Researchers Identify 6 Distinct ‘Types’ of COVID-19

A team of researchers from King’s College London revealed that there are six (6) distinct types of COVID-19 based on the analysis of data from the COVID-19 Symptom Study app which are distinguished by a particular cluster of symptoms. The research is called “Symptom Clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app.”

6 COVID-19 Clusters

These are the following clusters:

1. ‘Flu-like’ with no fever

• Headache,
• loss of smell,
• muscle pains,
• cough,
• sore throat,
• chest pain,
• no fever.

2. ‘Flu-like’ with fever

• Headache,
• loss of smell,
• cough,
• sore throat,
• hoarseness of voice,
• fever,
• loss of appetite.

3. Gastrointestinal

• Headache,
• loss of smell,
• loss of appetite,
• diarrhea,
• sore throat,
• chest pain,
• no cough.

4. Severe level one, fatigue

• Headache,
• loss of smell,
• cough, fever,
• hoarseness,
• chest pain,
• fatigue.

5. Severe level two, confusion

• Headache,
• loss of smell,
• loss of appetite,
• cough,
• fever,
• hoarseness,
• sore throat,
• chest pain,
• fatigue,
• confusion,
• muscle pain.

6. Severe level three, abdominal and respiratory

• Headache,
• loss of smell,
• loss of appetite,
• cough,
• fever,
• hoarseness,
• sore throat,
• chest pain,
• fatigue,
• confusion,
• muscle pain,
• shortness of breath,
• diarrhea,
• abdominal pain.

Also, the researchers conducted an investigation on whether individuals experiencing symptom clusters were more likely to require breathing support in the form of ventilation or additional oxygen.

Data Gathered from the COVID-19 Symptom Study App

The three (3) key COVID-19 symptoms that are usually highlighted are continuous cough, fever, and loss of smell. However, data gathered from the COVID-19 Symptom Study App users shows that people can experience a wide range of different symptoms such as the following:

• Headaches
• Muscle pains
• Fatigue
• Diarrhea
• Confusion
• Loss of appetite
• Shortness of breath

Furthermore, the progression and outcomes also differ significantly between people, ranging from mild flu-like symptoms or a simple rash to severe or fatal disease.

Machine Learning Algorithm

The researchers used a Machine Learning Algorithm to find out whether particular symptoms tend to appear together and how this related to the progression of COVID-19.

With the use of the Machine Learning Algorithm, they were able to analyze date from a subset of around 1,600 users with confirmed COVID-19 who had regularly logged their symptoms using the app in March and April in the United Kingdom (UK) and United States (US).

The algorithm was then tested again by the researchers buy running in on a second independent dataset of 1000 users who logged their symptoms during May from the UK, US, and Sweden.

Implication of the Findings

According to the researchers, these findings have a major implication for the clinical management of COVID-19. Furthermore, the findings could help doctors predict the following:

• Who is most at risk; and
• who is most likely to need hospital care in case of a second wave of COVID-19 infections.

Dr. Claire Steves from King’s College London said that these findings have important implications for the care and monitoring of people who are most vulnerable to severe COVID-19.

In a statement of Dr. Steves she said that,

 “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 hospitalisations and saving lives.”

While lead researcher Dr. Carole Sudre of King’s College London said in her statement how their study illustrates the importance of monitoring symptoms of COVID-19 to understand more about the disease.

”Our study illustrates the importance of monitoring symptoms over time to make our predictions about individual risk and outcomes more sophisticated and accurate. This approach is helping us to understand the unfolding story of this disease in each patient so they can get the best care.” – Dr. Carole Sudre

Source: King’s College London