Global research trends in West Nile virus from 1943 to 2016: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: West Nile virus (WNV) is an emerging infectious disease which is most commonly transmitted to humans through mosquito, and is considered a major public-health problem worldwide. The aim of the current study is to bibliometrically analyze the quantity and quality of publications indexed in Scopus from different countries to reveal the characteristics of global research output regarding WNV. METHODS: This study is a bibliometric analysis based on the Scopus database. This study focused on identifying WNV publication trends with regard to publication year, publication type, prolific countries, language of publication, as well as, prolific journals, citations, and collaboration patterns. RESULTS: A total of 4729 publications were considered in this study, which were published between 1943 and 2016. The annual quantity of literature published before 2000 followed a low rate of research growth; while the quantity of publications after 2000 were published in a stage of rapid development. The country with the greatest number of publications in WNV research field was the USA with 2304 (48.7%) publications, followed by France with 224 (4.7%) publications, and Canada with 222 (4.7%) publications. Contributions from low- and middle-income countries (LMIC) were considerably small, that is, (n = 519 publications; 11%). All publications related to WNV achieved h-index of 140 and were cited 124,222 times. The median [interquartile range] number of citations per article thus amounts to 9 [2-28]. The USA had the highest h-index of 131. Emerging Infectious Diseases is the most productive journal with 227 articles, followed by Journal of Virology with 162 publications. The result designated that Centers for Disease Control and Prevention was ranked the first in terms of publication output, followed by National Center for Emerging and Zoonotic Infectious Diseases. CONCLUSIONS: There is an obvious trend of WNV research after 2000, and countries with high income have more contributions in WNV research field. The research output is low among LMIC. The USA produced the largest number of publications. The Centers for Disease Control and Prevention obtained the leading position of the institutions in terms of publication output. In general, this study not only presents a full view of global WNV research, but also can contribute for future further research in this field.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.121 | 0.193 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".