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Efficient history matching of a high dimensional individual-based HIV transmission model.

Andrianakis, I. and McCreesh, N. and Vernon, I. and McKinley, T. J. and Oakley, J. E. and Nsubuga, R. and Goldstein, M. and White, R. G. (2017) 'Efficient history matching of a high dimensional individual-based HIV transmission model.', SIAM/ASA journal on uncertainty quantification., 5 (1). pp. 694-719.


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.

Item Type:Article
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Publisher statement:© 2017, Society for Industrial and Applied Mathematics
Date accepted:27 March 2017
Date deposited:20 September 2017
Date of first online publication:01 August 2017
Date first made open access:No date available

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