Title: Bayesian uncertainty quantification for epidemic spread on networks,
Abstract: While there exist a number of mathematical approaches to modeling the spread
of disease on a network, analyzing such systems in the presence of uncertainty
introduces significant complexity. In scenarios where system parameters must be
inferred from limited observations, general approaches to uncertainty
quantification can generate approximate distributions of the unknown
parameters, but these methods often become computationally expensive if the
underlying disease model is complex. In this paper, we apply the recent
massively parallelizable Bayesian uncertainty quantification framework $\Pi4U$
to a model of a disease spreading on a network of communities, showing that the
method can accurately and tractably recover system parameters and select
optimal models in this setting.