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A collaboration to support the work of pandemic modelling, influencing policy and saving lives

The Rapid Assistance in Modelling the Pandemic (RAMP) initiative was set up by The Royal Society to mobilise the UK’s wider scientific modelling community and bring a wide range of expertise from various different disciplines across academia and the private sector to support the work of the pandemic modelling.

This ongoing project working to model the current Covid-19 pandemic and to help guide the UK’s response, is made up of various strands, including an Urban Analytics task team which is led by LIDA’s Professor Mark Birkin. This team includes researchers from the University of Leeds and LIDA, Exeter, Cambridge and UCL plus non-academic partners who are working to create new models and which will inform Government’s advisory bodies, including the Scientific Advisory Group on Emergencies (SAGE) and support COVID-19 policymaking.

Professor Nick Malleson, Professor of Spatial Science at the Centre for Spatial Analysis and Policy at the University of Leeds who worked alongside Professor Birkin explains: “The aim of the Urban Analytics task team is to build a data driven computer model of disease spread to experiment with Covid-related policies.”

To do this, the team developed a fully functional model for the County of Devon and has developed tools to compare intervention scenarios for this demonstrator region.

The microsimulation of Devon layers various models and shows how individuals interact and move around. These movement patterns enable researchers to then connect behavioural patterns with data on disease transmission, providing policy makers and academics with tools to explore difference scenarios and the impact various interventions may have.

The simulation is underpinned by SPENSER, a micro-analytic model developed by one of the research teams at LIDA which creates estimates and future projection scenarios of the structure and behaviour of individuals and households. SPENSER is then layered with activity/behaviour models and data on supermarkets, schools and workplaces (provided by Cambridge and UCL) then topped with an epidemic simulation (provided by Exeter using public health data). From this, a virtual model of Devon’s entire 800,000-person population was created. This model has been replicated by Improbable simulation engineers who have made it faster and more interactive, which means the model can be scaled up from the 800,000 Devon population to cover all of the UK. Researchers can now run a month-long simulation of movement and viral spread involving nearly a million people in less than half a second. This allows the team to generate a huge amount of simulation data providing insights into the possible outcomes of different policies and activities.

This work has the potential to offer real significant benefit helping to create a national model of behaviour pattern showing social movement and mobility around schools, workplaces, parks, supermarkets – and how, if these places are locked down, does it change the implementation of other policies, and how this changes when varied from place to place.

“Currently all the ‘listen to the scientist’ and debate we see is about epidemiology but the purpose of the Urban Analytics theme within LIDA is to show how social behaviour is really important,” Professor Mark Birkin explains. “Lockdown strategies and social measures are designed, quite rightly, to remove the opportunity to transmit the disease, but the people who understand this are the social scientists.”

This data-science, cross-disciplinary approach is creating new models and insights to support existing research groups and inform the work of the Government’s scientific advisors. It will enhance modelling capacity to create clearer lockdown exit strategies, as well as allowing for more robust and comprehensive predictions that would not be possible otherwise. As Professor Mark Birkin says, this work will “put us in a better position for the future, whether that’s further lockdowns or working out how to deliver vaccines to a population of 60m. It provides an opportunity to influence policy and thinking, and save lives.”

Devon is a demonstrator region. Once trusted the model can be applied nationally and potentially beyond. “The model is data driven, so it could be applied in other countries and cities as long as they have the right kinds of data.” says Professor Nick Malleson.

The model code has now been fully open sourced and integrated into the project’s codebase.

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