History matching of a complex epidemiological model of HIV transmission using variance emulation
Journal of the Royal Statistical Society Series C: Applied Statistics
Wiley for Royal Statistical Society
© 2017 The Authors Journal of the Royal Statistical Society: Series C Applied Statistics published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre-)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model’s input values. The proposed method is applied to a real complex epidemiological model of HIV in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared to a previous variant of the method. The insight gained into the structure of the HIV model, and the constraints placed upon it, are then discussed.
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).
This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.
Published online 24 November 2016