Infectious 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.

The authors have declared no competing interest.

This study was funded by the EPSRC UQ4Covid project, grant number EP/V051555/1. TJM, LD and RC were also supported by the Medical Research Council (MRC) through the JUNIPER Consortium (grant number: MR/V038613/). DM was supported by the Met Office Hadley Centre Climate Programme funded by DSIT. DW was funded by AI for Net Zero grant EP/Y005597/1: ADD-TREES, LD and RC were also supported by EPSRC Grant No. EP/Y028392/1: AI for Collective Intelligence (AI4CI), and LD was supported by MRC (grant number MC/PC/19067).

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We used publicly available data throughout, except for data on hospital stay times where we used data from the CHESS study. These data were supplied after anonymisation under strict data protection protocols agreed between the University of Exeter and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the UK government SPI-M(O)/SAGE committees.

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All data produced by the present study are available upon reasonable request to the authors