Turing researchers work on the NHS COVID-19 contact tracing app


Following a request from NHSx shortly before its pilot launch on the Isle of Wight, professors Chris Holmes (Programme Director for Health and Medical Sciences) and Mark Briers (Programme Director for Defence and Security) have been giving independent advice to scope the technical development and help to oversee modelling and analytics of the NHS COVID-19 app. 

Chris Holmes said: “Our main contributions have been analysis of epidemiological models using risk scoring methods, and an assessment of distance estimation technology. Our role has been advisory, with the goal to inform the NHS Test and Trace programme’s understanding of the spread of the virus and help to optimise the performance of the app.”

Early results from the distance estimation work have found that using Bayesian statistics methodology to analyse Bluetooth signal strength, algorithms can be created which better estimate the distance between two smartphones running a contact tracing app.

Mark Briers, who is leading on the distance estimation work, said:

“A contact tracing app must estimate the distance between two people based on the strength of the Bluetooth beacon from one phone when it reaches the other. We have re-purposed a sequential Bayesian algorithm to help to solve this problem, which significantly improves distance estimation accuracy. This could mean that a contact tracing app can more accurately ensure more people that are at risk of spreading COVID-19 are requested to self-isolate.

“While these are early initial results and more testing is required, this could be a useful research output for all contact tracing apps.”

For more information

A pre-print paper (currently under peer review) on risk scoring calculation https://arxiv.org/abs/2005.11057

See below for a pre-print paper on distance estimation technology.