Systematic review of bibliometric studies on SARS-CoV-2
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Published:
Sep 24, 2020
Keywords:
bibliometry, COVID-19, coronavirus, research, 2019-nCoV epidemic
Main Article Content
Abstract
Objective: To perform a systematic review of articles that evaluated the scientific production on SARS-CoV-2 through bibliometric analyzes. Methods: Scopus, Web of Science and Google Scholar databases were used. After applying the pre-established inclusion criteria, 30 articles were included. Results. The total number of articles found in the bibliometric studies on SARS-CoV-2 varied widely from 153 to 21,395 articles and an average equal to 4,279 (± 5,510). A total of 17 countries published within the scope of this study, but only six published more than one article, emphasizing authors from Chinese institutions (17%). Scopus was the most used database in bibliometric studies (50%, n = 15). The articles used 72 different keywords with emphasis on: COVID-19 (15%), SARS-CoV-2 (12%) and 2019-nCoV (9%). Conclusion. We are facing an unprecedented scenario of information about SARS-CoV-2 and this has required a collective scientific effort reflected in the daily publication of hundreds of studies (articles, pre-prints, clinical guides, protocols). Bibliometric methods are being increasingly used by the scientific community to systematize this information. Therefore, the systematic review carried out in this study provided an overview of the bibliometric literature on the SARS-CoV-2 virus.
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1.
Silva TF, Melo AA de, Santos DLA dos, Vaz EC, Corvalan LCJ, Ribeiro M de L, Rodrigues FM. Systematic review of bibliometric studies on SARS-CoV-2. Health Sci J [Internet]. 2020Sep.24 [cited 2024Apr.26];10(3):116-25. Available from: https://portalrcs.hcitajuba.org.br/index.php/rcsfmit_zero/article/view/1023
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References
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39. Rathbone J, Carter M, Hoffmann T, Glasziou P. A comparison of the performance of seven key bibliographic databases in identifying all relevant systematic reviews of interventions for hypertension. Syst Rev. 2016;5:27. https://doi.org/10.1186/s13643-016-0197-5 PMID: 26862061 PMCID: PMC4748526
40. Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 PMID:20877712
41. Cavacini A. What is the best database for computer science journal articles? Scientometrics. 2015;102(3):2059–71. https://doi.org/10.1007/s11192-014-1506-1
42. Freeman MK, Lauderdale SA, Kendrach MG, Woolley TW. Google scholar versus PubMed in locating primary literature to answer drug-related questions. Ann Pharmacother. 2009;43(3):478–84. https://doi.org/10.1345/aph.1L223 PMID:19261965
43. Berg JM, Bhalla N, Bourne PE, et al. Preprints for the life sciences. Science. 2016;352(6288):899-901 https://doi.org/10.1126/science.aaf9133
44. Haustein S, Larivière V. The use of bibliometrics for assessing research: Possibilities, limitations and adverse effects. Incent Perform Gov Res Organ. 2015;121–39. https://crc.ebsi.umontreal.ca/files/sites/60/2015/10/HausteinLariviere_revised2.pdf
45. McKiernan EC, Schimanski LA, Muñoz Nieves C, Matthias L, Niles MT, Alperin JP. Meta Research: Use of the Journal Impact Factor in academic review, promotion, and tenure evaluations. Elife. 2019;8:e47338. https://doi.org/10.7554/eLife.47338 PMid:31364991
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2. Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020;382(13):1199–207. https://doi.org/10.1056/NEJMoa2001316
3. Gorbalenya AE, Baker SC, Baric RS, et al. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020;5(4):536–44. https://doi.org/10.1038/s41564-020-0695-z
PMid:32123347
4. Ceraolo C, Giorgi FM. Genomic variance of the 2019-nCoV coronavirus. J Med Virol. 2020;92(5):522–8. https://doi.org/10.1002/jmv.25700 PMid:32027036
5. Wong MC, Cregeen SJJ, Ajami NJ, Petrosino JF. Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019. bioRxiv [preprint]. 2020;2013:2020.02.07.939207. https://doi.org/10.1101/2020.02.07.939207
6. Zhang L, Zhao W, Sun B, Huang Y, Glänzel W. How scientific research reacts to international public health emergencies: a global analysis of response patterns. Scientometrics. 2020;124(1):747–73. https://doi.org/10.1007/s11192-020-03531-4 PMid:32836522
7. Rinaldi B, Rinaldi JP. Available evidence on risk factors associated with COVID-19’s poorer outcomes, worldwide and in Brazil. Rev Cienc Saude. 2020;10(2):80–9. https://doi.org/10.21876/rcshci.v10i2.985
