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Community-based study seeks to better understand COVID-19 spread

Home > News > Community Based Study Seeks Better Understand Covid 19 Spread

UCL has launched Virus Watch, inviting 50,000 households to take part in one of the largest and most comprehensive studies of COVID-19 in the UK.

The study, which will require participants to complete regular online symptom surveys, seeks to better understand community spread of the virus.



Project lead, Professor Andrew Hayward (UCL Epidemiology & Health Care) said: “Virus Watch is the world’s most comprehensive community study of COVID-19 and we want people from all walks of life and ethnic backgrounds to join.”

The findings will help us understand how populations respond to public health advice, including handwashing, social distancing, and travel bans, and will also seek to understand why the disease disproportionately affects some groups, and how our immune system protects us from disease.

Director of Research at UCL Computer Science and i-sense EPSRC IRC Deputy Director, Prof Ingemar Cox, said: “As many cases of COVID-19 are mild or asymptomatic and therefore may never present to public health systems, these large scale community studies can provide a more accurate picture of the pandemic.”

The two part study will bring together epidemiological data and modelling of online population data based on social media and search engine queries.

Community-based epidemiological analysis

The first part of the study, will include at least 25,000 individuals (2,500 in each region of England and Wales) for online symptom monitoring, and behaviour reporting through surveys. There will also be an optional use of a mobile app, allowing understanding of detailed movement patterns through access to the individual’s GPS data.

A sub-cohort of 10,000 individuals will have blood tests to look for antibodies and carry out self-swabbing when they are ill for detection of COVID-19 and other circulating viruses. A subset of households will undergo intensive self-swabbing and blood sampling following a household index case of suspected COVID-19. This will allow us to understand viral shedding and the role of asymptomatic infection in household transmission.

Big data to understand the impact of social distancing strategies

The second part of the study will build on experience of i-sense researchers working closely with Google, Microsoft and Public Health England to estimate the household secondary attack rate and serial interval of COVID-19.

Prof Cox, said: “The secondary attack rate and serial interval are important parameters to models of the spread of disease.”

“Improved estimates of these parameters should improve model accuracy, thereby better informing public policy.”

The UCL SpaceTimeLab for Big Data analytics will categorise tracking data into groups such as time at work and home, social venues, supermarkets, hospitals, GPs and on transport for incorporation into epidemiological analysis.

How will these findings support the NHS?

Findings from this study will inform various aspects of the public health response to future outbreaks of COVID-19, including better targeted interventions, clearer allocation of resources and picture of case load, and better understanding of risks to the community.  

The findings of the study will be shared with participants, health services and public health planners, as well as the general public to help manage the outbreak and better plan for future pandemics.

Results of the study will be regularly updated on the Virus Watch public dashboard.

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