A bibliometric analysis of vaccine research on Chikungunya virus: contributors, trends, and emerging frontiers
Bibliographic record
Abstract
mosquitoes and has the potential to become a globally pandemic pathogen. Infection with CHIKV typically results in high fever and often debilitating, sometimes chronic polyarthralgia, as well as central nervous system complications. To date, only few highly effective drugs available for treating CHIKV infection or controlling its replication and transmission. Immunotherapeutic strategies and vaccine development are considered promising approaches, particularly nucleic acid-based vaccines, which offer the advantages of rapid development, high efficacy, and precision. In this study, we performed a bibliometric analysis (the literature search was conducted up to June 2025) using data from three major medical databases to explore research trends, geographic and institutional distribution, and key research hotspots in the field of CHIKV immunotherapy and vaccine development. Our findings reveal a rapidly growing body of literature with significant contributions from leading research institutions in the United States, particularly the University of Texas system. Among the various vaccine types studied, live-attenuated vaccines and nucleic acid-based vaccines dominate the field, with the latter showing a marked increase in research attention in recent years.
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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.187 | 0.274 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".