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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
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 | ||||||
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| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
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| 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: |
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| Date of first compliant deposit: | 10 August 2020 | ||||||
| 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: |
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