The NHS COVID-19 (or ‘test and trace’) app helped prevent up to 600,000 infections and represented a breakthrough moment in medical app innovation.  The rigorously tested, medically approved app was created and launched in just 12 weeks – and downloaded by more than 22 million people. This is a story in itself.   

Another remarkable story is how digital twin technology within the app enabled decision makers to identify virus patterns, replace assumptions with evidence, and understand the implications of proposed policy changes.  

A digital twin is a type of computer programme that’s often confused with a simulation. Here’s the difference: where a simulation uses historical data to understand the outcome of a single process, a digital twin uses diverse and real-time datasets to run several simulations in parallel and understand the outcome of multiple processes. This way, a digital twin can help us accurately visualise, monitor, and optimise processes, services, and resources.

With appropriate use and data governance, digital twins have pioneering applications in healthcare – from clinical trials to precision medicine.  

As the global pandemic showed, they also have essential use cases in public health, helping to contain viruses and understand the implications of proposed safety measures and policies.    

Access to high-quality, consistent, and real-time data is essential for a timely and effective response during a crisis. But data collection on the ground is extremely difficult in such a situation. It can be slow, complex, and prone to error. What’s more, the data you collect is only as valuable as the framework you use to translate it into actionable insight.  

Given the urgency of the pandemic, there needed to be more time and resources to invest in additional data science, analytics, and engineering capacity. But analysis from our team showed a way to overcome these challenges. 

With access to secure and anonymised data from the COVID-19 app, a digital twin could translate epidemiological data into accessible and valuable knowledge. This in turn could prevent infections and help save lives.  

So how did this work in practice? 

Digital twin technology within the NHS COVID-19 app was used to create a behavioural feedback loop. This formed the basis for data science modelling and ongoing analysis to inform decision-makers about the spread of the virus.  

Here’s how it worked:  

1. Secure data collection: The digital twin gathered critical real-time data while ensuring anonymity and user privacy.  
2. Data science and modelling: This enabled us to perform data science and analysis – to build models and experiment with different scenarios.   
3. Scenario testing: Live testing enabled us to understand the implications of changing lockdown measures – for example, the impact of changing a specific location’s measures from level one to level two.  
4. Actionable recommendations: Epidemiological data was accessible to senior decision-makers in the UK via a dedicated channel, with data-backed recommendations on how to minimise COVID-19 outbreaks at a local and national level.  

In this way, the digital twin was instrumental in helping senior decision-makers anticipate and monitor their decisions' impact, with the agility to adapt measures in line with rapidly changing virus patterns.  

Internal dashboards provided the Steering Committee with live visualisations of cases, isolation payment claims, and other key impact measures. The committee used these visualisations to quickly grasp the implications of different options and inform their decisions. 

During a crisis, digital twin technology enabled rapid response and live testing – at an unprecedented national scale. The feedback loop to app users about their potential exposure to the virus helped them to manage their own risks and behaviours.   

At the same time, data from the app – combined with other datasets – informed policy and enabled policymakers to estimate the impact of ‘non-pharmaceutical interventions’ such as lockdown and tiering decisions.    

By converting data into actionable knowledge – and providing a means to visualise and monitor that knowledge in real-time – the digital twin enabled senior decision-makers to understand critical issues, including whether specific policy changes were helping to reduce hospital admissions.  

The technology was essential for addressing the pandemic's speed, scale, and complexity. Likewise, it will be pivotal in tackling other complex challenges in public health and healthcare systems. 

This example shows the vast potential of digital twins in healthcare. They can only succeed with the correct data, however. And this is a considerable challenge given the sensitivity of data in healthcare. But there are solutions to this challenge – from medical-grade apps to full-blown digital health platforms.

If you're interested in learning how a digital twin can be used in healthcare and other industries, get in touch with us today! Together with our clients, we find innovative solutions to complex challenges like COVID-19.