Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging

  1. R. B. Garabed1,
  2. W. O. Johnson2,
  3. J. Gill3,
  4. A. M. Perez4,
  5. M. C. Thurmond4

Article first published online: 28 MAR 2008

DOI: 10.1111/j.1467-985X.2008.00534.x

Issue

Journal of the Royal Statistical Society: Series A (Statistics in Society)

Journal of the Royal Statistical Society: Series A (Statistics in Society)

Volume 171, Issue 3, pages 699–722, June 2008

Additional Information

How to Cite

Garabed, R. B., Johnson, W. O., Gill, J., Perez, A. M. and Thurmond, M. C. (2008), Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171: 699–722. doi: 10.1111/j.1467-985X.2008.00534.x

Author Information

  1. 1

    University of California, Davis, USA

  2. 2

    University of California, Irvine, USA

  3. 3

    Washington University, St Louis, USA

  4. 4

    University of California, Davis, USA

*R. B. Garabed, Foot-and-Mouth Disease Modeling and Surveillance Laboratory, Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA. E-mail: rbgarabed@ucdavis.edu

Publication History

  1. Issue published online: 28 MAR 2008
  2. Article first published online: 28 MAR 2008
  3. [Received July 2007. Revised October 2007]

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Keywords:

  • Animal disease;
  • Economic health;
  • Expert opinion;
  • Political voice;
  • Prediction

Summary.  Using Bayesian model averaging, we quantify associations of governance and economic health with country level presence of foot-and-mouth disease (FMD) and estimate the probability of the presence of FMD in each country from 1997 to 2005. The Bayesian model averaging accounted for countries’ previous FMD status and other possible confounders, as well as uncertainty about the ‘true’ model, and provided accurate predictions (90% specificity and 80% sensitivity). This model represents a novel approach to predicting FMD, and other conditions, on a global scale and in identifying important risk factors that can be applied to global policy and allocation of resources for disease control.

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