CovidSim, the model used to inform the UK Government’s response to the pandemic has been analyzed by researchers at UCL, Brunel University, the CWI institute in the Netherlands, the Poznan Supercomputing and Network Center and the University of Amsterdam, and has been found to contain a large degree of uncertainty in its predictions, leading it to seriously underestimate the first wave. The researchers who performed this study, members of the two EU consortia VECMA and CompBioMed, both of which are led by UCL, undertook an extensive parametric sensitivity analysis and uncertainty quantification of the publicly available code. The study concluded that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense. Motivated by this finding, the scientific teams of the two EU consortia call for a better public understanding of the inherent uncertainty of models predicting COVID-19 mortality rates, saying they should be regarded as “probabilistic” rather than being relied upon to produce a particular and specific outcome. They maintain that future forecasts used to inform government policy should provide the range of possible outcomes in terms of probabilities to provide a more realistic picture of the pandemic framed in terms of uncertainties. This study has now been published in Nature Computational Science and the article has been made freely accessible via this link.