Home » Research Review 2020-21 » Research Review: Covid


As the COVID-19 pandemic progressed, we saw how fraught with uncertainty prediction could be, especially where human decision-making was concerned. Using big data to understand behavioural change and the consequences of policy interventions enabled our researchers to produce widespread and detailed evidence.   
The success of such expansive projects has relied on having access to a wide range of datasets. This is why cross-industry collaboration is so important – existing partnerships and networks at the Consumer Data Research Centre enabled our researchers to rapidly establish projects with partners from industry, academia, government and the third sector to help respond to the pandemic.   
Locally, we have been working with Leeds City Council on a data-driven model to help them test and understand the implications of different policy scenarios on a local level.  

Rapidly responding to new spatial modelling needs 

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 range of expertise from different disciplines across academia and the private sector. 

The ongoing project, which aims to model the COVID-19 pandemic and help guide the UK’s response, is made up of various strands, including an Urban Analytics task team which is led by the CDRC’s Professor Mark Birkin.  

The team, which includes researchers from the Universities of Leeds, Exeter, Cambridge and UCL, plus non-academic partners, have been building a data-driven model of disease spread to enable policy makers to understand the potential impact of different COVID-related policies. 

This data science, cross-disciplinary approach is creating new models and insights that can be used to support existing research groups and inform the work of the Government’s scientific advisors. It has enhanced modelling capacity to create clearer lockdown exit strategies, as well as allowing for more robust and comprehensive predictions that would not be possible otherwise. 

Measuring changes in mobility as COVID-19 progressed

Understanding the impact of new policy restrictions on behaviours has been an important objective of governments everywhere in handling the COVID-19 pandemic. The UK Government have made use of a wide range of data sources – from Google Mobility data and credit card spending patterns, to traffic flow counts and conventional surveys – in order to gain insights into the impact of different measures. Yet, there remain gaps in our understanding, and a new project conducted by the CDRC, in partnership with Cuebiq, has narrowed down on one issue in particular. 
Through the LIDA Data Scientist Internship project, Stuart Ross and George Breckenridge, under the supervision of Professor Ed Manley and Dr Mengdie Zhuang, investigated the prevalence of household visitation during the course of the pandemic. Visits to other households have been periodically restricted and relaxed since March 2020, but there has been little evidence available on how effective these policies have been. Using fine-grained mobility data collected by Cuebiq, and accessed via their online privacy preserving data platform, the CDRC researchers were able to construct metrics relating to the variation in household visitation relative to pre-pandemic baselines.  
The metrics reveal new insights into the spatial and temporal concentration of household visitation, highlighting how the effectiveness of the policy ebbed and flowed in the face of continued restrictions. The trends reveal how household visitation returned towards baseline levels in May 2020, despite the continuation of restrictions, and how different regions responded differently to the limitations. The metrics also reflected a period of ‘vaccine relaxation’ in Spring 2021, with strong correlation between increasing numbers being vaccinated and the extent of household mixing. These metrics are potentially useful in yielding new insights into human behaviour during the pandemic, and discussions are underway with government partners around the adoption of these techniques into policymaking practice.  

Forming the Emergent Alliance – a powerful data alliance to aid economic recovery after COVID-19 

The Emergent Alliance is a not-for-profit collaboration of large organisations, small businesses, institutes and individuals who share knowledge, data and skills to inform decision making on regional and global economic challenges related to COVID-19.  

The CDRC’s Professor Mark Birkin worked alongside consortium-leader Rolls-Royce to develop the concept, and the Leeds Institute for Data Analytics (LIDA) is one of a group of founding members, including IBM, Microsoft and Google Cloud.   

As the lead academic institution within the Alliance, LIDA provides members with secure infrastructure, scientific expertise and access to global academic research networks.   

Since it was launched in April 2020, the Emergent Alliance has grown to include over 65 organisations. Its combined ecosystem has the resources and power to solve some of the biggest challenges facing a post COVID-19 world.   

Two examples of the application of this work are the finding that rail is still safer than road (with caveats) and the Job Finder Machine, a tool to help regional re-skilling and redeployment. 

