Bayesian Inference for Stochastic Epidemics in Populations with Random Social Structure

Abstract

A single‐population Markovian stochastic epidemic model is defined so that the underlying social structure of the population is described by a Bernoulli random graph. The parameters of the model govern the rate of infection, the length of the infectious period, and the probability of social contact with another individual in the population. Markov chain Monte Carlo methods are developed to facilitate Bayesian inference for the parameters of both the epidemic model and underlying unknown social structure. The methods are applied in various examples of both illustrative and real‐life data, with two different kinds of data structure considered.