LCDS wins Impact Prize for data driven policy interventions during COVID-19
Professor Melinda Mills, Dr Jennifer Beam Dowd, and team (Leverhulme Centre for Demographic Science, Department of Sociology, University of Oxford)
The Leverhulme Centre for Demographic Science data driven policy interventions during COVID-19
Since the outbreak of the COVID-19 pandemic, the Leverhulme Centre for Demographic Science, led by Professor Melinda Mills, has been at the forefront of COVID-19-related research into patterns of COVID-19 mortality and excess mortality, ‘hotspots’ of infection, support bubbles, face coverings, and the deployment of vaccines – influencing both national and international policy.
In her role as a member of SAGE SPI-B (behavioural insights) and on the Royal Society’s Science in Emergencies Tasking COVID-19 (SET-C) group, Professor Mills led a number of high-profile studies, including on the effectiveness of face coverings (leading directly to the adoption of mandatory face coverings in indoor public spaces in England from July 2020) and on the social-behavioural factors underpinning vaccine deployment (informing government communication initiatives regarding vaccine deployment).
The LCDS’s research on social bubbles not only shifted public discourse on COVID-19 policies, action and protective measures, but also influenced both domestic policy and international practice in this field. The interdisciplinary team’s widely-cited research has also included the importance of demographic science and population composition on COVID mortality, the extent of children’s learning losses through school closures, and the forecasting and mapping of hospital ‘deserts’ by local authority early in the pandemic.
Reacting to their award, Professor Mills said, ‘Having a team of researchers in our Centre coming from multiple disciplines and countries allowed us to be agile, outward looking and to approach pandemic emergencies in a unique way. Working directly in a feedback loop with policy makers in the UK and around the world meant that our interdisciplinary data driven scientific approach was relevant and quickly translated into concrete policy suggestions.’