Symptom tracker app reveals six distinct types of COVID-19 infection

Modesto Morganelli
Luglio 19, 2020

Scientists say they have identified six different "types" of Covid-19, each based on a "particular cluster of symptoms".

A King's College London team found that the six types also correlated with levels of severity of infection, and with the likelihood of a patient needing help with breathing - such as oxygen or ventilator treatment - if they are hospitalised.

"If you can predict these vulnerable people earlier", Steves said, "you have time to give them support and early interventions to reduce hospitalizations".

People have been reported to experience headaches, confusion, fatigue, muscle pain, loss of appetite, shortness of breath and diarrhoea, among other symptoms.

The least severe categories of the virus were characterised by flu-like symptoms, either with or without fever. Headache and loss of smell are common to all six groupings, but the range of symptoms varies widely after that.

The third cluster showed gastrointestinal symptoms, with a combination of headache, loss of smell, loss of appetite, diarrhoea, sore throat and chest pain.

The fifth cluster (more severe compared to fourth) expressed the same symptoms, with the addition of loss of appetite, sore throat, confusion, and muscle pain.

There are then three "severe" clusters - the first causes fatigue, the second confusion and the final one causes abdominal and respiratory issues.

Patients with level 4,5 and 6 types were more likely to be admitted to hospital and more likely to need respiratory support, the researchers said.

Cluster 1 and 2 represent milder forms of COVID-19, according to the study.

However, 8.6% of cluster four needed breathing support, 9.9% for cluster five and 19.8% for cluster six. Almost half of the patients in cluster 6, according to the study, ended up in hospital, compared with just 16 per cent of those in cluster 1.

With their findings, the scientists developed a model that uses age, sex, body mass index and pre-existing health conditions to predict who is at risk of hospitalization, the news outlet reported.

The team also found that those who belonged to severe level 1, 2 and 3 cluster types were more likely to be older and frailer, be overweight, and have underlying conditions such as diabetes or lung disease.

Sebastien Ourselin, professor of healthcare engineering at King's College London and senior author on the study, said: "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".

"Data is our most powerful tool in the fight against COVID-19".

Professor Tim Spector added that the findings showed the importance of people getting into the habit of using the app daily, "helping us to stay ahead of any local hotspots or a second wave of infections".

The pre-print, non-peer reviewed paper is available online: Carole H Sudre et al.

Patients likewise experienced a wide array of other symptoms consisting of muscle discomfort, chills, tiredness and headache. Note: material may have been edited for length and content.

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