British scientists have developed a tool for people looking to assess their personal risk when it comes to dying from COVID-19.
While the Scottish Government are still urging people to stay at home, lockdown measures are beginning to ease down south in England and elsewhere around the world.
When the UK does return to normality, many people will still have to take extra precautions - especially those in the shielding category, who are deemed "'clinically extremely vulnerable".
Now, experts from University College London have revealed that more than eight million high risk people should remain indoors when lockdown is lifted, reports the Mirror.
The researchers insist that the only way to keep the coronavirus mortalities below 73,000 next year is to protect the vulnerable and warned that the virus could spread out of control if restrictions are lifted too quickly.
Researchers say the risk of death among people with underlying health conditions - which includes heart disease or diabetes - is five times higher than those without, while over 70s also high risk.
Estimating that 8.4 million Brits will need to avoid going outdoors to curb a spike in deaths, the team created the Covid-risk calculator that allows individuals to help people assess their own risk and take steps to safeguard themselves.
The tool calculates your risk based on factors that include the individual's age, sex and underlying health conditions to assess their effect on coronavirus mortality under different scenarios.
Dr Amitava Banerjee, lead researcher, said the calculator is an early prototype and provides one year mortality risks for common conditions by age and sex.
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He added: “Using data modelling on a number of different scenarios, our findings show the mortality risk for these vulnerable groups increases significantly, and could lead to thousands of avoidable deaths.
“Our calculator is the first to give doctors, health experts and the public a personalised risk from Covid.
“In the current emergency there is an urgent need to develop better understanding of who is at risk based on reliable health data.”
