History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation
Authored by I Andrianakis, I Vernon, N McCreesh, T J McKinley, J E Oakley, R N Nsubuga, M Goldstein, R G White
Date Published: 2017
DOI: 10.1111/rssc.12198
Sponsors:
European Union
Bill and Melinda Gates Foundation
United Kingdom Medical Research Council
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
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
method proposed is applied to a real complex epidemiological model of
human immunodeficiency virus 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 with a
previous variant of the method. The insight gained into the structure of
the human immunodeficiency virus model, and the constraints placed on
it, are then discussed.
Tags
individual-based models
calibration
population
systems
Inference
Gaussian processes
Bayesian uncertainty analysis
Gaussian process emulators
Inverse
problems
Stochastic simulators
Hiv-1-associated mortality
Galaxy formation