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Modelling vaccination strategies against foot-and-mouth disease

Author

Listed:
  • M. J. Keeling

    (University of Warwick)

  • M. E. J. Woolhouse

    (University of Edinburgh)

  • R. M. May

    (University of Oxford)

  • G. Davies

    (Zinnia)

  • B. T. Grenfell

    (University of Cambridge)

Abstract

Vaccination has proved a powerful defence against a range of infectious diseases of humans and animals. However, its potential to control major epidemics of foot-and-mouth disease (FMD) in livestock is contentious. Using an individual farm-based model, we consider either national prophylactic vaccination campaigns in advance of an outbreak, or combinations of reactive vaccination and culling strategies during an epidemic. Consistent with standard epidemiological theory, mass prophylactic vaccination could reduce greatly the potential for a major epidemic, while the targeting of high-risk farms increases efficiency. Given sufficient resources and preparation, a combination of reactive vaccination and culling might control ongoing epidemics. We also explore a reactive strategy, ‘predictive’ vaccination, which targets key spatial transmission loci and can reduce markedly the long tail that characterizes many FMD epidemics. These analyses have broader implications for the control of human and livestock infectious diseases in heterogeneous spatial landscapes.

Suggested Citation

  • M. J. Keeling & M. E. J. Woolhouse & R. M. May & G. Davies & B. T. Grenfell, 2003. "Modelling vaccination strategies against foot-and-mouth disease," Nature, Nature, vol. 421(6919), pages 136-142, January.
    • Handle: RePEc:nat:nature:v:421:y:2003:i:6919:d:10.1038_nature01343
    DOI: 10.1038/nature01343
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    Cited by:

    1. Leonid Sedov & Alexander Krasnochub & Valentin Polishchuk, 2020. "Modeling quarantine during epidemics and mass-testing using drones," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
    2. Stefan Sellman & Kimberly Tsao & Michael J Tildesley & Peter Brommesson & Colleen T Webb & Uno Wennergren & Matt J Keeling & Tom Lindström, 2018. "Need for speed: An optimized gridding approach for spatially explicit disease simulations," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-27, April.
    3. Peyrard, N. & Dieckmann, U. & Franc, A., 2008. "Long-range correlations improve understanding of the influence of network structure on contact dynamics," Theoretical Population Biology, Elsevier, vol. 73(3), pages 383-394.
    4. Tom Lindström & Michael Tildesley & Colleen Webb, 2015. "A Bayesian Ensemble Approach for Epidemiological Projections," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-30, April.
    5. Pilwon Kim & Chang Hyeong Lee, 2018. "Epidemic Spreading in Complex Networks with Resilient Nodes: Applications to FMD," Complexity, Hindawi, vol. 2018, pages 1-9, March.
    6. Wang, Yi & Cao, Jinde & Sun, Gui-Quan & Li, Jing, 2014. "Effect of time delay on pattern dynamics in a spatial epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 137-148.
    7. Parham, Paul E. & Singh, Brajendra K. & Ferguson, Neil M., 2008. "Analytic approximation of spatial epidemic models of foot and mouth disease," Theoretical Population Biology, Elsevier, vol. 73(3), pages 349-368.
    8. Tom Kompas & Pham Van Ha & Hoa-Thi-Minh Nguyen & Graeme Garner & Sharon Roche & Iain East, 2020. "Optimal surveillance against foot-and-mouth disease: A sample average approximation approach," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    9. Kompas, Tom & Ha, Pham Van & Nguyen, Hoa Thi Minh & East, Iain & Roche, Sharon & Garner, Graeme, 2017. "Optimal surveillance against foot-and-mouth disease: the case of bulk milk testing in Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(4), October.
    10. Gashirai, Tinashe B. & Musekwa-Hove, Senelani D. & Lolika, Paride O. & Mushayabasa, Steady, 2020. "Global stability and optimal control analysis of a foot-and-mouth disease model with vaccine failure and environmental transmission," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    11. Marco J Morelli & Gaël Thébaud & Joël Chadœuf & Donald P King & Daniel T Haydon & Samuel Soubeyrand, 2012. "A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-14, November.
    12. Senapati, Abhishek & Panday, Pijush & Samanta, Sudip & Chattopadhyay, Joydev, 2020. "Disease control through removal of population using Z-control approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    13. Hennessy, David A. & Rault, Arnaud, 2023. "On systematically insufficient biosecurity actions and policies to manage infectious animal disease," Ecological Economics, Elsevier, vol. 206(C).
    14. Montazeri Hesam & Little Susan & Mozaffarilegha Mozhgan & Beerenwinkel Niko & DeGruttola Victor, 2020. "Bayesian reconstruction of transmission trees from genetic sequences and uncertain infection times," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(4-6), pages 1-13, December.
    15. Tenzin & Aldo Dekker & Hans Vernooij & Annemarie Bouma & Arjan Stegeman, 2008. "Rate of Foot‐and‐Mouth Disease Virus Transmission by Carriers Quantified from Experimental Data," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 303-309, April.
    16. Ahamad, Mazbahul & Gustafson, Christopher & VanWormer, Elizabeth, 2016. "Ex-post Livestock Diseases, and Pastoralists' Averting Decisions in Tanzania," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235764, Agricultural and Applied Economics Association.

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