Bayesian Modeling of Global Viral Diffusions at Scale

Holbrook presents a Bayesian hierarchical model to infer the rate at which different seasonal influenza virus subtypes travel across global transportation networks. Data take the form of 5,392 viral sequences and their associated 14 million pairwise distances, arising from the annual number of commercial airline seats between viral sampling locations. To adjust for shared evolutionary history of the viruses, the model implements a phylogenetic extension to the Bayesian multidimensional scaling model. Subtype H3N2 spreads most effectively, consistent with its epidemic success relative to other seasonal influenza subtypes.

  Speaker: Andrew Holbrook, PhD, Assistant Professor, Biostatistics, UCLA

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