Exploring 97 Years of Aedes aegypti as the Vector for Dengue, Yellow Fever, Zika, and Chikungunya (Diptera: Culicidae): Scientometric Analysis
Bibliographic record
Abstract
BACKGROUND: Aedes aegypti is an important vector that transmits dengue, Zika, chikungunya, and yellow fever viruses. Although research on Aedes aegypti has been conducted for decades, scientometric studies on Aedes aegypti are scarce, are limited to regions, and cover short periods. Thus, there is still a knowledge gap in the current trend, research focuses and directions, leading authors and collaboration, journal and citation impacts, countries, and worldwide collaborations. OBJECTIVE: The objectives of the study are to investigate the research trend, focus and directions, citation impact, leading authors and collaboration, journals, and countries of the published works on Aedes aegypti to inform the current knowledge gaps and future direction of the control of the vector. METHODS: In this study, we searched the Scopus database for articles on Aedes aegypti published from the year 1927 until April 5th, 2024, and included articles, reviews, books, and book chapters that were written in English. A total of 16,247 articles in 160 journals with 481,479 citations were included. Inconsistencies in authors' names were checked and cleaned using OpenRefine. The data were grouped into 4 periods; years 1927-1999, 2000-2009, 2010-2019, and 2020-2023. The relative growth rate and doubling time of publications were calculated. The analysis was conducted using VOSviewer, R bibliometrics, and citeSpace. RESULTS: The overall RGR was 0.1. Doubling time increased from 9.3 in 1978-1998 to 12.1 in 2000-2009. The main research clusters were "using Wolbachia," "Dengue Zika," "worldwide diversity," "community support," "larvicidal activity," "mosquito genotype-dependent," and "sterile insect technique." Journal of Medical Entomology was the leading journal (758/16,247, 4.7%). The most cited articles were authored by Halstead SB and team in Science (N=1355) and Kraemer MU and team in eLife (N=1324). The United States (5806/23,538, 24.7%) and Brazil (2035/23,538, 8.6%) were the top countries. Gubler DJ was the top co-cited author (n=2892) from 2000 to 2019. The co-cited author cluster patterns informed the significant specialty research on Aedes aegypti across time. Authors from various specialized research fields tended to collaborate across countries, especially neighboring countries. Countries with more research funding on the study of Aedes aegypti published more papers. CONCLUSIONS: Researchers or entomologists could understand the current knowledge gap on Aedes aegypti and plan for future research pathways. This study contributed to the public health stakeholders in improving the vector control interventions and elucidated the extent of research subject areas.
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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 | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.080 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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, 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".