Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model

Authored by Nicky McCreesh, Richard G White, Loannis Andrianakis, Ian Vernon, Trevelyan J McKinley, Jeremy E Oakley, Rebecca N Nsubuga, Michael Goldstein

Date Published: 2017

DOI: 10.1137/16m1093008

Sponsors: European Union Bill and Melinda Gates Foundation United Kingdom Medical Research Council

Platforms: NetLogo

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

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.

Tags

calibration systems Inference Emulation Computer-models Gaussian processes Linear regression Bayesian uncertainty analysis Gaussian process emulators Galaxy formation