Changes in symptoms and characteristics of COVID-19 patients across different variants: two years study using neural network analysis

Abstract

Abstract Background Considering the fact that COVID-19 has undergone various changes over time, its symptoms have also varied. The aim of this study is to describe and compare the changes in personal characteristics, symptoms, and underlying conditions of individuals infected with different strains of COVID-19. Methods This descriptive-analytical study was conducted on 46,747 patients who underwent PCR testing during a two-year period from February 22, 2020 to February 23, 2022, in South Khorasan province, Iran. Patient characteristics and symptoms were extracted based on self-report and the information system. The data were analyzed using logistic regression and artificial neural network approaches. The R software was used for analysis and a significance level of 0.05 was considered for the tests. Results Among the 46,747 cases analyzed, 23,239 (49.7%) were male, and the mean age was 51.48 ± 21.41 years. There was a significant difference in symptoms among different variants of the disease (p < 0.001). The factors with a significant positive association were myalgia (OR: 2.04; 95% CI, 1.76 – 2.36), cough (OR: 1.93; 95% CI, 1.68—2.22), and taste or smell disorder (OR: 2.62; 95% CI, 2.1 – 3.28). Additionally, aging was found to increase the likelihood of testing positive across the six periods. Conclusion We found that older age, myalgia, cough and taste/smell disorder are better factors compared to dyspnea or high body temperature, for identifying a COVID-19 patient. As the disease evolved, chills and diarrhea, demonstrated prognostic strength as in Omicron.

Keywords

• SARC-COV-2
• COVID-19
• Strains
• Symptoms
• Artificial Neural Network