using the stochastic model.

ad, The predicted model output compared to baseline predictions at the start (2005) and end (2010) of the implementation, for the ten controls listed in the Supplementary information and Extended Data Table 2, for reactors (a), cattle culled (b), herds tested (c) and herds under restrictions (d). e, For the baseline case and seven control measures listed in the main paper, the change in number of reactors at a county scale. Counties are aggregated into four bins (x axis) based on the number of reactors one year, and the expected number of reactors in the next year is shown on the y axis. Error bars denote the 95% prediction intervals.

Extended Data Table 1 The biological meanings, prior distributions, point estimates (expected value from the posterior) and 95% intervals calculated from the marginal posterior distributions
Extended Data Table 2 The estimated effect of control measures

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data 1-8 and additional references. (PDF 220 kb)

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Brooks-Pollock, E., Roberts, G. & Keeling, M. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature 511, 228–231 (2014).

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