Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability

  • Vinny Davies
  • Richard Reeve
  • William T. Harvey
  • Dirk Husmeier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9874)


In Foot-and-Mouth Disease Virus (FMDV), understanding how viruses offer protection against related emerging strains is vital for creating effective vaccines. With testing large numbers of vaccines being infeasible, the development of an in silico predictor of cross-protection between virus strains has been a vital area of recent research. The current paper reviews a recent contribution to this area, the SABRE method, a sparse hierarchical Bayesian model which uses spike and slab priors to identify key antigenic sites within FMDV serotypes. WAIC is then combined with the SABRE method and its ability to approximate Bayesian Cross Validation performance in terms of correctly selecting random effect components analysed. WAIC and the SABRE method have then been applied to two FMDV datasets and the results analysed.


Model selection Spike and slab prior Foot-and-Mouth Disease Virus Bayesian hierarchical models WAIC Cross Validation 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vinny Davies
    • 1
  • Richard Reeve
    • 1
  • William T. Harvey
    • 1
  • Dirk Husmeier
    • 1
  1. 1.University of GlasgowGlasgowScotland, UK

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