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Royal Society launches Covid-19 data analytics group

The group will advise the Scientific Advisory Group for Emergencies on its work on pandemic

The Royal Society has convened a new multi-disciplinary data analytics group to help the government respond to the coronavirus crisis.

Data Evaluation and Learning for Viral Epidemics, known as Delve, will aim to support a data-driven approach to learning from the different approaches other countries are taking to manage the pandemic, the learned society announced on 17 April.

According to the society, the group has already been discussed and welcomed by the government, who have arranged for it to feed into the government’s Scientific Advisory Group for Emergencies, Sage.

“We are at a crucial juncture in the UK’s response to the Covid-19 pandemic,” the Royal Society said. “There is a pressing need to analyse emerging data from countries around the world to identify the most important factors that can help slow the spread of the virus and help find long-term solutions to the pandemic.”

The efforts will include analysis of national and international data to determine the effect of different measures and strategies on a range of public health, social and economic outcomes, and will be divided across three working groups.

Data scientists and subject-matter experts will carry out data analysis, synthesise results and rapid reviews, while a wider advisory group of experts will provide review and feedback on those findings.

Finally, a committee of experts will oversee the overall progress and communicate the findings to the government’s chief scientific adviser Patrick Vallance.

One of the committee members, Tim Gowers, a professor of mathematics at the University of Cambridge told Research Professional News that “there are a number of very important questions that need answering, and many of them are of a kind where more than one approach to finding answers can be valuable”.

He added: “Delve has been convened not because of a feeling that existing advice is unsatisfactory, but because we need quick answers to difficult questions, so it is desirable to attack those questions from every possible angle.”

The work will complement modelling findings from the Royal Society’s Rapid Assistance in Modelling the Pandemic initiative that was launched earlier to help model the pandemic.

“It is important to stress that these are not rival approaches—they are complementary,” Gowers said. “We expect that our conclusions will normally agree with, and reinforce, those of Ramp, which will be useful for increasing confidence in those conclusions.

“Occasionally they might disagree, and that too would be valuable as a sign that a question needs to be investigated further.”