I. Andrianakis
Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model
Andrianakis, I.; McCreesh, N.; Vernon, I.; McKinley, T.J.; Oakley, J.E.; Nsubuga, R.; Goldstein, M.; White, R.G.
Authors
N. McCreesh
Professor Ian Vernon i.r.vernon@durham.ac.uk
Professor
T.J. McKinley
J.E. Oakley
R. Nsubuga
M. Goldstein
R.G. White
Abstract
History matching is a model (pre-)calibration method that has been applied to computer models from a wide range of scientific disciplines. In this work we apply history matching to an individual-based epidemiological model of HIV that has 96 input and 50 output parameters, a model of much larger scale than others that have been calibrated before using this or similar methods. Apart from demonstrating that history matching can analyze models of this complexity, a central contribution of this work is that the history match is carried out using linear regression, a statistical tool that is elementary and easier to implement than the Gaussian process--based emulators that have previously been used. Furthermore, we address a practical difficulty with history matching, namely, the sampling of tiny, nonimplausible spaces, by introducing a sampling algorithm adjusted to the specific needs of this method. The effectiveness and simplicity of the history matching method presented here shows that it is a useful tool for the calibration of computationally expensive, high dimensional, individual-based models.
Citation
Andrianakis, I., McCreesh, N., Vernon, I., McKinley, T., Oakley, J., Nsubuga, R., …White, R. (2017). Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 694-719. https://doi.org/10.1137/16m1093008
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 27, 2017 |
Online Publication Date | Aug 1, 2017 |
Publication Date | Aug 1, 2017 |
Deposit Date | Aug 22, 2016 |
Publicly Available Date | Sep 20, 2017 |
Journal | SIAM/ASA Journal on Uncertainty Quantification |
Publisher | Society for Industrial and Applied Mathematics |
Peer Reviewed | Peer Reviewed |
Volume | 5 |
Issue | 1 |
Pages | 694-719 |
DOI | https://doi.org/10.1137/16m1093008 |
Public URL | https://durham-repository.worktribe.com/output/1377490 |
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Copyright Statement
© 2017, Society for Industrial and Applied Mathematics
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