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Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain

Author

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  • Neil M. Ferguson

    (Faculty of Medicine, Imperial College of Science, Technology and Medicine, St Mary's Campus)

  • Christl A. Donnelly

    (Faculty of Medicine, Imperial College of Science, Technology and Medicine, St Mary's Campus)

  • Roy M. Anderson

    (Faculty of Medicine, Imperial College of Science, Technology and Medicine, St Mary's Campus)

Abstract

The foot and mouth disease (FMD) epidemic in British livestock remains an ongoing cause for concern, with new cases still arising in previously unaffected areas. Epidemiological analyses1,2,3 have been vital in delivering scientific advice to government on effective control measures. Using disease, culling and census data on all livestock farms in Great Britain, we analysed the risk factors determining the spatiotemporal evolution of the epidemic and of the impact of control policies on FMD incidence. Here we show that the species mix, animal numbers and the number of distinct land parcels in a farm are central to explaining regional variation in transmission intensity. We use the parameter estimates thus obtained in a dynamical model of disease spread to show that extended culling programmes were essential for controlling the epidemic to the extent achieved, but demonstrate that the epidemic could have been substantially reduced in scale had the most efficient control measures been rigorously applied earlier.

Suggested Citation

  • Neil M. Ferguson & Christl A. Donnelly & Roy M. Anderson, 2001. "Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain," Nature, Nature, vol. 413(6855), pages 542-548, October.
    • Handle: RePEc:nat:nature:v:413:y:2001:i:6855:d:10.1038_35097116
    DOI: 10.1038/35097116
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    1. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    2. Alzahrani, Abdullah K. & Alshomrani, Ali Saleh & Pal, Nikhil & Samanta, Sudip, 2018. "Study of an eco-epidemiological model with Z-type control," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 197-208.
    3. Namilae, S. & Srinivasan, A. & Mubayi, A. & Scotch, M. & Pahle, R., 2017. "Self-propelled pedestrian dynamics model: Application to passenger movement and infection propagation in airplanes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 248-260.
    4. Boni, Maciej F. & Galvani, Alison P. & Wickelgren, Abraham L. & Malani, Anup, 2013. "Economic epidemiology of avian influenza on smallholder poultry farms," Theoretical Population Biology, Elsevier, vol. 90(C), pages 135-144.
    5. Rakowski, Franciszek & Gruziel, Magdalena & Bieniasz-Krzywiec, Łukasz & Radomski, Jan P., 2010. "Influenza epidemic spread simulation for Poland — a large scale, individual based model study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3149-3165.
    6. Maud Marsot & Séverine Rautureau & Barbara Dufour & Benoit Durand, 2014. "Impact of Stakeholders Influence, Geographic Level and Risk Perception on Strategic Decisions in Simulated Foot and Mouth Disease Epizootics in France," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-16, January.
    7. Montazeri Hesam & Mozaffarilegha Mozhgan & Little Susan & 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.
    8. Rob Deardon & Babak Habibzadeh & Hau Yi Chung, 2012. "Spatial measurement error in infectious disease models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1139-1150, November.
    9. 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.
    10. Thomas House & Matt J Keeling, 2010. "The Impact of Contact Tracing in Clustered Populations," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-9, March.
    11. Ioannidis, John P.A. & Cripps, Sally & Tanner, Martin A., 2022. "Forecasting for COVID-19 has failed," International Journal of Forecasting, Elsevier, vol. 38(2), pages 423-438.
    12. 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.
    13. Don Klinkenberg & Christophe Fraser & Hans Heesterbeek, 2006. "The Effectiveness of Contact Tracing in Emerging Epidemics," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-7, December.
    14. Krämer, J. & Farwick, J., 2009. "Schäden in der Landwirtschaft durch Maul- und Klauenseuche: Simulationsrechnungen für ausgewählte Modellregionen," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 44, March.
    15. Tom Lindström & Daniel A Grear & Michael Buhnerkempe & Colleen T Webb & Ryan S Miller & Katie Portacci & Uno Wennergren, 2013. "A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    16. Larry Stikeleather & William Morrow & Robert Meyer & Craig Baird & Burt Halbert, 2013. "Evaluation of CO 2 Application Requirements for On-Farm Mass Depopulation of Swine in a Disease Emergency," Agriculture, MDPI, vol. 3(4), pages 1-14, September.
    17. Finlay Campbell & Anne Cori & Neil Ferguson & Thibaut Jombart, 2019. "Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
    18. 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.
    19. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    20. Hennessy, David A. & Rault, Arnaud, 2023. "On systematically insufficient biosecurity actions and policies to manage infectious animal disease," Ecological Economics, Elsevier, vol. 206(C).
    21. Yuan, Xinpeng & Xue, Yakui & Liu, Maoxing, 2013. "Analysis of an epidemic model with awareness programs by media on complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 48(C), pages 1-11.

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