Hostname: page-component-7c8c6479df-ws8qp Total loading time: 0 Render date: 2024-03-29T15:06:05.530Z Has data issue: false hasContentIssue false

Bibliometric Analysis of COVID-19 and the Association With the Number of Total Cases

Published online by Cambridge University Press:  08 June 2021

Ozge Pasin*
Affiliation:
Department of Biostatistics, Faculty of Medicine, Bezmialem University, Istanbul, Turkey
Tugce Pasin
Affiliation:
Department of Physical Medicine and Rehabilitation, Istanbul Goztepe Training and Research Hospital, Istanbul, Turkey
*
Corresponding author: Ozge Pasin, Email: opasin@bezmialem.edu.tr

Abstract

Objectives:

Coronavirus disease 2019 (COVID-19) has spread quickly all over the world. The number of studies in this field being performed and published is increasing day by day. The aim of this study is to analyze the publications in the field of COVID-19 with the help of bibliometric methods. After bibliometric analysis, the second aim is to investigate the relationship between the number of publications in countries and the number of total cases.

Methods:

The data in the study were taken from the Web of Science (WOS) site. Analyses and mapping processes were performed using VOSviewer and SPSS package program. The words “COVID-19”, “Novel Coronavirus”, “2019-nCoV”, “SARS-CoV-2” were used as key words for analysis. The data include publications from 2019 to 2021 (January 10).

Results:

As a result of the study, a total of 38,080 publications were evaluated. It was determined that the countries with the highest number of publications on COVID-19 were China and the United States, and the country with the highest number of citations was China. Most of the studies in the field of COVID-19 have been conducted on General Internal Medicine and Public Enviromental Occupational Health. In addition, statistically significant relationships were observed between the number of publications and the number of total cases in terms of countries (r = 0.806; P < 0.001).

Conclusions:

As a result, bibliometric analysis about COVID-19 can be useful for the future studies. It gives a general perspective of the studies.

Type
Original Research
Copyright
© Society for Disaster Medicine and Public Health, Inc. 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

WHO. Novel coronavirus—China. 2020. https://www.who.int/csr/don/12-january-2020-novelcoronavirus-china/en/. Accessed July 27, 2020).Google Scholar
Sohrabi, C, Alsafi, O’Neill N, et al. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. 2020;76:71-76.CrossRefGoogle Scholar
Al, U, Tonta, Y. Citation analysis: sources cited in dissertations completed at Hacettepe University Department of Librarianship. Information World. 2004;5:19-47.Google Scholar
Schaer, P. Applied informetrics for digital libraries: an overview of foundations, problems and current approaches. Hist Soc Res. 2013;38:267-281.Google Scholar
Alan, P. Statistical bibliography or bibliometrics? J Doc. 1969;25:348-349.Google Scholar
Ivan, Z, Cater, T. Bibliometric methods in management and organization. Organ Res Methods. 2015;18:429-472.Google Scholar
Worldometer. COVID-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/?. Accessed January 10, 2021.Google Scholar
De Felice, F, Polimeni, A. Coronavirus disease (COVID-19): a machine learning bibliometric analysis. In Vivo. 2020;34(3 Suppl):1613-1617.CrossRefGoogle ScholarPubMed
Farooq, RK, Rehman, SU, Ashiq, M et al. Bibliometric analysis of coronavirus disease (COVID-19) literature published in Web of Science 2019-2020. J Family Community Med. 2021;28:1-7.Google Scholar