  Understanding how genetic changes allow emerging virus strains to escape the
protection afforded by vaccination is vital for the maintenance of effective
vaccines. In the current work, we use structural and phylogenetic differences
between pairs of virus strains to identify important antigenic sites on the
surface of the influenza A(H1N1) virus through the prediction of
haemagglutination inhibition (HI) assay, pairwise measures of the antigenic
similarity of virus strains. We propose a sparse hierarchical Bayesian model
that can deal with the pairwise structure and inherent experimental variability
in the H1N1 data through the introduction of latent variables. The latent
variables represent the underlying HI assay measurement of any given pair of
virus strains and help account for the fact that for any HI assay measurement
between the same pair of virus strains, the difference in the viral sequence
remains the same. Through accurately representing the structure of the H1N1
data, the model is able to select virus sites which are antigenic, while its
latent structure achieves the computational efficiency required to deal with
large virus sequence data, as typically available for the influenza virus. In
addition to the latent variable model, we also propose a new method, block
integrated Widely Applicable Information Criterion (biWAIC), for selecting
between competing models. We show how this allows us to effectively select the
random effects when used with the proposed model and apply both methods to an
A(H1N1) dataset.
