Event
Robert Deardon, PhD, University of Calgary
Tuesday, January 31, 2017 15:30to16:30
Purvis Hall
1020 avenue des Pins Ouest, Montreal, QC, H3A 1A2, CA
Inferring the spatial dynamics of infectious diseases via Gaussian process emulation.
Statistical inference for spatial models of infectious disease spread is often very computationally expensive. Such models are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework, which requires multiple calculation of what is often a computationally cumbersome likelihood function. This problem is especially severe when there are large numbers of latent variables to compute. Here, we 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 spatial disease systems, and that this can be done in much more computationally efficient manner than full Bayesian MCMC allows.