Scientists Use Machine Learning Algorithm to Identify Six Types of COVID-19 with Distinctive Symptoms

An analysis of data from the COVID Symptom Study app has revealed that there are six distinct ‘types’ of COVID-19, each distinguished by a particular cluster of symptoms and differing in the severity of the disease as well as need for respiratory support during hospitalization.

The findings have major implications for clinical management of COVID-19, and could help doctors predict who is most at risk and likely to need hospital care in a second wave of coronavirus infections. Although continuous cough, fever and loss of smell (anosmia) are usually highlighted as the three key symptoms of COVID-19, data gathered from app users shows that people can experience a wide range of different symptoms including headaches, muscle pains, fatigue, diarrhea, confusion, loss of appetite, shortness of breath and more. The progression and outcomes also vary significantly between people, ranging from mild flu-like symptoms or a simple rash to severe or fatal disease.


To find out whether particular symptoms tend to appear together and how this related to the progression of the disease, the research team at King’s College London (London, UK) used a machine learning algorithm to analyze data from a subset of around 1,600 users in the UK and US with confirmed COVID-19 who had regularly logged their symptoms using the app in March and April. The analysis revealed six specific groupings of symptoms emerging at characteristic timepoints in the progression of the illness, representing six distinct ‘types’ of COVID-19. The algorithm was then tested by running it on a second independent dataset of 1,000 users in the UK, US and Sweden, who had logged their symptoms during May. All people reporting symptoms experienced headache and loss of smell, with varying combinations of additional symptoms at various times. Some of these, such as confusion, abdominal pain and shortness of breath, are not widely known as COVID-19 symptoms, yet are hallmarks of the most severe forms of the disease.

The team also discovered that people experiencing particular symptom clusters were more likely to require breathing support in the form of ventilation or additional oxygen. The researchers then developed a model combining information about age, sex, BMI and pre-existing conditions together with symptoms gathered over just five days from the onset of the illness. This was able to predict which cluster a patient falls into and their risk of requiring hospitalization and breathing support with a higher likelihood of being correct than an existing risk model based purely on age, sex, BMI and pre-existing conditions alone. Given that most people who require breathing support come to hospital around 13 days after their first symptoms, this extra eight days represents a significant ‘early warning’ as to who is most likely to need more intensive care.

“These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19,” said Dr Claire Steves from King’s College London. “If you can predict who these people are at day five, you have time to give them support and early interventions such as monitoring blood oxygen and sugar levels, and ensuring they are properly hydrated - simple care that could be given at home, preventing hospitalizations and saving lives.”

“Being able to gather big datasets through the app and apply machine learning to them is having a profound impact on our understanding of the extent and impact of COVID-19, and human health more widely,” said Sebastien Ourselin, professor of healthcare engineering at King’s College London and senior author of the study.

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