Researchers are using COVID Symptom Study app data to map local hotspots

October 20, 2020

Since we launched the COVID Symptom Study app, we’ve had millions of users logging more than 170 million daily health reports. This has enabled our research team to make a huge amount of progress in understanding the symptoms of COVID-19, who is most likely to be affected, and how it is spreading through the population. 

But there is much more that can be done with this vast and vital dataset, so from the very start we worked with BREATHE, the Health Data Research Hub for respiratory health, to make anonymised data from the COVID Symptom Study app available for researchers and the NHS in order to help understand more about COVID-19 and plan the pandemic response.

The data is available for research through the SAIL databank in Swansea. This is what’s known as a Trusted Research Environment (TRE), which allows approved researchers to securely log in to  access and analyse the data, taking away only their results without ever receiving a copy of the data itself.

Dr Richard Fry, part of the Health Data Research UK (HDR UK) Wales and Northern Ireland team at Swansea University, works closely with SAIL. 

He’s been analysing the COVID Symptom Study app data using fine-level mapping methods to home in on geographical hotpots and discover more about how COVID-19 spreads.

Mapping COVID-19 hotspots

We already provide maps of COVID-19 prevalence by county, based on the number of people predicted to have COVID-19 based on symptoms and reported test results. But known risk factors for COVID-19 like social deprivation or ethnicity often vary over much smaller areas within towns and cities.

In order to target testing most efficiently and implement effective local controls to slow the spread of COVD-19, Richard and his collaborators wanted to pinpoint virus hotspots more accurately.

"We securely accessed the COVID Symptom Study app data through SAIL and used sophisticated mapping methods to create predictions of COVID-19 prevalence across the UK at high resolution in near real-time," he explains.

The team were able to map the likely number of cases over areas covering an average of 1500 people (1000 to 3000 people). These are smaller areas with more people in urban environments and bigger areas in rural locations, to ensure that it’s not possible to identify individual homes.

"We also produced confidence intervals around these predictions. So in each area, we have a predicted COVID-19 prevalence, and an indication of how reliable that prediction is," Richard says.  

How does COVID-19 spread across communities?

Published as a preprint in August, the models produced by Richard and the team show how the disease can move across communities.

"We've seen in recent months that the spread of COVID is very localised. It's a disease of proximity, and the interactions we have within a local community are what drives the spread of the virus," says Richard. 

He explains that when a disease hotspot forms, it can spread into the surrounding geographic areas over time if left unchecked, sometimes very quickly, so it’s important to get this detailed local data to keep track of what is happening ‘on the ground’. Richard and his team are feeding the results of their analysis through to the Scottish and Welsh governments on a weekly basis to help inform their pandemic responses. 

This is a great demonstration of how the COVID Symptom Study app is being used to generate new insights to help control the spread of the virus, and there are many other projects currently underway. It’s no surprise that the COVID Symptom Study app data is the most requested dataset through the Health Data Research Innovation Gateway - a new portal allowing researchers to discover and request secure access to more than 500 health datasets.

None of this would be possible without our dedicated users. As the virus continues spreading as we move into the winter months, we urge everyone to download the COVID Symptom Study app and spend just one minute a day logging your health to help beat the pandemic. Thank you.

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