Fast Inference for Network Models of Infectious Disease Spread

摘要

Models of infectious disease over contact networks offer a versatile means of capturing heterogeneity in populations during an epidemic. Highly connected individuals tend to be infected at a higher rate early during an outbreak than those with fewer connections. A powerful approach based on the probability generating function of the individual degree distribution exists for modelling the mean field dynamics of outbreaks in such a population. We develop the same idea in a stochastic context, by proposing a comprehensive model for 1-week-ahead incidence counts. Our focus is inferring contact network (and other epidemic) parameters for some common degree distributions, in the case when the network is non-homogeneous at random'. Our model is initially set within a susceptible-infectious-removed framework, then extended to the susceptible-infectious-removed-susceptible scenario, and we apply this methodology to influenza A data.