Scientists from Kings College London and the Secure Anonymised Information Linkage (SAIL) Databank at Swansea University will work with the Welsh Government to analyse the data from COVID-19 Symptom Tracker app.
This collaborative effort will utilise the capabilities of SAIL Databank to facilitate a secure, anonymised data pipeline to deliver information from a new COVID-19 symptom tracking app into the NHS, supporting the response to the pandemic.
The wealth of data within SAIL Databank has proven to be useful for recent, similar research. The databank was used to validate the results of a 2019 respiratory health study – ‘Identifying people most at risk of a severe asthma attack using routine electronic healthcare record data’1.
The study established an algorithm for predicting who may be at high-risk of a severe Asthma attack and identify individuals for a trial to evaluate existing ‘at-risk’ registers within the GP primary care setting.
This algorithm could be used to benefit the NHS by reducing hospital admissions if these individuals were prioritised for primary care.
Two key objectives of the COVID-19 Symptom Tracking App aims to do just that: Identify high risk areas in the country and identify who is most at risk by better understanding symptoms linked to health conditions.
First Minister, Mark Drakeford said:
“Having a range of evidence and data is crucial in helping us build a clear picture of how the virus is behaving and affecting everyone’s lives. Crucially this app can help us anticipate potential COVID hot spots and get our NHS services ready. I’m asking everyone in Wales to download the new COVID Symptom Tracker app, so you can help protect our workers and save lives. Together we can build the best scientific picture, so we are better armed to fight this terrible disease.”
The developers hope the new tracker app data will help the NHS support sick individuals.
1Clark A, Stirling S, Price D, et al, P145 Identifying people most at risk of a severe asthma attack using routine electronic healthcare record data, Thorax 2019;74:A170.
Cristina Menni, Ana M. Valdes, Maxim B. Freidin, Carole H. Sudre, Long H. Nguyen, David A. Drew, Sajaysurya Ganesh, Thomas Varsavsky, M. Jorge Cardoso, Julia S. El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth C. E. Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Sebastien Ourselin, Andrew T. Chan, Claire J. Steves & Tim D. Spector Real-time tracking of self-reported symptoms to predict potential COVID-19, Nature Medicine,2020
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BY CHRIS ROBERTS, SWANSEA UNIVERSITY
SAIL Databank is one of the nine Centres of Excellence based in Population Data Science at Swansea University Medical School