Potential utility of Indian Ocean sea surface temperature for predicting dengue outbreaks in South Central Asia
Bibliographic record
Abstract
Dengue has emerged as a significant public health challenge and the world's most prevalent climate-sensitive mosquito-borne disease. No antiviral drugs are currently available to treat the disease, but vaccine development has led to promising results in reducing dengue’s burden. As climate change is predicted to lead to geographic expansion of vector populations and increases in dengue outbreaks, the development of early warning systems is critical to improving outbreak preparedness to respond to dengue epidemics. Here, we investigate the remote response of tropical Indian Ocean sea surface temperature (SST) variability to dengue case counts in South Central Asia (SCA). More specifically, we provide new evidence on the association between the main modes of oceanic SST variability and dengue case counts using singular value decomposition (SVD) analysis. A cross-correlation analysis is then performed to quantify the maximum correlations and lags between SST climate indices and dengue case counts in SCA. We used traveler data from the GeoSentinel global infectious disease surveillance network and dengue case counts from the OpenDengue project. SST data was retrieved from the latest fifth generation global reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The results were compared with gridded SST datasets from observational reports and satellite data (HadISST1 and ERSSTv5). The SVD analysis reveals significant influence of SST anomalies on dengue case counts. The first leading SVD mode, which accounts for 25% of the total square covariance, represents the Indian Ocean basin mode, which is characterized by basin-wide warming and is statistically significantly correlated with dengue case counts. We found that positive SST anomalies over the western tropical Indian Ocean were associated with a surge in dengue cases in SCA after a lag time of 1-2 months. Our study demonstrated potential for predicting regional dengue epidemics based on remote SSTs. Combining dengue surveillance data and climatological data may be a promising mechanism to anticipate the geographic locations of future 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".