Top 100 cited articles in one year of COVID-19 research – A bibliometric analysis

Abstract

BACKGROUND: Coronavirus disease (COVID-19)-related scientific publications have increased exponentially during the present pandemic but their influence on biomedical literature is not known. The characteristics of highly cited articles help us to identify important advances and their scientific impact. OBJECTIVES: In the present study, we have identified and analyzed the top 100 most highly-cited articles of COVID-19 research published in the year 2020. METHODS: A cross-sectional bibliometric analysis was using the search terms "severe acute respiratory syndrome-coronavirus-2," "COVID," "nCoV," "Coronavirus," and "HCoV" querying the Google Scholar database using the program "Publish or Perish." The first 100 English language articles with the maximum number of citations were identified and evaluated in detail. RESULTS: The top 100 COVID-19 articles in 2020 had citations ranging from 1147 to 20,440. The median (interquartile range [IQR]) number of citations was 1970 (1456-2939). The number of authors ranged from 1 to 79 (median 10; IQR 5.25-19). The majority of first authors was from China (58%), followed by the United States of America (16%) and the United Kingdom (7%). The top three journals in terms of the number of published articles (37%) were the New England Journal of Medicine, Journal of the American Medical Association, and The Lancet. Most of the top-cited COVID-19 literature were descriptive studies focusing on epidemiology (48%) and clinical course (60%) of COVID-19. CONCLUSION: Clinical course and epidemiology have been the predominant areas of research interest in COVID-19 in 2020. Citation analysis of COVID-19 literature helps us to map out the most important focus for research in this pandemic and to identify gaps in knowledge which would guide further research.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.