Publication details for Ian VernonAndrianakis, I., McCreesh, N., Vernon, I., McKinley, T. J., Oakley, J. E., Nsubuga, R., Goldstein, M. & White, R. G. (2017). Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model. SIAM/ASA Journal on Uncertainty Quantification 5(1): 694-719.
- Publication type: Journal Article
- ISSN/ISBN: 2166-2525
- DOI: 10.1137/16M1093008
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.