History matching of a complex epidemiological model of HIV transmission using variance emulation

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.