Andrianakis, I., Vernon, I., McCreesh, N. et al. (5 more authors) (2017) History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66 (4). pp. 717-740. ISSN 0035-9254
Abstract
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Royal Statistical Society. This is an author produced version of a paper subsequently published in Journal of the Royal Statistical Society: Series C (Applied Statistics). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Calibration; Gaussian processes; Individual-based models; Inverse problems; Stochastic simulators |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/J005088/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jan 2017 13:43 |
Last Modified: | 24 Nov 2017 01:38 |
Published Version: | https://doi.org/10.1111/rssc.12198 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1111/rssc.12198 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110612 |