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Updated: Dec 19, 2025
05:02
Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
33.2K
Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.
Francesca DE Felice1, Antonella Polimeni2
1Department of Radiotherapy, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy fradefelice@hotmail.it.
In Vivo (Athens, Greece)
|June 7, 2020
Summary
This bibliometric analysis reveals exponential growth in COVID-19 research, with China leading publications. Key findings highlight influential papers and topics for understanding and managing coronavirus disease.
Area of Science:
- Bibliometrics
- Infectious Diseases
- Public Health
Background:
- Research on coronavirus disease (COVID-19) has rapidly expanded.
- Understanding research trends is crucial for effective disease management.
Purpose of the Study:
- To evaluate research trends in COVID-19 using bibliometric analysis.
- Identify key publications, authors, and institutions driving COVID-19 research.
Main Methods:
- Bibliometric analysis utilizing machine learning methodology.
- Data extracted from Scopus database including publications, countries, institutions, journals, keywords, funding, and citations.
- Analysis of 1883 eligible papers.
Main Results:
- Significant exponential increase in COVID-19 publications.
- China, USA, UK, and Italy are leading contributing countries.
- High collaboration noted among top authors and institutions.
- "BMJ" published most papers; "The Lancet" received most citations.
- COVID-19 clinical features emerged as the most frequent research topic.
Conclusions:
- This bibliometric analysis identifies influential COVID-19 research.
- Findings can enhance understanding and management strategies for COVID-19.
- Highlights the dynamic nature of COVID-19 research landscape.
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