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

This is the author accepted manuscript. The final version is available from Society for Industrial and Applied Mathematics via the DOI in this record.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 analyse models of this complexity, a central contribution of this work is that the history match is carried out using linear regression, an elementary and easier to implement statistical tool compared to the Gaussian process based emulators that have previously being used. Furthermore, we address a practical difficulty of history matching, namely, the sampling of tiny, non-implausible 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.This work was funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/J005088/1). RGW is additionally funded by the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276) and UNITAID (4214-LSHTM-Sept15; PO #8477-0-600