On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1
Williamson, D. B.; McKinley, T.; Xiong, X.; Salter, J. M.; Challen, R.; Danon, L.; Youngman, B. D.; McNeall, D.
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AO_SCPLOWBSTRACTC_SCPLOWInfectious disease models are used to predict the spread and impact of outbreaks of a disease. Like other complex models, they have parameters that need to be calibrated, and structural discrepancies from the reality that they simulate that should be accounted for in calibration and prediction. Whilst Uncertainty Quantification (UQ) techniques have been applied to infectious disease models before, they were not routinely used to inform policymakers in the UK during the COVID-19 pandemic. In this paper, we will argue that during a fast moving pandemic, models and policy are changing on timescales that make traditional UQ methods impractical, if not impossible to implement. We present an alternative formulation to the calibration problem that embeds model discrepancy within the structure of the model, and appropriately assimilates data within the simulation. We then show how UQ can be used to calibrate the model in real-time to produce disease trajectories accounting for parameter uncertainty and model discrepancy. We apply these ideas to an age-structured COVID-19 model for England and demonstrate the types of information it could have produced to feed into policy support prior to the lockdown of March 2020.
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