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Coronavirus (COVID-19) risk assessment

QCovid® is a coronavirus risk prediction model, created by the University of Oxford, which we're using to support the NHS coronavirus response.

QCovid® is an evidence-based risk prediction model that estimates a person's combined risk of:

  • catching coronavirus and being admitted to hospital
  • catching coronavirus and dying
  • dying of coronavirus following a positive PCR test 

How QCovid® was developed

The Chief Medical Officer for England asked leading academics, clinicians and scientists to create a way of predicting who may be at high risk of serious illness if they catch coronavirus.

A team of researchers, led by the University of Oxford, studied the anonymised health records of more than 8 million people using GP records, hospital records and mortality data from late January 2020 to April 2020. This initial analysis, funded by the National Institute for Health Research, was done using data collected during the first wave of the coronavirus pandemic in the United Kingdom.

The results showed that things such as age, sex assigned at birth, height and weight (used to calculate body mass index (BMI)), ethnicity and some medical conditions increased risk relating to coronavirus – these are known as risk factors.

Further research was done using more up to date data (up to June 2021), and the model has been refined and updated based on the latest findings. Factors such as vaccinated status and current infection rates are now reflected in the model and some conditions have been removed as risk factors where sufficient data was not available.  

QCovid® was designed to:

  • risk assess the general population
  • inform people about their risk level
  • support people with decisions about behaviours in consultation with a clinician

The QCovid® model is not a vaccine effectiveness study and is therefore not designed to be used for comparing someone’s risk when not vaccinated with their risk once vaccinated. 


How QCovid® works

QCovid® works by taking information about risk factors and converting each of these into values. These values are then combined in an equation that estimates risk and generates figures for absolute risk and relative risk.

Absolute risk is the overall risk, based on what happened to other people with the same characteristics and risk factors who caught coronavirus and went to hospital or died as a result.

Relative risk is the level of risk compared to a person who is the same age and sex registered at birth, and has the same vaccination status, but without any other risk factors.


To reflect the fact that some risk factors have a bigger impact on risk, some values contribute more to the result than others (weighting).

Some risk factors also interact with others, and this means that the extent to which each risk factor contributes to a person’s overall risk level (and risk assessment result) will depend on the individual.

  • as well as considering health condition risk factors, the risk may also be affected in combination with other factors such as a person’s age, sex or ethnicity
  • the impact of some risk factors is affected by the presence or absence of other factors. For example, the risk associated with Type 2 diabetes increases with age
  • the impact of some risk factors increases with their severity, for example, a higher level of obesity means a higher risk.
  • some risk factors affect men more than women, and vice versa

QCovid® is a complex model which cannot be simply summarised. However, detailed information about the QCovid® risk factors and their relative weightings is available in research published in the British Medical Journal:

View the list of health conditions and treatments considered by the COVID-19 Population Risk Assessment and the COVID-19 Clinical Risk Assessment Tool.  


Factors not incorporated into QCovid®

As with any model like this, QCovid® can only estimate risk and cannot take all factors into account. There are several things that are important to consider that are not included in QCovid®, such as:

  • an individual’s behaviour (for example hand washing, wearing face coverings and visiting friends or family)
  • occupation
  • local infection rates
  • local and national lockdown measures

How QCovid® has been validated

QCovid® has been peer reviewed, which means independent academic experts have checked that the research is robust.

The research and evidence both for the original version of QCovid® and the latest version were published in the British Medical Journal (BMJ), a respected academic medical journal which only accepts quality peer-reviewed research (around 6% of research papers submitted are published).

The data used to develop the original QCovid® model was collected in early 2020, it was later tested with new data and continued to perform well and accurately predict outcomes. The model has since been reviewed and updated in line with the latest available data and research. 

The Office for National Statistics (ONS) has independently validated the performance of QCovid®. The ONS has shown that the model performs well and accurately identifies patients at high risk from coronavirus. The NHS can therefore be confident that the model is robust and meets the highest standards of evidence.  

The QCovid® model has been embedded by the University of Oxford into the QCovid® Calculation Engine. This has been registered with the Medical and Healthcare products Regulatory Agency (MHRA) and categorised as a Class 1 medical device.

QCovid® is available online with a license that means the website can be used in Great Britain by clinically trained professionals, for academic research and for the purpose of peer review.


Updates to QCovid®

QCovid® is a ‘living’ risk prediction model. This means that, although it is not updated automatically in real-time, it can be updated periodically by the University of Oxford using the latest data and as we learn more about coronavirus.

New versions of the QCovid® model were published in November 2021 and we incorporated these changes into our COVID-19 Clinical Risk Assessment Tool on 25 November 2021. 


Last edited: 24 November 2021 2:58 pm