Bibliographic record
Abstract
Mosquito-borne diseases represent a significant global health threat, impacting millions of people annually. This study provides a comprehensive overview of the epidemiological patterns of mosquito-borne diseases worldwide, focusing on malaria, dengue fever, Zika virus, chikungunya, yellow fever, and West Nile virus. It examines the primary mosquito species involved, including Anopheles , Aedes , and Culex , and explores the geographic distribution, seasonal variations, and the influence of socioeconomic and demographic factors on disease prevalence. Additionally, the research delves into the life cycle and vector competence of mosquitoes, the impact on public health through morbidity and mortality rates, and the economic burden on healthcare systems. Prevention and control strategies are discussed, with a focus on vector control methods, vaccination, medical interventions, and community-based initiatives. Case studies on malaria control in Sub-Saharan Africa and dengue outbreak management in Southeast Asia illustrate successful intervention strategies. This study concludes by addressing challenges such as insecticide resistance, the impact of climate change, and the need for innovative disease management approaches, providing recommendations for future research and global health policy implications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".