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.
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|Publisher Web site:||https://doi.org/10.1137/16M1093008|
|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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