Symptom cluster patterns based on text clustering method of COVID-19 and its population characteristics in Sichuan province, China

Abstract

Background: Evidence of the aggregation of COVID-19 symptoms was still limited. Exploration of likely cluster patterns of symptoms may be helpful.Methods: This study enrolled a total of 1067 COVID-19 cases. Symptom cluster patterns were explored by text clustering method. A multinomial logistic regression was applied to revealed its population characteristics. And time intervals between symptoms onset and the first visit was analyzed to take into account the symptoms progression over time. Results: Based on text clustering, the symptoms were summarized into 4 groups. Except for the group without obvious symptoms, the dominate individual symptoms under the other three groups were fever (68.7%), expectoration (59.4%) and fatigue (42.7%), respectively. And the most frequent symptom combinations were fever only (47.8%), expectoration only(19.8%) and fatigue accompanied with fever (4.2%), respectively. People aged 45-64 years were more likely to have symptom group 4 than those aged 65 years or older(OR = 2.66, P = 0.015) and had longer time intervals. Conclusions: The symptoms cluster patterns and common symptom combinations under each pattern may provide information for identification of the infected. Middle-aged population was a group expecting more attention, from the perspective of medical delays.