Revealed: early Covid symptoms differ by age group and gender

Researchers call for coronavirus testing criteria to be personalised according to these characteristics

Covid symptoms were known to differ by variant, but now other causes of difference have been identified. Getty

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Early symptoms of Covid-19 vary according to age, health and gender, and coronavirus testing should be personalised according to these characteristics, a new study suggests.

The differences are most noticeable between younger (16-59 years) and older age groups (60-80 years old), and between men and women.

The research, led by King's College London, published on Friday by Lancet Digital Health, analysed data from the Zoe Covid Symptom Study app between April 20 and October 15, 2020.

In a collaboration with the UK's health ministry, app contributors were asked to get tested as soon as they reported any new symptoms.

Researchers at King's then modelled the early signs of Covid infection, and successfully detected 80 per cent of cases when using three days of self-reported symptoms.

Researchers compared the ability to predict early signs of coronavirus using current National Health Service UK diagnostic criteria and a type of machine learning.

Examining 18 symptoms, the AI was able to discern that they were different across various groups in the early stage of infection.

For example, loss of smell, one of the key markers of coronavirus. In people over 60 it became less prevalent, and in people over 80 it was barely present at all.

Differences between genders were even starker. Men were more likely to report shortness of breath, fatigue, chills and fever, whereas women were more likely to report loss of smell, chest pain and a persistent cough.

Overall, the most important symptoms for earliest detection included loss of smell, chest pain, persistent cough, abdominal pain, blisters on the feet, eye soreness and unusual muscle pain.

"[Our findings] suggest that the criteria to encourage people to get tested should be personalised using individuals' information, such as age," said Dr Marc Modat, senior lecturer from the School of Biomedical Engineering and Imaging Sciences.

"Alternatively, a larger set of symptoms could be considered, so the different manifestations of the disease across different groups are taken into account.”

Dr Liane dos Santos Canas, first author from the School of Biomedical Engineering and Imaging Sciences, said: “Currently, in the UK, only a few symptoms are used to recommend self-isolation and further testing. Using a larger number of symptoms and only after a few days of being unwell, using AI, we can better detect Covid-19 positive cases. We hope such a method is used to encourage more people to get tested as early as possible to minimise the risk of spread.”