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Bayesian history matching of complex infectious disease models using emulation: A tutorial and a case study on HIV in Uganda

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

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Authors

Ioannis Andrianakis

N. McCreesh

T.J. McKinley

J.E. Oakley

R. Nsubuga

M. Goldstein

R.G. White



Abstract

Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.

Citation

Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T., Oakley, J., Nsubuga, R., …White, R. (2015). Bayesian history matching of complex infectious disease models using emulation: A tutorial and a case study on HIV in Uganda. PLoS Computational Biology, 11(1), Article e1003968. https://doi.org/10.1371/journal.pcbi.1003968

Journal Article Type Article
Acceptance Date Oct 8, 2014
Online Publication Date Jan 8, 2015
Publication Date Jan 8, 2015
Deposit Date Jan 6, 2016
Publicly Available Date Feb 3, 2016
Journal PLoS Computational Biology
Print ISSN 1553-734X
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Article Number e1003968
DOI https://doi.org/10.1371/journal.pcbi.1003968
Public URL https://durham-repository.worktribe.com/output/1423714

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

Copyright Statement
© 2015 Andrianakis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.





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