Technical advice for the NHS COVID-19 app

Explaining the science

For a contact tracing system (automated or manual) to be trusted and effective, it will have to identify those who are actually at risk of contracting the virus and inform them of the need to self-isolate. If the contact tracing system is unable to accurately identify those who need to self-isolate, for example by notifying people who are not at risk (known as a ‘false positive’) or by not identifying pre-symptomatic and asymptomatic individuals (known as a ‘false negative’), the consequences may be severe. Self-isolation may negatively impact people’s lives, by compromising their ability to go to work and feed their family, or raising the level of anxiety that some may feel at being told that they are at risk of having the virus. On the other hand, not notifying individuals who carry the virus can increase the reproduction number, resulting in potential outbreaks, unnecessary deaths and, as a result, restrictive policy decisions being enforced, further impacting the economy and societal fabric. It is therefore important to have an app that is able to appropriately characterise risk; that is, individuals who are actually at risk of contracting and spreading the virus are notified to self-isolate.

 

Project aims

The Turing researchers’ main contributions have been analysis of epidemiological models using risk scoring methods, and an assessment of distance estimation technology.

We have developed four new ideas:

• Introducing a probabilistic framework to the risk score to assess and compensate for uncertainties in the distance and duration calculations.
• Creating a new likelihood function that encapsulates many forms of environmental error in the Bluetooth signal such as multi-path and attenuation. We have used the MIT dataset in order to evaluate the performance of this new function, verified in 5 simulation and against other trials data.
• Integrating a statistical approach to distance estimation via an Unscented Kalman Smoothing algorithm. This specific algorithmic choice is a compromise between 6 computational complexity, mathematical complexity and accuracy. We have demonstrated an improvement in localisation accuracy (compared to other widely used approaches), in simulation and on the MIT dataset.
• Exploring the use of existence probabilities in order to incorporate the actual probability of people staying in close proximity for a long period of time versus them having moved in and out of contact during that time.