Update on the COVID-19 Vaccine Research Trends: A Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is ravaging the world. To date, there are no standard therapies available to cure the disease. Consequently, research on COVID-19 vaccines is booming. This report aimed to assess the research trends of the global COVID-19 vaccines. METHODS: (http://www.histcite.com/) software was used to calculate the total local citation score (TLCS) and total global citation score (TGCS) of each variable and generate the citation historiography graph of COVID-19 vaccine development using the citation time series analysis method. RESULTS: A total of 5070 studies authored by 21,151 researchers and published by 1364 different journals were eventually included in this study. The bulk of the retrieved studies were original articles (n = 2401, 47.36%). Among these studies, 1204 (23.75%) were published in 2020. A total of 3863 (76.19%) were published in 2021 and 4295 (84.71%) were open access. The highest number of studies was conducted in the USA, followed by England, China, and Germany. The main partners of the USA were China, England, and Canada. The University of Maryland (TLCS: 1618, TGCS: 2991) and Prof. Ugur Sahin from the University Medical Center of the Johannes Gutenberg University (TLCS: 1397, TGCS: 2407) were the most cited institution and author, respectively. The vaccines featured the highest number of papers, with 294 publications (TLCS: 0, TGCS: 1226). The most cited journal was the New England Journal of Medicine (TLCS: 3310, TGCS: 5914), with an impact factor (IF) of 91.245. The related topics included the following six aspects: attitudes towards vaccination, immunoinformatics analysis, clinical research, effectiveness and side effects, and the public management of vaccines. The timing diagram revealed that the research hotspots focused on the side effects of vaccines and public attitude towards vaccination. CONCLUSION: This novel comprehensive bibliometric analysis can help researchers and non-researchers to rapidly identify the potential partners, landmark studies, and research topics within their domains of interest. Through this study, we hope to provide more data to combat the COVID-19 pandemic.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.234 | 0.234 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".