These differences are most pronounced among the younger age group (16-59 years) compared to the elderly (60-> 80 years), with men compared to women in the early stages of COVID-19 infection. Shows different symptoms.
NS paperPublished today at The Lancet Digital Health, led by researchers of King’s analytical data from the ZOE COVID Symptom Study app on April 20NS Up to 15NS October 2020. Thanks to a joint initiative with the Ministry of Health, app contributors can be tested as soon as new symptoms are reported. Researchers have modeled the early signs of COVID-19 infection and have succeeded in detecting 80% of cases using 3-day self-reported symptoms.
Researchers have compared the ability of the current United Kingdom National Health Service UK diagnostic criteria to predict early signs of COVID-19 infection using a hierarchical Gaussian process model, a type of machine learning.
This machine learning model was able to incorporate several characteristics of the affected person, such as age, gender, and health status, and showed that the symptoms of early COVID-19 infection differed between different groups. ..
Eighteen symptoms were tested and had different associations with early detection in different groups. The most important symptoms for early detection of COVID-19 as a whole included loss of smell, chest pain, persistent cough, abdominal pain, blisters on the legs, eye pain, and abnormal myalgia. However, loss of smell became less important in people over the age of 60 and was irrelevant for subjects over the age of 80. Other early symptoms such as diarrhea were important in the elderly group (60-79 and> 80). Fever is a known symptom of the illness, but it was not an early feature of the illness at any age group.
Men were more likely to report shortness of breath, malaise, chills, and fever, and women were more likely to report loss of smell, chest pain, and persistent cough.
These models were generated in the COVIDSymptom Study app, but suggest that the models will be replicated over time and will apply to non-app contributors as well. Although the model was used with the first strain of the virus and the alpha mutant, important findings suggest that the symptoms of the delta and subsequent mutants also differ between population groups.
Dr. Lianedos Santos Canas, the first author of biomedical engineering and imaging science, said: AI can be used to better detect COVID-19-positive cases only after a few days of illness, using more symptoms. We hope to use such methods to encourage more people to take the test as soon as possible to minimize the risk of spread. “
Dr. Mark Modat, a senior lecturer in the Department of Biomedical Engineering and Imaging Science, said: “As part of our study, we were able to confirm that the profile of symptoms with COVID-19 varies from group to group. This is a criterion for encouraging people to take the test for individuals such as age. It suggests that the information should be used to personalize, or it is possible to consider a larger set of symptoms, which takes into account the different symptoms of the disease across different groups. “
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