Multiple simulations best for Covid-19 predictions

Computer modelling used to forecast Covid-19 mortality contains significant uncertainty in its predictions, according to a new study led by researchers at UCL and the CWI institute in the Netherlands. The authors of the study, performed for the Royal Society's RAMP initiative for Rapid Assistance in Modelling the Pandemic, highlighted that however well constructed such models are, they are only ever as robust as the "input" parameters - which include highly uncertain factors relating to how the disease is spread. The authors said the models should be regarded as "probabilistic" rather than being relied upon to produce a particular and specific outcome. They maintained 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. Professor Peter Coveney (UCL Chemistry), who leads the EU H2020 Computational Biomedicine Centre of Excellence as well as the EU VECMA programme on uncertainty quantificationthat undertook the study, said: "There is a large degree of uncertainty in the modelling used to guide governments' responses to the pandemic and this is necessary for decision makers to understand. "This is not a reason to disregard modelling. It is important that these simulations are understood in terms of providing a range of probabilities for different outcomes, rather than a single fixed prediction of Covid-19 mortality." "Because of this uncertainty, future forecasts of the death rates of Covid-19 should be based not on an individual simulation, but on lots of different simulations of a code, each with slightly adjusted assumptions.