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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
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 | ||||||||
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| 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: |
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| 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: |
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