Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Abstract

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1

    Department for Environment Food and Rural Affairs. Bovine Tuberculosis Evidence Plan (2013)

  2. 2

    National Audit Office. Identifying and Tracking Livestock in England (2003)

  3. 3

    Waddington, K. To stamp out “so terrible a malady”: bovine tuberculosis and tuberculin testing in Britain, 1890–1939. Med. Hist. 48, 29–48 (2004)

    Article  Google Scholar 

  4. 4

    Reynolds, D. A review of tuberculosis science and policy in Great Britain. Vet. Microbiol. 112, 119–126 (2006)

    Article  Google Scholar 

  5. 5

    Krebs, J. The Independent Scientific Review Group. Bovine Tuberculosis in Cattle and Badgers (1997)

    Google Scholar 

  6. 6

    Gopal, R., Goodchild, A., Hewinson, G., de la Rua Domenech, R. & Clifton-Hadley, R. Introduction of bovine tuberculosis to north-east England by bought-in cattle. Vet. Rec. 159, 265–271 (2006)

    CAS  Article  Google Scholar 

  7. 7

    The Independent Scientific Group on Cattle TB. Bovine TB: The Scientific Evidence (2007)

  8. 8

    Department for Environment Food and Rural Affairs. Bovine TB Eradication Programme for England http://www.defra.gov.uk/publications/files/pb13601-bovinetb-eradication-programme-110719.pdf (2011)

  9. 9

    Pollock, J. M. et al. Immune responses in bovine tuberculosis. Tuberculosis (Edinb.) 81, 103–107 (2001)

    CAS  Article  Google Scholar 

  10. 10

    Green, L. E. & Cornell, S. J. Investigations of cattle herd breakdowns with bovine tuberculosis in four counties of England and Wales using VETNET data. Prev. Vet. Med. 70, 293–311 (2005)

    CAS  Article  Google Scholar 

  11. 11

    Monaghan, M. L., Doherty, M. L., Collins, J. D., Kazda, J. F. & Quinn, P. J. The tuberculin test. Vet. Microbiol. 40, 111–124 (1994)

    CAS  Article  Google Scholar 

  12. 12

    Conlan, A. J. K. et al. Estimating the hidden burden of bovine tuberculosis in Great Britain. PLOS Comput. Biol. 8, e1002730 (2012)

    MathSciNet  CAS  Article  Google Scholar 

  13. 13

    Keeling, M. J. et al. Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science 294, 813–817 (2001)

    ADS  CAS  Article  Google Scholar 

  14. 14

    Ferguson, N. M., Donnelly, C. A. & Anderson, R. M. The foot-and-mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science 292, 1155–1160 (2001)

    ADS  CAS  Article  Google Scholar 

  15. 15

    Barlow, N. D., Kean, J. M., Hickling, G., Livingstone, P. G. & Robson, A. B. A simulation model for the spread of bovine tuberculosis within New Zealand cattle herds. Prev. Vet. Med. 32, 57–75 (1997)

    CAS  Article  Google Scholar 

  16. 16

    Fischer, E. A. J., van Roermund, H. J. W., Hemerik, L., van Asseldonk, M. A. P. M. & de Jong, M. C. M. Evaluation of surveillance strategies for bovine tuberculosis (Mycobacterium bovis) using an individual based epidemiological model. Prev. Vet. Med. 67, 283–301 (2005)

    CAS  Article  Google Scholar 

  17. 17

    Woolhouse, M. E. J. et al. Epidemiological implications of the contact network structure for cattle farms and the 20–80 rule. Biol. Lett. 1, 350–352 (2005)

    CAS  Article  Google Scholar 

  18. 18

    Green, D. M., Kiss, I. Z., Mitchell, A. P. & Kao, R. R. Estimates for local and movement-based transmission of bovine tuberculosis in British cattle. Proc. R. Soc. B 275, 1001–1005 (2008)

    Article  Google Scholar 

  19. 19

    Blower, S. M. et al. The intrinsic transmission dynamics of tuberculosis epidemics. Nature Med. 1, 815–821 (1995)

    CAS  Article  Google Scholar 

  20. 20

    Cox, D. R. et al. Simple model for tuberculosis in cattle and badgers. Proc. Natl Acad. Sci. USA 102, 17588–17593 (2005)

    ADS  CAS  Article  Google Scholar 

  21. 21

    Brooks-Pollock, E. et al. Age-dependent patterns of bovine tuberculosis in cattle. Vet. Res. 44, 97 (2013)

    Article  Google Scholar 

  22. 22

    O’Hare, A., Orton, R. J., Bessell, P. R. & Kao, R. R. Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach. Proc. R. Soc. B 281, 20140248 (2014)

    Article  Google Scholar 

  23. 23

    Sisson, S. A., Fan, Y. & Tanaka, M. M. Sequential Monte Carlo without likelihoods. Proc. Natl Acad. Sci. USA 104, 1760–1765 (2007)

    ADS  MathSciNet  CAS  Article  Google Scholar 

  24. 24

    Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6, 187–202 (2009)

    Article  Google Scholar 

  25. 25

    Donnelly, C. & Hone, J. Is there an association between levels of bovine tuberculosis in cattle herds and badgers? Stat. Commun. Infect. Dis. 2, (2010)

  26. 26

    Godfray, H. C. J. et al. A restatement of the natural science evidence base relevant to the control of bovine tuberculosis in Great Britain. Proc. R. Soc. B 280, 20131634 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

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

Affiliations

Authors

Contributions

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.

Ethics declarations

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)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brooks-Pollock, E., Roberts, G. & Keeling, M. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature 511, 228–231 (2014). https://doi.org/10.1038/nature13529

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing