Bibliometric and Visualization Analysis of Human Coronaviruses: Prospects and Implications for COVID-19 Research

被引:30
作者
Deng, Ziqin [1 ]
Chen, Junsheng [1 ]
Wang, Ting [1 ]
机构
[1] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Basic Med, Dept Pathogen Biol, Wuhan, Peoples R China
基金
中国国家自然科学基金;
关键词
human coronavirus; COVID-19; SARS-CoV-2; bibliometric; visualization; DISEASE; VIRUS; OUTBREAKS; HOSTS;
D O I
10.3389/fcimb.2020.581404
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Human coronaviruses, which can cause a range of infectious diseases, have been studied for nearly 60 years. The field has gained renewed interest from researchers around the world due to the COVID-19 outbreak in late 2019. Despite a large amount of research, little is known about the knowledge structure and developing trends of this topic. Here, we apply bibliometric analysis along with visualization tools to analyze 15,207 publications related to human coronavirus from the Scopus database, using indicators on publication and citation, journal, country or territory, affiliation and international cooperation, author, and keyword co-occurrence cluster. The results show that research on human coronavirus is dominated by SARS-CoV. Although there have been many publications, only 626 publications (4.1% of total) have more than 100 citations. The top 20 journals with most publications account for 20.6% of total publications and 41% of total citations. In addition to the United States and some European countries, many Asian and African countries are involved in this research, with China holding an important position in this area. Leading researchers from various fields of human coronavirus research are listed to facilitate collaboration and promote effective disease prevention and control. The keywords co-occurrence analysis reveals that the research focus on virology, public health, drugs and other hotspot fields, and uncovers changes in the direction of coronavirus research. The research map on human coronavirus obtained by our analysis are expected to help researchers to efficiently and effectively explore COVID-19.
引用
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页数:13
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