Updated 9th April 2022

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At least one in five infected with COVID-19 don't show any symptoms

Written byZOE Editorial Staff

    Have you had COVID-19 without knowing? New study reveals at least one in five people infected don't show any symptoms

    A new antibody testing study led by researchers at King’s College London has shown that one in five people in London and the South East of England who have been infected with coronavirus didn’t show any symptoms of COVID-19, while more than a quarter who did fall ill didn’t have the three core signs of the disease: persistent cough, fever and loss of smell (anosmia).

    This is the first UK-based study linking detailed ongoing symptom collection data with antibody testing, and highlights the likely extent of COVID-19 infection across the region. 

    To understand the true picture of the disease we not only need to know who is infected right now - currently determined through swab testing - but the extent to which people have previously been exposed to the SARS-CoV-2 coronavirus, as revealed by testing for antibodies in the blood. 

    The virus is difficult to study as some people become seriously or fatally ill, others will have a wide range of less severe symptoms, while some will have very mild or no symptoms at all (asymptomatic). 

    The researchers carried out antibody testing on blood samples from 432 adults aged 18-89 in the ongoing TwinsUK study living in Greater London and South East England, 382 of whom had also been regularly logging their health over three months using the COVID Symptom Study app. Participants were also given a swab PCR test to check whether they were currently infected with coronavirus, and asked if they had ever previously been tested.

    The team discovered that one in 8 (51, 12%) were positive for viral antibodies. This is approximately double the proportion estimated by a recent ONS survey of antibody testing in the UK general population

    Focusing on 48 people with a positive antibody result who had also been regularly logging their health, the researchers found that nearly one in five (9, 19%) never experienced any COVID-19 symptoms throughout the duration of the study. 

    More than one in four (16, 27%) who had antibodies and experienced symptoms didn’t have the 3 core NHS symptoms of persistent cough, fever and anosmia. However, loss of smell alone was still highly specific for having COVID-19, compared with the combination of all three symptoms, highlighting its importance as a key early warning sign of the disease.

    Additionally, around half of the app users who reported symptoms that were highly predictive of COVID-19 did not have antibodies against the virus, confirming other studies showing that current antibody testing misses a substantial fraction of cases.  

    This could be some because antibodies fade quickly in some people, or they never create an antibody response at all. Further work by the team is looking into whether alternative aspects of immunity, such as T cells, may also play a part in the immune response to the virus.

    Understanding the true extent of infection and transmission is vital in order to effectively identify and contain further outbreaks across the UK. This study highlights the fact that a significant proportion of the population in London and the South East are likely to have been exposed to coronavirus, many of them without showing any symptoms at all. 

    Study lead and consultant geriatrician Dr Claire Steves said:

    “Our findings highlight the fact that a significant proportion of people who get infected with coronavirus don’t have any obvious symptoms but may still pass the disease on to others. It’s essential that we all take steps to protect the health of everyone by sticking to social distancing guidelines, wearing face coverings in public and following good hand hygiene practices.”

    “Testing is much more available now,” she added, “So if you have symptoms you can’t explain - get a swab or saliva test, as that way you will know to protect yourself and the people around you.”

    COVID Symptom Study lead Professor Tim Spector said:

    “These results suggest that both swab and antibody testing significantly underestimate the extent of coronavirus infections in the population. We need to combine testing approaches together with getting as many people as possible logging their daily health through the COVID Symptom Study app to really understand the spread of the virus and control it over the months ahead.” 

    The findings are available online as a pre-print, and have been submitted to a scientific journal for rapid peer review and publication.

    About King’s College London

    King's College London is one of the top 10 UK universities in the world (QS World University Rankings, 2018/19) and among the oldest in England. King’s has more than 31,000 students (including more than 12,800 postgraduates) from some 150 countries worldwide, and some 8,500 staff.

    King's has an outstanding reputation for world-class teaching and cutting-edge research. In the 2014 Research Excellence Framework (REF), eighty-four per cent of research at King’s was deemed ‘world-leading’ or ‘internationally excellent’ (3* and 4*).

    Since our foundation, King’s students and staff have dedicated themselves in the service of society. King’s will continue to focus on world-leading education, research and service, and will have an increasingly proactive role to play in a more interconnected, complex world. Visit our website to find out more about Vision 2029, King’s strategic vision for the next 12 years to 2029, which will be the 200th anniversary of the founding of the university.  

    World-changing ideas. Life-changing impact. Kcl.ac.uk

    About ZOE

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    Located in London and Boston, ZOE was founded by Professor Tim Spector of King’s College London, machine learning leader Jonathan Wolf and entrepreneur George Hadjigeorgiou. ZOE has carried out the largest nutritional studies of their kind in the world, and was named one of the Deloitte Fast 50 Rising Stars in 2019 for the company’s contribution to science enabled by technology and machine learning. 

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