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

Author

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  • Ellen Brooks-Pollock

    (Disease Dynamics Unit, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
    WIDER Centre, Mathematics Institute and School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK)

  • Gareth O. Roberts

    (University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK)

  • Matt J. Keeling

    (WIDER Centre, Mathematics Institute and School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK)

Abstract

Bovine tuberculosis is a major economic burden on the cattle industry, and attempts to control it have been politically controversial; here farm movement and bovine tuberculosis incidence data are used to construct a mechanistic model and tease apart the factors contributing to epidemic bovine tuberculosis spread.

Suggested Citation

  • Ellen Brooks-Pollock & Gareth O. Roberts & Matt J. Keeling, 2014. "A dynamic model of bovine tuberculosis spread and control in Great Britain," Nature, Nature, vol. 511(7508), pages 228-231, July.
    • Handle: RePEc:nat:nature:v:511:y:2014:i:7508:d:10.1038_nature13529
    DOI: 10.1038/nature13529
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    Citations

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    Cited by:

    1. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2018. "Control fast or control smart: When should invading pathogens be controlled?," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-21, February.
    2. Sifat A Moon & Lee W Cohnstaedt & D Scott McVey & Caterina M Scoglio, 2019. "A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    3. Ellen Brooks-Pollock & Leon Danon & Hester Korthals Altes & Jennifer A Davidson & Andrew M T Pollock & Dick van Soolingen & Colin Campbell & Maeve K Lalor, 2020. "A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-14, March.
    4. Montagnon, Pierre, 2020. "Stability of piecewise deterministic Markovian metapopulation processes on networks," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1515-1544.
    5. Peter Brommesson & Uno Wennergren & Tom Lindström, 2016. "Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn’t Fit All," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-20, October.
    6. Horan, Richard D. & Fenichel, Eli P. & Finnoff, David & Wolf, Christopher A., 2015. "Managing dynamic epidemiological risks through trade," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 192-207.
    7. Lambert, Sébastien & Gilot-Fromont, Emmanuelle & Toïgo, Carole & Marchand, Pascal & Petit, Elodie & Garin-Bastuji, Bruno & Gauthier, Dominique & Gaillard, Jean-Michel & Rossi, Sophie & Thébault, Anne, 2020. "An individual-based model to assess the spatial and individual heterogeneity of Brucella melitensis transmission in Alpine ibex," Ecological Modelling, Elsevier, vol. 425(C).
    8. Lan Li & Yuliang Xi & Fu Ren, 2016. "Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China," IJERPH, MDPI, vol. 13(3), pages 1-17, March.
    9. C. E. Dangerfield & A. E. Whalley & N. Hanley & C. A. Gilligan, 2018. "What a Difference a Stochastic Process Makes: Epidemiological-Based Real Options Models of Optimal Treatment of Disease," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(3), pages 691-711, July.

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