Marian Farah (MRC Biostatistics Unit, Cambridge, UK): A Strategy for Calibrating Time Series Epidemic Simulators

We develop a Bayesian framework for the calibration of a computationally expensive dynamic epidemic model using time series epidemic data. Specifically, we work with a model for H1N1 influenza, which is implemented as a deterministic computer simulator that takes as input the underlying epidemic parameters and calculates the corresponding time series of reported infections. We propose an efficient approximation to the simulator using an emulator, a statistical model, that combines a Gaussian process prior for the output function of the simulator with a dynamic linear model for its evolution through time.  This modeling framework is both flexible and tractable, resulting in efficient posterior inference through Markov Chain Monte Carlo. The dynamic emulator is validated over a set of simulator runs, and then it is used for Bayesian estimation of the epidemic parameters, illustrated using simulated epidemic data.

Keywords: Multivariate time series, Emulation, Calibration, Gaussian process.

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