Gaussian process approximations for fast inference from infectious disease data
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Published on: June 5, 2017
Elizabeth Buckingham-Jeffery1, Valerie Isham2, Thomas House3
1Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, UK; School of Mathematics, University of Manchester, Manchester M13 9PL, UK.
Abstract:
We present a flexible framework for deriving and quantifying the accuracy of Gaussian process approximations to non-linear stochastic individual-based models of epidemics. We develop this for the SIR and SEIR models, and we show how it can be used to perform quick maximum likelihood inference for the underlying parameters given population estimates of the number of infecteds or cases at given time points. We also show how the unobserved processes can be inferred at the same time as the underlying parameters.
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