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Gaussian process approximations for fast inference from infectious disease data

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Buckingham-Jeffery, Elizabeth, Isham, Valerie and House, Thomas A. (2018) Gaussian process approximations for fast inference from infectious disease data. Mathematical Biosciences, 301 . pp. 111-120. doi:10.1016/j.mbs.2018.02.003

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Official URL: http://doi.org/10.1016/j.mbs.2018.02.003

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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. [Abstract copyright: Copyright © 2018. Published by Elsevier Inc.]

Item Type: Journal Article
Subjects: R Medicine > RA Public aspects of medicine
Divisions: Faculty of Science > Centre for Complexity Science
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Epidemiology -- Mathematical models, Epidemiology -- Statistical methods, Communicable diseases -- Mathematical models, Biomathematics, Stochastic processes
Journal or Publication Title: Mathematical Biosciences
Publisher: Elsevier Science Inc.
ISSN: 0025-5564
Official Date: July 2018
Dates:
DateEvent
July 2018Published
20 February 2018Available
17 February 2018Accepted
Volume: 301
Page Range: pp. 111-120
DOI: 10.1016/j.mbs.2018.02.003
Status: Peer Reviewed
Publication Status: Published
Publisher Statement:
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/I01358X/1 ; EP/N033701/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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