COVID-19 research output in 2020: The Global Perspective using Scientometric Study
Bibliographic record
Abstract
This study presents the global level perspective of COVID-19 research output from January to April 2020, and these analyses include global publication share, patterns of research communication channels, the most productive Sources, Authors and Institutions. Data were extracted from the Web of Science citation database using the search string of “Coronavirus” OR “COVID-19” and limited to 2020, a total of 1658 publications were retrieved, which have received 4804 citations and the overall H-index is 29. On the basis of literature analysis around the world, it is found that the 1658 publications came from 78 Countries. As expected China is the most productive country with 523 papers (31.5%) and received 3521 Citations followed the USA with 315 and recorded 912 Citations, the UK with 142 and recorded 351 Citations, Italy with 116 and recorded 154 Citations, Germany 66 and recorded 188 Citations and Canada 58 and recorded 139 Citations, India ranked 10th position among the countries in the year of 2020. There were 2027 institutes involved in the research in COVID-19. Huazhong University, Science & Technology Wuhan-China, recorded highest publications of 46 (583 Citations) and 12 Institutes from China with top 12 randed and covered one-third of Publications out of 1658. BMJ-BRITISH MEDICAL JOURNAL in the first journal with the highest number of publications with 181 and Impact Factor value is 27.60 followed by LANCET 86 and Impact Factor value is 59.10. The most top impact factor journal is NEW ENGLAND JOURNAL OF MEDICINE 70.67.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.000 |
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 teacher head, 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".