Edeling, W; Arabnejad, H; Sinclair, R; Suleimenova, D; Gopalakrishnan, K; Bosak, B; Groen, D; ... Coveney, PV; + view all Edeling, W; Arabnejad, H; Sinclair, R; Suleimenova, D; Gopalakrishnan, K; Bosak, B; Groen, D; Mahmood, I; Crommelin, D; Coveney, PV; - view fewer (2021) The impact of uncertainty on predictions of the CovidSim epidemiological code. Nature Computational Science , 1 (2) pp. 128-135. 10.1038/s43588-021-00028-9.

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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.

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