8. Bar-Ilan J. Citations to the “Introduction to informetrics” indexed by WOS, Scopus and Google Scholar. Scientometrics. 2010;82(3):495–506. https://doi.org/10.1007/s11192-010-0185-9
9. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097 PMCID: PMC2707599
10. Chahrour M, Assi S, Bejjani M, et al. A bibliometric analysis of Covid-19 research activity: A call for increased output. Cureus. 2020;12(3): e7357. https://doi.org/10.7759/cureus.7357 PMid: 32328369
11. Hamidah I, Sriyono S, Hudha MN. A Bibliometric analysis of Covid-19 research using VOSviewer. Indon J Sci Technol [Internet]. 2020 [cited 2020 Sep 22];5(2):34-41. https://doi.org/10.17509/ijost.v5i2.24522 Avaiable from: https://ejournal.upi.edu/index.php/ijost/article/view/24522
12. Melo MC, Cabral ERM, Rolim ACA, et al. A bibliometric analysis of global researches at COVID-19: COVID-19 bibliometric analysis. Interam J Med Health. 2020;3:e202003019. https://doi.org/10.31005/iajmh.v3i0.88
13. Kumar K. Author productivity of COVID-19 research output globally: Testing Lotka's Law. SSRN [preprint]. 2020 Apr 6:1-15. http://dx.doi.org/10.2139/ssrn.3603889
14. Dehghanbanadaki H, Seif F, Vahidi Y, et al. Bibliometric analysis of global scientific research on Coronavirus (COVID-19). Med J Islam Repub Iran [Internet]. 2020 [cited 2020 Sep 22];34:51. Avaiable from: http://mjiri.iums.ac.ir/article-1-6629-en.pdf
15. Lou J, Tian SJ, Niu SM, et al. Coronavirus disease 2019: a bibliometric analysis and review. Eur Rev Med Pharmacol Sci. 2020;24(6):3411-21. https://doi.org/10.26355/eurrev_202003_20712
16. Belli S, Mugnaini R, Baltà J, Abadal E. Coronavirus mapping in scientific publications: when science advances rapidly and collectively, is access to this knowledge open to society? Scientometrics. 2020;124:2661–85. https://doi.org/10.1007/s11192-020-03590-7
17. Liu N, Chee ML, Niu C, et al. Coronavirus disease 2019 (COVID-19): an evidence map of medical literature. BMC Med Res Methodol. 2020;20:177. https://doi.org/10.1186/s12874-020-01059-y
18. Haghani M, Bliemer MCJ. Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature. bioRxiv [preprint]. 2020 Jun 1 [cited 2020 Sep 22];2006.00674. Available from: https://arxiv.org/abs/2006.00674
19. Pathak M. COVID-19 research in India: a quantitative analysis. Indian J Biochem Biophys. 2020;57(3):351–5. Available from: http://nopr.niscair.res.in/handle/123456789/54461
20. Kousha K, Thelwall M. COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts. Quant Sci Stud. 2020;1(3):1068-91. https://doi.org/10.1162/qss_a_00066
21. Fiesco-Sepúlveda KY, Serrano-Bermúdez LM. Contributions of Latin American researchers in the understanding of the novel coronavirus outbreak: a literature review. Peer J. 2020;8:e9332. https://doi.org/10.7717/peerj.9332 PMId: 32547890
22. Hossain MM. Current status of global research on novel coronavirus disease (COVID-19): a bibliometric analysis and knowledge mapping [version 1; peer review: 2 approved with reservations]. F1000Research 2020;9:374. https://doi.org/10.12688/f1000research.23690.1
23. Torres-Salinas D. Daily growth rate of scientific production on Covid-19. Analysis in databases and open access repositories. El Profes Inform. 2020;29(2): e290215. https://doi.org/10.3145/epi.2020.mar.15
24. Kirchhoff J, Mertens A, Scheufen M. Der Corona-Innovationswettlauf in der Wissenschaft: Eine Analyse der wissenschaftlichen Publikationen zur Bekämpfung der Corona-Pandemie und die Bedeutung für den Pharma-Standort Deutschland, IW-Report [Internet]. Köln: Institut der deutschen Wirtschaft (IW); 2020(17):30pp. Available from: http://hdl.handle.net/10419/216830
25. Hu YJ, Chen MM, Wang Q, et al. From SARS to COVID-19: A bibliometric study on emerging infectious diseases with natural language processing technologies. Research Square [preprint];2020 May 6 [cited 2020 Sep 22]. https://doi.org/10.21203/rs.3.rs-25354/v1
26. Helliwell JA, Bolton WS, Burke JR, Tiernan JP, Jayne DG, Chapman SJ. Global academic response to COVID-19: Cross-sectional study. medRxiv [preprint]. 2020 May 3 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.27.20081414
27. Latif S, Usman M, Manzoor S, et al. Leveraging data science to combat COVID-19: A comprehensive review. TechRxiv [preprint]. 2020 Apr 30 [cited 2020 Sep 22]. https://doi.org/10.36227/techrxiv.12212516.v1
28. O’Brien N, Barboza-Palomino M, Ventura-León J, Caycho-Rodríguez T, Sandoval-Díaz JS, López-López W, et al. Coronavirus disease (COVID-19). A bibliometric analysis. Rev Chil Anest. 2020;49(3):408–15. https://doi.org/10.25237/revchilanestv49n03.020
