When a pandemic outbreak occurs, it seems logical that related scientific production should increase substantially; however, it is important to recognize its interdisciplinary usefulness to find a solution to the problem. The main aim of this research is to analyse the main keywords of the scientific research about COVID-19, by subject area. To discover the influence of certain terms and their transferability, synergies, and future trends, a cluster analysis of the keywords was performed. The results show that Health Sciences dominate the publications with 88.23% of the total volume. As expected, the largest volume of research was dedicated to medical aspects of the disease, like experimental treatments, its physiopathology, or its respiratory syndrome. However, other fields, like Social Sciences (6.07%), Technology (2.68%), Physical Sciences (1.95%), and Arts and Humanities (1.08%), also played an important role in research on COVID-19.
ArchambaultÉ.CampbellD.GingrasY.LarivièreV. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326. https://doi.org/10.1002/asi.21062
2.
AriaM.CuccurulloC. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975, https://doi.org/10.1016/j.joi.2017.08.007
3.
BansardJ. Y.Rebholz-SchuhmannD.CameronG.ClarkD.Van MulligenE.BeltrameF.del HoyoE.Martín-SanchezF.MilanesiL.TollisI.Van Der LeiJ.CoatrieusJ. L. (2007). Medical informatics and bioinformatics: A bibliometric study. IEEE Transactions on Information Technology in Biomedicine, 11(3), 237–243. https://doi.org/10.1109/TITB.2007.894795
4.
BhagavathulaA. S.AldhaleeiW. A.RahmaniJ.MahabadiM. A.BandariD. K. (2020). Knowledge and perceptions of COVID-19 among health care workers: Cross-sectional study. JMIR Public Health and Surveillance, 6(2), e19160. https://doi.org/10.2196/19160
5.
BrymanA. (2016). Social research methods. Oxford University Press.
6.
DawoodF. S.IulianoA. D.ReedC.MeltzerM. I.ShayD. K.ChengP. Y.FeikinD. R. (2012). Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: A modelling study. The Lancet Infectious Diseases, 12(9), 687–695, https://doi.org/10.1016/S1473-3099(12)70121-4
7.
DE FeliceF.PolimeniA. (2020). Coronavirus disease (COVID-19): A machine learning bibliometric analysis. In Vivo, 34(3 Suppl.), 1613–1617, https://doi.org/10.21873/invivo.11951
8.
EpsteinI. (2005). Following in the footnotes of giants: Citation analysis and its discontents. Social Work in Health Care, 41(3–4), 93–101. https://doi.org/10.1300/J010v41n03_04
GorraizJ.SchloeglC. (2008). A bibliometric analysis of pharmacology and pharmacy journals: Scopus versus Web of Science. Journal of Information Science, 34(5), 715–725. https://doi.org/10.1177/0165551507086991
HouJ.YangX.ChenC. (2018). Emerging trends and new developments in information science: A document co-citation analysis (2009–2016). Scientometrics, 115(2), 869–892. https://doi.org/10.1007/s11192-018-2695-9
15.
KhairatS.MengC.XuY.EdsonB.GianforcaroR. (2020). Interpreting COVID-19 and virtual care trends: Cohort study. JMIR Public Health and Surveillance, 6(2), e18811. https://doi.org/10.2196/18811
16.
LiF.LiM.GuanP.MaS.CuiL. (2015). Mapping publication trends and identifying hot spots of research on Internet health information seeking behavior: A quantitative and co-word biclustering analysis. Journal of Medical Internet Research, 17(3), e81, https://doi.org/10.2196/jmir.3326
17.
LiQ.GuanX.WuP.WangX.ZhouL.TongY.RenR.LeungK.LauE.WongJ. Y.XingX.XiangN.WuY.LiC.ChenQ.LiD.LiuT.ZhaoJ.LiuM.TuW.. . . FengZ. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine382(13), 1199–1207, https://doi.org/10.1056/nejmoa2001316
18.
MacRobertsM. H.MacRobertsB. R. (2018). The mismeasure of science: Citation analysis. Journal of the Association for Information Science and Technology, 69(3), 474–482. https://doi.org/10.1002/asi.23970
NoackA. (2007). Energy models for graph clustering. Journal of Graph Algorithms and Applications, 11(2), 453–480. https://doi.org/10.7155/jgaa.00154
21.
OksanenA.KaakinenM.LatikkaR.SavolainenI.SavelaN.KoivulaA. (2020). Regulation and trust: 3-Month follow-up study on COVID-19 mortality in 25 European countries. JMIR Public Health and Surveillance, 6(2), e19218, https://doi.org/10.2196/19218
22.
Osei AsibeyB.AgyemangS.Boakye DankwahA. (2017). The internet use for health information seeking among Ghanaian university students: A cross-sectional study. International Journal of Telemedicine and Applications, 2017, Article 1756473. https://doi.org/10.1155/2017/1756473
23.
RiouJ.AlthausC. L. (2020). Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance, 25(4), 2000058. https://doi.org/10.2807/1560-7917.es.2020.25.4.2000058
24.
WaltmanL.Van EckN. J.NoyonsE. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002
