What is the QCovid Calculator?

The QCovid calculator is an evidence based risk prediction model that estimates your risk of hospitalisation or death from Covid-19. It uses factors such as age, sex, ethnicity and existing medical conditions to generate a detailed risk assessment. This helps you understand your risks of Covid-19.

• To find out your risk, visit qcovid.org/Calculation and complete the QCovid calculation form.

How Does it Work?

The health records of over 8 million people were analysed during the first wave of the pandemic. It was found that certain factors affected your risk of being hospitalised or dying from Covid-19. These risk factors include age, sex, BMI, ethnicity and existing medical conditions.

Each factor has a different effect on the level of risk and therefore has an associated weighting. A risk prediction algorithm then uses these weightings to estimate a patient’s absolute risk and relative risk.

• Absolute risk – overall risk based on what happened to other people who caught Covid-19 and had the same risk factors as you.
• Relative risk – risk compared to someone who is the same age and sex as you, but doesn’t have any other risk factors.

How was QCovid Developed?

Julie Hippisley-Cox, a professor at the University of Oxford, developed the original academic code behind the risk prediction algorithm. Oxford Computer Consultants (OCC) took on the challenge of turning this code into a usable product for clinicians, patients and software developers. All within an ambitious timeframe of eight weeks.

• Learn more about the risk prediction algorithm and how it was validated.

The first task was to transform the core code into a high performing, yet efficient algorithm. OCC’s developers are scientists themselves and so really understand the principles behind the research before turning it into software.

OCC developed and optimised an algorithm which can process 45 million medical records in just 90 minutes. A phenomenal improvement over the original target of 14 hours. Therefore, the algorithm can be updated regularly, ensuring its results remain accurate and relevant in the ever-changing landscape of Covid-19.

Once the code was developed, OCC then focused on making it accessible to everyone. A software development kit (SDK) library was created to allow other developers to include QCovid within their own software. While the code itself was written in .NET Core so that it could run effectively on both Windows and Linux.

To showcase an example of how to implement the algorithm, OCC then built and designed a public website, qcovid.org, for Oxford University Innovation. This was a collaborative effort to design a user interface which was easy to use, aesthetic and that complied with NHS standard styling.

Although, the real challenge was to obtain a declaration of conformity, so that the website could be used for medical purposes. To achieve this, OCC had to generate the necessary software documentation to successfully register the QCovid calculator as a Class 1 medical device.

"This website was originally just an example of how to implement the code. But it’s grown into something much bigger," highlights Tim Palmer, Head of Project Delivery at Oxford Computer Consultants.

"Over the last 3 months, we’ve had over a million users, reaching hundreds of thousands on some days. That’s much more than we expected, but our solution was robust and efficient enough to cope with these high loads."

How has QCovid Helped the Fight Against Covid-19?

QCovid has played a crucial role in assisting NHS Digital throughout the pandemic. It was used to develop both the Covid-19 Population Risk Assessment as well as the Clinical Risk Assessment tool. The former identified people who were clinically vulnerable, helping to determine the Shielded Patients List and who should be prioritised for vaccines. While the latter helped clinicians inform patients about their risk level.

Overall, OCC transformed research into a suite of tools that helped the NHS, clinicians and patients understand their risks of Covid-19. Providing some certainty, at a time when we need it most.