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The use of mixture density networks in the emulation of complex epidemiological individual-based models

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Davis, Christopher N., Hollingsworth, T. Déirdre, Caudron, Q. and Irvine, Michael Alastair (2020) The use of mixture density networks in the emulation of complex epidemiological individual-based models. PLoS Computational Biology, 16 (3). e1006869. doi:10.1371/journal.pcbi.1006869

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Official URL: https://doi.org/10.1371/journal.pcbi.1006869

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Abstract

Complex, highly-computational, individual-based models are abundant in epidemiology. For epidemics such as macro-parasitic diseases, detailed modelling of human behaviour and pathogen life-cycle are required in order to produce accurate results. This can often lead to models that are computationally-expensive to analyse and perform model fitting, and often require many simulation runs in order to build up sufficient statistics. Emulation can provide a more computationally-efficient output of the individual-based model, by approximating it using a statistical model. Previous work has used Gaussian processes (GPs) in order to achieve this, but these can not deal with multi-modal, heavy-tailed, or discrete distributions. Here, we introduce the concept of a mixture density network (MDN) in its application in the emulation of epidemiological models. MDNs incorporate both a mixture model and a neural network to provide a flexible tool for emulating a variety of models and outputs. We develop an MDN emulation methodology and demonstrate its use on a number of simple models incorporating both normal, gamma and beta distribution outputs. We then explore its use on the stochastic SIR model to predict the final size distribution and infection dynamics. MDNs have the potential to faithfully reproduce multiple outputs of an individual-based model and allow for rapid analysis from a range of users. As such, an open-access library of the method has been released alongside this manuscript.

Item Type: Journal Article
Subjects: R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine
Divisions: Faculty of Science > Life Sciences (2010- )
Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH): Epidemiology , Epidemiology -- Statistical methods, Communicable diseases , Communicable diseases -- Simulation methods
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 16 March 2020
Dates:
DateEvent
16 March 2020Published
20 February 2020Accepted
Volume: 16
Number: 3
Article Number: e1006869
DOI: 10.1371/journal.pcbi.1006869
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDChildren’s Investment Fund Foundationhttps://ciff.org/

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