Scientific efforts on SARS-CoV-2 research: A global survey analysis
Bibliographic record
Abstract
INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has been a global pandemic. Researchers have made great efforts to investigate SARS-CoV-2. However, there are few studies analyzing the general situation of SARS-CoV-2 research at global level. This study aimed to characterize global scientific efforts based on SARS-CoV-2 publications. METHODOLOGY: SARS-CoV-2 -related publications were retrieved using Web of Science. The number of publications, citation, country, journal, study topic, total confirmed cases, and total deaths were analyzed. RESULTS: A total of 441 publications were identified. China contributed the largest number of publications (198, 44.90%), followed by USA (51, 11.56%), Italy (28, 6.35%), Germany (19, 4.31%), and South Korea (13, 2.95%). Upper-middle-income economies (51.70%) produced the most SARS-CoV-2 publications, followed by high-income (45.12%), lower-middle-income (2.95%), and low-income economies (0.23%). The research output had a significant correlations with total confirmed cases (r = 0.666, p = 0.000) and total deaths (r = 0.610, p = 0.000). China had the highest total citations (1947), followed by USA (204), and Germany (54). China also had the highest average citations (9.83), followed by Netherlands (5.80), and Canada (5.43). The most popular journals were Journal of Medical Virology, Eurosurveillance, and Emerging Microbes and Infections. The most discussed topic was the epidemiology of SARS-CoV-2. CONCLUSIONS: Scientific research on SARS-CoV-2 is from worldwide researchers' efforts, with some countries and journals having special contributions. The countries with more total confirmed cases and total deaths tend to have more research output in the field of SARS-CoV-2. China was the most prolific country, and had the highest quality of publications on SARS-CoV-2.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.037 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".