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

Autor: Seyed Hossein Torabi, Seyed Mohammad Riahi, Azadeh Ebrahimzadeh, Fatemeh Salmani
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: BMC Infectious Diseases, Vol 23, Iss 1, Pp 1-10 (2023)
Druh dokumentu: article
ISSN: 1471-2334
DOI: 10.1186/s12879-023-08813-9
Popis: 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
Databáze: Directory of Open Access Journals
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