History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation

• Published in: Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 66; no. 4; pp. 717 - 740
• Main Authors: Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M., White, R. G.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons Ltd 01.08.2017; Oxford University Press; John Wiley and Sons Inc
• Subjects: Antiretroviral drugs; Calibration; Computer simulation; Constraint modelling; Epidemiology; Gaussian processes; HIV; Human immunodeficiency virus; Immune system; Individual‐based models; Inverse problems; Matching; Original; Probability theory; Stochastic models; Stochastic simulators; Variance; Viruses; Stochastic simulators; Inverse problems; Calibration; Individual‐based models; Gaussian processes
• ISSN: 0035-9254; 1467-9876
• DOI: 10.1111/rssc.12198

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.