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Bibliometric Analysis of Global Scientific Research on SARSCoV-2 (COVID-19)

Rafiei Nasab, F.; rahim, F.

2020-03-23 health informatics
10.1101/2020.03.19.20038752 medRxiv
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Background and AimSince late 2019, an unknown-origin pneumonia outbreak detected in Wuhan city, Hubei Province, China. We aimed to build a model to qualitatively and quantitatively assess publications of research of COVID-19 from 2019 to 2020. Materials and MethodsData were obtained from the Web of Science (WOS), PubMed, and Scopus Core Collection on March 02, 2020, and updated on March 10. We conducted a qualitative and quantitative analysis of publication outputs, journals, authors, institutions, countries, cited references, keywords, and terms according to bibliometric methods using VOS viewer c software packages. ResultsInitially, we identified 227 papers, of which after an exclusion process, 92 studies were selected for statistical analyses. China accounted for the highest proportion of published research (44 papers, 40.48%), followed by the United States (21 papers, 19.32%), and Canada (7 papers, 6.44%). Adjusted by gross domestic product (GDP), ranked first, with 0.003 articles per billion GDP. In total, the top 10 journals published 47 articles, which accounted for 51.08% of all publications in this Feld. A total of 6 studies (05.52%) were supported by National Natural Science Foundation of China. Chinese Academy of Sciences ranked second 2, 2.76%). ConclusionBibliometric and visualized mapping may quantitatively monitor research performance in science and present predictions. The subject of this study was the fast growing publication on COVID-19. Most studies are published in journals with very high impact factors (IFs) and other journals are more interested in this type of research. HighlightsO_LIBibliometric description and mapping provided a birds-eye view of information on Covid-19 related research C_LIO_LIReaders to comprehend the history of published Covid-19 articles in just a few minutes. C_LIO_LIWe evaluated the research strength of countries and institutions, C_LIO_LIScholars might refer to in order to find cooperative institutions. C_LIO_LIDuring our research using the selected database, we tried to guarantee comprehension and objectivity. C_LI

Published in Cell Journal (Yakhteh) · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.