Bibliometric Analysis of the Evolution and Distribution of Research on Analytical Methods for Climate-Sensitive Infectious Diseases in Latin America and the Caribbean
Bibliographic record
Abstract
Climate-Sensitive Infectious Diseases (CSIDs) are diseases whose prevalence and transmission are heavily influenced by climatic factors, posing a significant challenge to public health, particularly in vulnerable regions such as Latin America and the Caribbean (LAC). This study employs a bibliometric analysis to evaluate the evolution and distribution of research on CSID and the analytical methods employed in the field. Using bibliometric and text-mining techniques, the analysis examines publication trends, research hotspots, and methodological developments from 2015 to 2024. The results highlight a regional concentration of research, with Brazil leading in CSID studies, particularly on arboviruses such as dengue, Zika, and chikungunya. The analysis also reveals the predominance of regression models, time-series analysis, and spatial analysis as primary methods used to forecast and analyze disease outbreaks. However, advanced techniques such as neural networks and niche modeling are gaining traction, indicating a shift towards more data-intensive approaches. The findings underscore the importance of enhancing forecasting capabilities and integrating analytical models into public-health systems to anticipate the impact of climate change on disease patterns. This study offers critical insights into methodological trends and identifies gaps for future research, contributing to more effective decision making in public health across Latin America and the Caribbean.
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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 | 0.019 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.159 | 0.220 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 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".