A Machine Learning-based Dynamic SST Index for Long-lead Malaria Prediction in the Peruvian Amazon
Bibliographic record
Abstract
Malaria imposes a major health burden in the Peruvian Amazon, and its early warning is essential for effective disease prevention. The tropical sea surface temperature (SST) variability, fundamentally shaping the global weather patterns, may also alter malaria transmission and potentially improve its long-lead predictability. In this study, we propose a machine learning-based methodology to identify long-lead predictors for Peruvian malaria from the ocean. First, we demonstrate that significant correlations broadly exist between tropical sea surface temperature (SST) anomalies and Peruvian malaria incidence across different seasons and time lags, confirming the potential predictability from the tropical ocean. Then, we apply the self-organizing map to synthesize the spatiotemporally varying SST-malaria relationship and identify a unique dynamic SST index for Peruvian malaria. The dynamic SST index provides better prediction performance (higher correlation coefficients and lower root mean square errors) in the single-predictor generalized linear model, compared to the traditional El Niño–Southern Oscillation (ENSO) index, with lead times exceeding three months. Furthermore, the dynamic SST index captures the evolution of the ENSO life cycle from its precursor climate mode (Pacific meridional mode) and appears to influence Peruvian malaria by altering the local near-surface air temperature and specific humidity. Such underlying mechanisms provide the physically plausible basis for the long-lead predictability of Peruvian malaria using a machine learning-based remote predictor.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".