The association between tropical sea surface temperature variability and sentinel reporting of travel-related dengue 
Bibliographic record
Abstract
Oceanic-atmospheric interactions play a crucial role in the modulation of monsoon rainfall. This is the first study that directly investigates the impact of tropical sea surface temperature (SST) variability on the frequency of sentinel reporting of travel-related dengue from the Geosentinel global emerging infectious disease surveillance network, by using the latest climate reanalysis ERA-5 produced by the European Center for Medium-Range Weather Forecasts, for the period 2007 to 2019. More specifically, we explore lag structures and the associated spatial correlation patterns between travel-related dengue cases, SSTs, and total precipitation over the tropics. We found that the Indo-Pacific and Atlantic Ocean SSTs have a remote influence on dengue risk in global regions that exhibit distinct monsoon characteristics. The coupling between SST variations and rainfall is an important driver of travel-related dengue cases and could act as an early warning signal for outbreak preparedness and travel medicine preventive advice. Finally, our findings highlight the need to better understand the large-scale and local circulation response to changes in the pattern of tropical ocean warming, to be able to better predict extreme events such as droughts and floods and devise adaptation measures against dengue outbreaks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".