Mapping the global research output on Ebola vaccine from research indexed in web of science and scopus: a comprehensive bibliometric analysis
Bibliographic record
Abstract
Introduction: The Ebola Virus outbreak in Africa is believed to be one of the deadliest viral infections that causes severe hemorrhagic fever in human and nonhuman primates, which has resulted in increased mortality rates in the affected African countries. Thus, the current study mapped and quantified global research output and trends in the EBOV vaccine publications via a bibliometric analysis.Methods: Publications about the Ebola virus vaccine were extracted from the Web of Science and Scopus databases. HistCite, Bibliometrix, an R package, and VOSviewer.Var1.6.6 were used for data mapping and analysis.Results: A total of 541 (WoS) and 511 (Scopus) documents were included, with a cumulation of 24,611 citations in both databases. These documents were published in 141 journals in the Wos and 185 in Scopus. The USA was the most productive country with 206 (38.08%) publications in the Wos. Although the top-cited authors are from the USA, the United Kingdom, and Canada, only one author from Africa “Samai M” from the University of Sierra Leone contributed 13 publications. Meanwhile, the Journal of Infectious Diseases was the most productive (45, 8.32%) in this field.Conclusion: The study provides insight for researchers and health policy on the trends and progress of the EBOV vaccine research and development, focusing on the hot topics, research collaboration, and research dearth that requires urgent redress to fast-track an all-inclusive EBOV vaccine development.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.050 | 0.264 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".