DENGUE IN 2025 EVOLVING EPIDEMIOLOGY, DIAGNOSTIC CHALLENGES, AND THE URGENT NEED FOR INTEGRATED VECTOR CONTROL
Bibliographic record
Abstract
As we enter 2025, dengue fever continues to pose one of the most formidable public health challenges across tropical and subtropical regions including Pakistan, South Asia, Southeast Asia, and parts of the Middle East 1. The epidemiological landscape of dengue has changed considerably over the past decade, driven by climate variability, rapid urbanization, human mobility, and the expanding geographic distribution of Aedes mosquitoes. This year, global surveillance networks are already reporting earlier seasonal onset, higher viral circulation, and a worrisome rise in secondary infections that predispose patients to severe dengue 2. The burden of dengue in 2025 is not merely a continuation of past trends but a reflection of deeply rooted systemic vulnerabilities. Increased rainfall variability, unplanned urban settlements, and inadequate waste management systems have created ideal breeding environments for Aedes aegypti and Aedes albopictus 3. In high-density populations such as those in Lahore, Karachi, Rawalpindi, Dhaka, and Manila, mosquito indices have surpassed previous thresholds within the first quarter of the year. These patterns signal the potential for prolonged transmission, expanded outbreaks, and greater pressure on healthcare services 4.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".