Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda
Vernon, Ian R.
McKinley, Trevelyan J.
Oakley, Jeremy E.
Nsubuga, Rebecca N.
White, Richard G.
PLoS Computational Biology
Public Library of Science
Copyright: © 2015 Andrianakis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was 10(11) times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.
This work was supported by a Medical Research Council (UK) grant on Model Calibration (MR/J005088/1) (http://www.mrc.ac.uk/). RGW is additionally funded by the Medical Research Council (UK) (G0802414), the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: Grants 21675/OPP1084276 and Consortium to Respond Effectively to the AIDS/TB Epidemic 19790.01), and CDC/PEPFAR via the Aurum Institute (U2GPS0008111). TJM is supported by Biotechnology and Biological Sciences Research Council grant number BB/I012192/1.
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Vol. 11, e1003968
Place of publication