The Progress and Research Trends in Coronavirus (COVID-19) Research Publications: Epidemiological and Bibliometrical Approaches
Bibliographic record
Abstract
The main objective of the present study is to summarize the research output about COVID-19. The search was conducted in Scopus, the largest abstract and citation database of peer-reviewed literature, and later it was analyzed on VOSviewer. Total 34716 research documents have been published about COVID-19 till September 2020. We focused on three parameters, i.e., co-authorship pattern, citations, and co-words analysis. Based on the total number of publications, h-index, total citations, and citations per document, we provided the list of the top ten authors, institutes, and countries. Based on the total number of publications, the top-ranked author is Wiwanitkit, V., and the top institute is Harvard Medical School, USA. It is worthy to note that more than 150 countries have contributed to research output. Based on the total publications, citations, and h-index, we provided details for each continent. Later, we provided the list of the top ten countries. The highest documents are published by the USA (25.35%). We analyzed the 343682 keywords from all publications to provide a general overview or the common trends in publications. We also analyzed the top 2000 most cited documents and provided the details of the top ten authors, institutes, and countries. Based on the VOSviewer' analysis, the information on the co-occurrence of words in titles, abstracts, and keywords is provided. This may help to depict the common trends in research publications. Based on the bibliometrics results, significant work has been published on pathogenesis, diagnosis, treatment, and prevention of this pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.176 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".