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Gaussian process emulators for spatial individual-level models of infectious disease. (English. French summary) Zbl 1353.92008
Summary: 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.

MSC:
92B15 General biostatistics
92D30 Epidemiology
62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
Software:
mlegp; pomp
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