Projected Burden of Enteric Bacterial Infections Under Climate Change Scenarios
Bibliographic record
Abstract
MS5-03 Abstract: A number of studies have shown an effect of ambient temperature on the occurrence of certain enteric diseases after controlling for seasonal and long-term trends, suggesting a vulnerability to increased enteric diseases because of climate change. The WHO estimates that in 2030 the risk of diarrheal diseases will be up to 10% higher in some regions because of climate change. In this study, we are interested in determining what additional burden of enteric diseases might be seen in Canada under selected climate change scenarios using models from the Hadley Centre and the Canadian Centre for Climate Modeling. Enteric diseases are already an important burden in Canada and are sensitive to variations in temperature and precipitation. With time series methods, we project the future burden of enteric disease based on national notifiable and hospitalization data of enteric bacterial infections using age-specific rates, future population growth, and socioeconomic status. Ten cities in Canada were selected between 1992 and 2000. We used downscaled climate change model data Hadley 3 and CGCM2 models to project future burden of disease under climate scenarios 2030, 2050, and 2080.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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 teacher head, 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".