Bibliometrics analysis of scientific outputs of Covid-19 disease in Scopus database

Aim: The aim of the present study was to analyze the descriptive and content structure of scientific documents produced by Covid-19 in the Scopus database using scientometrics method. Materials and Methods: The present study was conducted using a scientometrics method. The population of this study consists of 1353 documents in the field of Covid-19 in Scopus Database. The collected data were analyzed using Excel software and the subject maps of this area were mapped using RavarPremap, UCINET, NetDraw and SPSS software. Results: The findings of the study show that a total of 46901 documents are indexed by Covid-19 on the Scopus database. Extracted documents were 8 formats. The results showed the most productive authors of Covid-19 disease show that the three researchers Wang Y, Xia J and Li X had the most scientific output respectively. The results showed authors with high social network centrality. Conclusions: Also clustering analysis of the concepts and words of this new viral disease shows that research by world researchers have included 8 study clusters. These 10 study clusters include: Diagnostic Imaging and Isolation; Symptoms of Coronavirus; Virus Genome and Phylogeny; Pathogenicity; Public health and Outbreak Novel and Coronavirus; Epidemic Coronavirus; Coronavirus Infection and Covid-19; Virus Pneumonia and SARS-cov-2.