Abstract
Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to contain the rapid spread of the COVID-19 pandemic during March and April 2020; however, CovidSim contains several sources of uncertainty that affect the quality of its predictions: parametric uncertainty, model structure uncertainty and scenario uncertainty. Here we report on parametric sensitivity analysis and uncertainty quantification of the code. From the 940 parameters used as input into CovidSim, we find a subset of 19 to which the code output is most sensitive-imperfect knowledge of these inputs is magnified in the outputs by up to 300%. The model displays substantial bias with respect to observed data, failing to describe validation data well. Quantifying parametric input uncertainty is therefore not sufficient: the effect of model structure and scenario uncertainty must also be properly understood.
Highlights
Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19
We have performed an analysis on the original closed-source version of the code; the majority of our sensitivity analysis and uncertainty quantification efforts lie with the current updated open-source release of CovidSim
We identify the following three sources of uncertainty in CovidSim; namely, parametric uncertainty, model structure uncertainty and scenario uncertainty
Summary
Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19. It is a modified version of an earlier model designed to support pandemic influenza planning[1] and has been used to explore various non-pharmaceutical interventions (NPIs) with the aim of reducing the transmission of the coronavirus, as documented in the key paper[2] denoted Report 9. We will use a dimension-adaptive sampling method for this purpose[9] to be able to handle the high-dimensional input space This type of anisotropic sampling method adaptively exploits a possible low effective dimension, where only a subset of all inputs have a substantial impact on the model output. We will argue the case for the prediction of uncertainty in high-impact decision-making, after we first describe our results
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