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New funding for Universities working together on Covid-19 pandemic modelling to advise Government bodies

A national consortium, being co-led by Professor Matthew Keeling here at Warwick, brings together leading mathematical and statistical research modellers from seven UK universities, whose work will produce in depth predictions about the Covid-19 pandemic.

The consortium has received £3 million in funding from UK Research and Innovation. The funding enables the joint research groups across the universities to come together to boost their speed and capacity, but also advance their accuracy with new data and growing knowledge.

The JUNIPER consortium (‘Joint UNIversities Pandemic and Epidemiological Research’) will advise UK government bodies based on their bespoke models to provide predictions and estimates on key questions arising about the Covid-19 pandemic. The results feed regularly into the Scientific Pandemic Influenzas Group on Modelling (SPI-M) that provides evidence to the Scientific Advisory Group for Emergencies (SAGE) and the wider UK government.

Some examples of modelling the consortium provides to the government includes:

  • Understanding how new Covid variants are spreading across the UK and developing statistical models to determine whether a new variant is causing more hospitalisations or death
  • Forecasting and providing real-time estimates of the R value, using data from many sources including 2 testing, hospital data and mobility data. They are currently providing eight of 12 models contributing real time R estimates that go from SPI-M to SAGE each week
  • Modelling the effectiveness of different testing strategies on virus transmission and suppression, and modelling the effect of vaccinations and predicting outcomes from different scenarios of how to ease lockdown restrictions.

The research groups in the consortium are based at seven universities: University of Cambridge University of Warwick, University of Exeter, University of Oxford, University of Bristol, The University of Manchester and Lancaster University. They plan to make their models open-source, so scientists worldwide can access them and benefit.

Professor Matt Keeling, co-lead of the consortium from the University of Warwick, said:

“We’re generating about half the models for the nowcasting that goes into SPI-M and SAGE every week. This consortium allows us to not only boost our speed and capacity, but also to continue to advance the accuracy of our models using the new data and growing knowledge from the pandemic.”

“Standard epidemiological modelling tools have worked well so far, but the future with COVID-19 now demands a suite of new tools to deal with the upcoming complexities of the pandemic, such as localised regional outbreaks, growing understanding of socioeconomic differences with this disease, complexities of imperfect vaccines and the growing problem ahead with new variants. Having several teams using different models working on the same problem helps us to verify our results and makes the consortium much bigger than the sum of its parts.”

Professor Julia Gog, co-lead of the consortium from the University of Cambridge, said:

“By bringing research groups together from our seven universities we can provide predictions and estimates about the pandemic to address questions from the government with unprecedented speed. By combining the right expertise together swiftly across research teams we can now respond to questions in less than 24 hours, which might have taken a week for one team working alone. And further, being able to call upon specialist expertise combinations across multiple research groups means we can provide more robust outputs.”

“In this unprecedented pandemic, modelling has been hugely important to provide evidence-based predictions and estimates and at great speed. Our insights from transmission modelling are fully integrated with scientific evidence from other disciplines and feed into government decision making.”

Professor Charlotte Deane, COVID-19 Response Director at UKRI, said:

“This consortium enables disease modellers to pool their expertise nationally to increase the scale, speed and quality of their models of policy options and predictions for the pandemic. They’ll provide cutting-edge evidence about the pandemic into the UK government’s decision-making.”