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History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation

Andrianakis, I.; Vernon, I.; McCreesh, N.; McKinley, T.J.; Oakley, J.E.; Nsubuga, R.N.; Goldstein, M.; White, R.G.

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Authors

I. Andrianakis

N. McCreesh

T.J. McKinley

J.E. Oakley

R.N. Nsubuga

R.G. White



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.

Citation

Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T., Oakley, J., Nsubuga, R., …White, R. (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, 66(4), 717-740. https://doi.org/10.1111/rssc.12198

Journal Article Type Article
Acceptance Date Oct 1, 2016
Online Publication Date Nov 24, 2016
Publication Date Aug 1, 2017
Deposit Date Jul 25, 2017
Publicly Available Date Jul 25, 2017
Journal Journal of the Royal Statistical Society: Series C
Print ISSN 0035-9254
Electronic ISSN 1467-9876
Publisher Royal Statistical Society
Peer Reviewed Peer Reviewed
Volume 66
Issue 4
Pages 717-740
DOI https://doi.org/10.1111/rssc.12198
Public URL https://durham-repository.worktribe.com/output/1351396

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2017 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.





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