Review Article

A 50-Year Overview of the Coronavirus Family with Science Mapping Techniques: A Review


Background: The COVID-19 pandemic from the coronavirus family is the most important agenda of today's world, also called the “New World”. In this outbreak period, declared a pandemic by WHO and affected the whole world and humanity on a global scale, all kinds of scientific information and evidence-based sharing on the subject gained great importance.

Methods: Overall, 12,301 articles from the web of Science (WOS) Core Collection database were analyzed using SciMAT software, conducted to examine the development of coronavirus publications in the process and to reveal the scientific mapping related to the subject. To analyze the development in the process based on periods, the articles covering the 50 years were compared as five periods of 10 years.

Results: The most publications with the Coronavirus theme were made between 2010 and 2020 (n=1020), the total number of citations of these articles was 15,966 and the h-index value was 54. The theme "Coronavirus” was associated with the themes “infection” (w=0.04), “SARS” (w=0.03), “virus” (w=0.04), “identification” (w=0.05) and "swine" (w=0.03). Due to the recent emergence of the COVID-19 theme, it was found to be directly related to the “outbreak” theme (w=0.01). In terms of the distribution of the articles on coronavirus by country, most articles were published by the USA. This country is followed by China, Germany, England and the Netherlands.

Conclusion: This research on the coronavirus family can offer a holistic view of the virus family in the scientific world and can make a scientific contribution to the fight against the virus by creating awareness on this issue.

1. Sharma AK, Som A (2020). Deep phyloge-netic analysis of Orthocoronavirinae ge-nomes traces the origin, evolution and transmission route of 2019 novel coro-navirus. bioRxiv. doi:
2. Xu J, Zhao S, Teng T, et al (2020). Systemat-ic comparison of two animal-to-human transmitted human coronaviruses: SARS-CoV-2 and SARS-CoV. Viruses, 12(2):244.
3. Malik YA (2020). Properties of Coronavirus and SARS-CoV-2. Malays J Pathol, 42(1):3-11.
4. Zhou P, Yang XL, Wang XG, et al (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 270–273.
5. Ashour HM, Elkhatib WF, Rahman M, El-shabrawy HA (2020). Insights into the re-cent 2019 novel coronavirus (SARS-CoV-2) in light of past human coronavirus outbreaks. Pathogens, 9(3):186.
6. Ji JS (2020). Origins of MERS-CoV, and lessons for 2019-nCoV. Lancet Planet Health, 4(3):e93.
7. Shereen MA, Khan S, Kazmi A, Bashirb N, Siddiquea R (2020). COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J Adv Res, 24:91-98.
8. Wilder-Smith A, Freedman DO (2020). Iso-lation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J Travel Med, 27(2):taaa020.
9. Ariaa M, Cuccurullo C (2017). Bibliometrix: an rtool for comprehensive science map-ping analysis. Journal of Informetrics, 11:959-975
10. Chen C (2017). Science mapping: a system-atic review of the literature. Journal of Data and Information Science, 2(2):1-40.
11. Zupic I (2015). Bibliometric methods in management and organization. Organiza-tional Research Methods, 18(3):429-472.
12. Kurutkan MN, Usta E, Orhan F, et al (2015). Application of the IHI Global Trigger Tool in measuring the adverse event rate in a Turkish healthcare setting. Int J Risk Saf Med, 27(1):11-21.
13. Van Raan AF (2014). Advances in biblio-metric analysis: research performance as-sessment and science mapping. 3:17-28.
14. Bilim Dallarında Dünya, Ülkeler ve Gruplara Ait Veriler: Tıbbi Bilimler (2010-2015). TÜBİTAK Ulakbim Cahit Arf Bilgi Merkezi. 2020.
15. Cobo MJ, López-Herrera AG, Herrera-Viedma E, et al (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1):146-166.
16. Cobo MJ, Martínez MÁ, Gutiérrez-Salcedo M, et al (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-Based Systems, 80:3-13.
17. Cobo MJ, López Herrera AG, Herrera Viedma E, et al (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8):1609-1630.
18. Martínez MA, Cobo MJ, Herrera M, et al (2015). Analyzing the scientific evolution of social work using science mapping. Re-search on Social Work Practice, 25(2):257-277.
19. Murgado-Armenteros EM, Gutiérrez-Salcedo M, Torres-Ruiz FJ, et al (2015). Analysing the conceptual evolution of qualitative marketing research through science mapping analysis. Scientometrics, 102(1):519-557.
20. Summary of probable SARS cases with on-set of illness from 1 November 2002 to 31 July 2003. Erişim Tarihi: [10.06.2020].
21. Middle East respiratory syndrome corona-virus (MERS-CoV)
22. Hogue BG, King B, Brian DA (1984). Anti-genic relationships among proteins of bovine coronavirus, human respiratory coronavirus OC43, and mouse hepatitis coronavirus A59. J Virol, 51(2):384-8.
23. Keck JG, Hogue BG, Brian DA, et al (1988). Temporal regulation of bovine corona-virus RNA synthesis. Virus Res,9(4):343-56.
24. Sánchez CM, Jiménez G, Laviada MD, et al (1990). Antigenic homology among coronaviruses related to transmissible gastroenteritis virus. Virology, 174(2):410-7.
25. Cook JKA, Mockett APA (2013). The Epide-miology of Infectious Bronchitisi Virus. Eds: Sid-del SG. The Coronaviridae. Springer Science & Business Media. London.
26. Kahn JS (2006). Epidemiology of Human Metapneumovirus. Clin Microbiol Rev, 19(3):546-57.
27. Raj VS, Osterhaus AD, Fouchier RA, et al (2014). MERS: Emergence of a Novel Human Coronavirus. Curr Opin Virol, 5:58-62.
28. Chu DK, Poon LL, Gomaa MM, et al (2014). MERS coronaviruses in drome-dary camels, Egypt. Emerg Infect Dis, 20(6):1049-53.
29. Reusken CB, Haagmans BL, Muller MA, et al (2013). Middle East Respiratory Syn-drome Coronavirus Neutralising Serum Antibodies in Dromedary Camels: a Comparative Serological Study. Lancet In-fect Dis, 13:P859-866.
30. Cavanagh D, Davis PJ, Mockett AA (1988). Amino acids within hypervariable region 1 of avian coronavirus IBV (Massachu-setts serotype) Spike Glycoprotein are Associated with Neutralization Epitopes. Virus Res, 11(2):141-50.
31. He Y, Zhou Y, Liu S, Kou Z, Li W, Farzan M, Jiang S(2004). Receptor-binding do-main of SARS-CoV Spike Protein Induc-es Highly Potent Neutralizing Antibodies: İmplication for Developing Subunit Vac-cine. Biochem Biophys Res Com-mun,324(2):773-81.
32. Peiris JM, Tang WH, Chan KH, et al (2003). Children with Respiratory Disease Asso-ciated with Metapneumovirus in Hong Kong. Emerg Infect Dis, 9(6):628-633.
33. Qiu H, Wu J, Hong L, et al (2020). Clinical and epidemiological Features of 36 Chil-dren with Coronavirus Disease 2019 (COVID-19) in Zhejiang, China: An Ob-servational Cohort Study. Lancet Infect Dis, 20(6):689-96.
IssueVol 50 No 4 (2021) QRcode
SectionReview Article(s)
Coronavirus Pandemic COVID-19 Science mapping Scimat

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How to Cite
Tabur A, Arslanoğlu A. A 50-Year Overview of the Coronavirus Family with Science Mapping Techniques: A Review. Iran J Public Health. 50(4):649-664.