Mapping the Intellectual Structure of COVID-19 Scientific Articles in Iran: A Scientometrics Study

Purpose: The outbreak of COVID-19 in Wuhan, China, and its rapid spread to many countries worldwide has prompted numerous studies on various aspects of the disease, both internationally and within Iran. Research plays a fundamental role in understanding a problem and developing a strategy to address it. Therefore, the current research aims to analyze the scientific metrics and delineate the intellectual structure and concepts of scientific articles authored by Iranian researchers in the field of COVID-19, as indexed in the Web of Science database.Methodology: This research employs text content analysis, utilizing standard techniques from scientometric studies, including co-word analysis and clustering. The statistical population of this research comprises all studies conducted in Iran regarding COVID-19, as indexed in the Web of Science database from 2020 to 2023. A total of 10,963 scientific articles authored by Iranian researchers related to the field of COVID-19 were retrieved from the Web of Science. Then, data preparation analysis, and visualization were conducted using RaverPrimp (v. 1.2), SPSS (v. 23), UCINET, and NetDraw (v. 6) software.Findings: The research findings indicate that a total of 10,963 articles authored by 46,487 researchers across 2,741 journals have contributed to the production of scientific literature on COVID-19 produced by Iranian scholars. A total of 177 countries have collaborated with Iranian researchers in producing documents related to COVID-19, resulting in the publication of numerous articles. A total of 3,435 universities have contributed to the production of scientific articles on COVID-19 by Iranian researchers. Among the 4,985 universities, Tehran University of Medical Sciences ranked first with 1,247 articles, while Shahid Beheshti University of Medical Sciences and Iran University of Medical Sciences ranked second and third, respectively, with 850 and 573 articles. Additionally, the research findings indicated a positive trend in the publication of articles. Based on the distribution of high-frequency keywords, the term "pandemic" ranks first with 352 occurrences, followed by "infection" and 202 and 193 occurrences, respectively. The findings of the centrality measures—Rank, Betweenness, Closeness, and Degree—of authors and keywords in scientific articles related to COVID-19 produced by Iranian researchers in the Web of Science database were reported. The results of the centrality analysis in the reviewed articles indicate that the author "Sahebkar, A.", with a rank centrality of 0.975, holds the first position, followed by "Rezaei, N.", with a rank centrality of 0.869 in the second place. The author "Bagheri, K.M." third with a degree centrality of 0.610. Additionally, the results of the Closeness centrality analysis reveal that "Rezaei, N, with a closeness centrality of 0.509, ranks first, followed closely by "Soltani, S.", with a closeness centrality of 0.505. The author "Tabarsi, P." with a closeness centrality of 0.499. The results of the Betweenness centrality analysis indicate that the author "Rezaei, N." has the highest Betweenness centrality score of 7.85, ranking first. This is followed by the author "Sahebkar, A.", a Betweenness centrality score of 7.251, ranking second. The author "Afshar, Z.M." with a Betweenness centrality score of 6.28.Conclusion: The COVID-19 pandemic requires the serious attention of scientists and researchers involved in scientific communication. Hierarchical clustering results, based on Bradford's law, analyzed 102 keywords within the research area and led to the formation of 13 thematic clusters. These clusters include: COVID-19 and mental health pressures (anxiety, stress, and depression); the mental health and quality of life of nurses and healthcare workers; the role of health monitoring and physical activities in preventing the COVID-19 epidemic (including the impact of electronic services); the effect of mesenchymal stem cells on immune system modulation in COVID-19 (immune regulation and cellular responses); respiratory infectious diseases; diagnostic tests for COVID-19 and antiviral treatments; the application of nanoparticles in the production of the coronavirus vaccine; examination of the molecular structure of the coronavirus through molecular dynamics simulations; coronavirus detection using artificial intelligence; specialized care for the elderly with chronic diseases; kidney disorders in COVID-19 patients; prevention of acute respiratory infectious diseases in vulnerable populations; and the effects of COVID-19 on the central nervous system. The findings indicate that the field of COVID-19 research is extensive, with numerous topics across various domains, including epidemiology, infection, SARS, and immunity, all of which have garnered significant interest from researchers.