There are two major kinds of testing for COVID-19 i.e the viral testing which tells does you currently infected with COVID-19 or not and the antibody test tells if you have the previous infection of COVID-19 or not.
The researchers from King’s College London, Massachusetts General Hospital, and health science company ZOE have recently published a paper in Nature Medicine in which they describe an artificial intelligence-based diagnostic tool that predicts whether someone is likely to have COVID-19 without the need of testing.
The AI model uses data from the COVID Symptom Study app to predict COVID-19 infection, by comparing people’s symptoms and the results of traditional COVID tests. Researchers say this may provide help for populations where access to testing is limited. Two clinical trials in the UK and the US are due to start shortly.
The researchers in there study noted that more than 3.3 million people globally have downloaded the COVID Symptom Study app, and are using it to report daily on their health status, whether they feel well, or have any new symptoms such as persistent cough, fever, fatigue, and loss of taste or smell.
The model was trained on data from more than 2.5 million users of the COVID Symptom Study app developed at King’s College London, which anyone can download to report their daily health status.
Around a third of the users had logged symptoms associated with COVID-19. Some 18,374 reported having had a test for coronavirus, 7,178 of whom had tested positive.
In their study, the researchers investigated which symptoms known to be associated with COVID-19 were most likely to be associated with a positive test. After spending lots of time analyzing the data they found a wide range of symptoms compared to cold and flu, and warn against focusing only on fever and cough.
Their findings suggest that focusing on fever and cough is dangerously insufficient. In fact, their most common predictor of COVID-19 was the loss of taste and smell — known as anosmia.
They found loss of taste and smell (anosmia) was particularly striking, with two-thirds of users testing positive for infection reporting this symptom compared with just over a 5th of the participants who tested negative.
The findings suggest that anosmia is a stronger predictor of COVID-19 than fever, supporting anecdotal reports of loss of smell and taste as a common symptom of the disease.
Using a new mathematical model which they created, the scientists then predicted with nearly 80 percent accuracy whether an individual is likely to have COVID-19 based on their age, sex, and a combination of four key symptoms i.e loss of smell or taste, severe or persistent cough, fatigue and skipping meals.
Applying this model to the entire group of over 800,000 app users experiencing symptoms predicted that just under a fifth of those who were unwell (17.42%) were likely to have COVID-19 at that time.
Professor Tim Spector from King’s College London said: “Our results suggest that loss of taste or smell is a key early warning sign of COVID-19 infection and should be included in routine screening for the disease.
We strongly urge governments and health authorities everywhere to make this information more widely known, and advise anyone experiencing the sudden loss of smell or taste to assume that they are infected and follow local self-isolation guidelines.”
Researchers suggest that combining this AI prediction with the widespread adoption of the app could help to identify those who are likely to be infectious as soon as the earliest symptoms start to appear, focusing tracking and testing efforts where they are most needed.
Citing the limitations of the study, the researchers said the prediction is based on the self-reported nature of the included data, which they said cannot replace physiological assessments of smell and taste function, or testing people’s samples for SARS-CoV-2 genetic material.
Another drawback in the study, cited by the researchers, was that the volunteers using the app are a self-selected group who might not be fully representative of the general population.
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