29. Torres-Salinas D, Robinson-Garcia N, Castillo-Valdivieso PA. Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 related literature. BioRxiv [preprint]. 2020 Apr 26 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.23.057307
30. Tran BX, Ha GH, Nguyen LH, et al. Studies of novel coronavirus disease 19 (COVID-19) pandemic: A global analysis of literature. Int J Environ Res Public Health. 2020;17(11):4095. https://doi.org/10.3390/ijerph17114095
31. Zhou Y, Chen L. Twenty-year span of global coronavirus research trends: a bibliometric analysis. In Int J Environ Res Public Health. 2020;17(9),3082. https://doi.org/10.3390/ijerph17093082
32. Haghani M, Bliemer MC, Goerlandt F, Li J. The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review. Safety Sci. 2020;129:104806. https://doi.org/10.1016/j.ssci.2020.104806
33. Aguado-Cortés C, Castaño VM. Translational knowledge map of COVID-19. arXiv [preprint]. 2020 Mar 22 [cited 2020 Sep 22]:2003.10434. Avaiable from: https://arxiv.org/abs/2003.10434
34. Gori AD, Boetto E, Fantini MP. Analysis of the scientific literature in the first 30 Days of the novel coronavirus outbreak. medRxiv [preprint]. 2020 Mar 30 [cited 2020 Sep 22]: 2020.03.25.20043315. https://doi.org/10.1101/2020.03.25.20043315
35. Golinelli D, Nuzzolese AG, Boetto E, et al. The impact of early scientific literature in response to COVID-19: a scientometric perspective. medRxiv [preprint]. 2020 Apr 18 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.15.20066183
36. Kambhampati SB, Vaishya R, Vaish A. Unprecedented surge in publications related to COVID-19 in the first three months of pandemic: A bibliometric analytic report. J Clin Orthop Trauma. 2020;11(Suppl3):S304. https://doi.org/10.1016/j.jcot.2020.04.030
37. Bhattacharya S, Singh S. Visible Insights of the Invisible Pandemic: A Scientometric, Altmetric and Topic Trend Analysis. arXiv [preprint]. 2020 Apr 22[cited 2020 Sep 22]:arXiv:2004.10878. Avaiable from: https://arxiv.org/abs/2004.10878
38. Zhang L, Li B, Jia P, et al. [An analysis of global research on SARS-CoV-2]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020;37(2):236-45. Chinese. https://doi.org/10.7507/1001-5515.202002034. PMID: 32329275.
39. Rathbone J, Carter M, Hoffmann T, Glasziou P. A comparison of the performance of seven key bibliographic databases in identifying all relevant systematic reviews of interventions for hypertension. Syst Rev. 2016;5:27. https://doi.org/10.1186/s13643-016-0197-5 PMID: 26862061 PMCID: PMC4748526
40. Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 PMID:20877712
41. Cavacini A. What is the best database for computer science journal articles? Scientometrics. 2015;102(3):2059–71. https://doi.org/10.1007/s11192-014-1506-1
42. Freeman MK, Lauderdale SA, Kendrach MG, Woolley TW. Google scholar versus PubMed in locating primary literature to answer drug-related questions. Ann Pharmacother. 2009;43(3):478–84. https://doi.org/10.1345/aph.1L223 PMID:19261965
43. Berg JM, Bhalla N, Bourne PE, et al. Preprints for the life sciences. Science. 2016;352(6288):899-901 https://doi.org/10.1126/science.aaf9133
44. Haustein S, Larivière V. The use of bibliometrics for assessing research: Possibilities, limitations and adverse effects. Incent Perform Gov Res Organ. 2015;121–39. https://crc.ebsi.umontreal.ca/files/sites/60/2015/10/HausteinLariviere_revised2.pdf
45. McKiernan EC, Schimanski LA, Muñoz Nieves C, Matthias L, Niles MT, Alperin JP. Meta Research: Use of the Journal Impact Factor in academic review, promotion, and tenure evaluations. Elife. 2019;8:e47338. https://doi.org/10.7554/eLife.47338 PMid:31364991
46. Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics. 2015;105(3):1809–31. https://doi.org/10.1007/s11192-015-1645-z PMid:26594073
47. Nabout J, Parreira MR, Teresa FB, Carneiro FM, Da Cunha HF, De Souza Ondei L, et al. Publish (In a group) or perish (alone): The trend from single- to multi-authorship in biological papers. Scientometrics. 2015;102(1):357–64. https://doi.org/10.1007/s11192-014-1385-5
48. Scopus [Internet site]. Elsevier. 2020 [cited 2020 Jul 10]. Available from: https://service.elsevier.com/app/answers/detail/a_id/15534/supporthub/scopus/#tips
49. PubMed [Internet site]. National Center for Biotechnology Information. 2020 [cited 2020 Jul 10]. Available from: https://pubmed.ncbi.nlm.nih.gov/about/
50. Web of Science [Internet site]. Clarivate. 2020 [cited 2020 Jul 10]. Available from: https://clarivate.com/webofsciencegroup/solutions/web-of-science/