App-based symptom tracking may predict potential COVID-19
Researchers at King's College London and Massachusetts General Hospital have developed an artificial intelligence (AI) diagnostic that can predict whether someone is likely to have the novel coronavirus (COVID-19) based on their symptoms, according to new findings published in Nature Medicine.
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 United Kingdom and the United States are due to start soon.
More than 3.3 million people globally have downloaded the 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.
In this study, the researchers analyzed data gathered from just under 2.5 million people in the U.K. and U.S. who had been regularly logging their health status in the app, around a third of whom had logged symptoms associated with COVID-19. Of these, 18,374 reported having had a test for coronavirus, with 7,178 people testing positive.
The research team investigated which symptoms known to be associated with COVID-19 were most likely to be associated with a positive test. They found a wide range of symptoms compared to cold and flu, and warn against focusing only on fever and cough. Additionally, two thirds of users testing positive for coronavirus infection reported loss of taste and smell compared with just over a fifth 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, the researchers said.
The researchers then created a mathematical model that 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 symptom, including 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, 17.4 percent, were likely to have COVID-19 at that time.
Researchers suggest that combining this AI prediction with 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.