Helping UK Local Authorities to tackle widening inequalities  

When the COVID-19 pandemic struck, already strained Local Authorities resources were stretched even further, with infection and transmission of the virus exacerbating existing social inequalities. In order to support the response of  local authorities, groups and stakeholders to COVID-19, the Local Data Spaces (LDS) project was set up – a collaboration between the Consumer Data Research Centre (CDRC), the Joint Biosecurity Centre (JBC), the Office for National Statistics (ONS) and ADR UK.  
CDRC researchers – Jacob MacDonald, Dr Mark Green, Dr Maurizio Gibin and Simon Leech – identified two core research priorities  which focused on broader COVID-19 health impacts and inequalities, and economic vulnerability and recovery potential.  Using data from the ONS’s Secured Research Service (SRS), a series of reports were generated, specific to each local area, investigating themes including demographic and ethnic inequalities in COVID-19, excess mortality, economic vulnerabilities and human mobility.  
The primary beneficiaries of Local Data Spaces have been Local Authorities in England. The project engaged with 25 Local Authorities to co-produce research questions and 10 reports were generated for all 314 Local Authorities in England. These reports diversified the available information – highlighting new information to policymakers of which they would otherwise not have been aware – as well as provided much-needed research capacity lacking within the Local Authorities themselves.  
These data insights invariably supported policy decisions. For example, the LDS team played a pivotal role in Liverpool City Council’s (LCC) ‘mass testing’ pilot (the first large-scale pilot of asymptomatic testing of COVID-19 in the UK). First, they mapped accessibility of test sites and showed large areas in Liverpool with poor access. Subsequently, Liverpool City Council tasked the LDS team to identify the best locations to open 12 new testing sites for their ‘Super Weekend’ event in order to encourage greater participation in testing. The LDS team also presented evidence on inequalities in testing uptake, allowing Liverpool City Council to revise their strategy to tailor their outreach activities. Data insights from the project formed part of presentations to Department of Health and Social Care, UK Government and SAGE on lessons learnt for shaping the national roll-out of asymptomatic COVID-19 testing. 

The project was also able to work with colleagues in the ONS to answer timely research questions on COVID-19 outcomes among younger women (20-40 years) at the request of the UK Government and SAGE. The data identified, for the first time, that while the younger women had no overall difference in prevalence of COVID-19 compared to males of the same age this was not even across all work sectors (e.g. teachers and hairdressers had higher risks). These data then supported SAGE and Government decisions and were referenced in two SAGE reports (11 February 2021 and 24 March 2021). 
The Local Data Spaces project has subsequently won the prestigious ONS Research Excellence Project Award 2021

Understanding isolation and exclusion in a post-social distancing COVID-19 world

CDRC Data Scientist Intern, Rosalind Martin, working with Professor Susan Grant-Muller, Professor Alison Heppenstall and Dr Vikki Houlden from the University of Leeds, and Professor Rachel Franklin from the University of Newcastle, has produced a dashboard that identifies geographical areas which might experience increased isolation and exclusion as we leave the COVID-19 pandemic and lockdowns. 

SPENSER (a synthetic population) was used to identify individuals and households at risk from five COVID-19 restrictions: shielding, school closures, limited household interaction, furlough and limited to local area, along with households at risk from unique combinations of these five scenarios. This has been translated onto a dashboard which displays additive counts of household level impacts at the Middle Layer Super Output Area (MSOA) level.

Dashboards with mapping have shown to be an important tool for understanding how health impacts of COVID-19 are distributed, and this same logic applies to how lockdown restrictions combine spatially. Although patterns of household-level impacts are difficult to see from these maps, this work has explored how to use proxy data in order to identify individual- and household-level impacts from COVID-19 restrictions, and begun to unpack the complexities of combining data at the household level. This is something that must continue going forward as academics and policy makers continue to face the challenges that accompany understanding the social and spatial impacts of the emergence from lockdown. 

Unsafe inhaler prescribing, social demographics and the COVID-19 lockdown 

An increase in inhaler prescriptions during the pandemic has meant that research using CDRC data to explore patterns of asthma inhaler prescriptions in England has been particularly relevant this year.  
Professor Alison Heppenstall and Dr Roger Beecham have been working to identify which practices are vulnerable to supply line shortages based on historical prescribing patterns and demographic factors, providing insight for large policymaking organisations such as NIHR, NICE and the NHS.  
The next step for the project, which is a partnership with The Alan Turing Institute, will involve patient engagement and public dissemination of the findings, in order to ensure the public are aware of the practical changes for asthma sufferers that are implied by current and COVID-related prescribing practices.