Gaussian process approximations for fast inference from infectious disease data

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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 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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)111-120
JournalMathematical Biosciences
Volume301
Early online date20 Feb 2018
DOIs
Publication statusPublished - Jul 2018

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