Gaussian process emulators for spatial individual-level models of infectious disease

Statistical inference for spatial models of infectious disease spread is often very computationally expensive. These models are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework, which requires multiple iterations of the computationally cumbersome likelihood function. We here propose a method of inference based on so-called emulation techniques. Once again the method is set in a Bayesian MCMC context, but avoids calculation of the computationally expensive likelihood function by replacing it with a Gaussian process approximation of the likelihood function built from simulated data. We show that such a method can be used to infer the model parameters and underlying characteristics of the spatial disease system, and this can be done in a computationally efficient manner. (C) 2016 Statistical Society of Canada