MODELING SEASONAL TRENDS OF FOODBORNE ILLNESS AND TEMPERATURE
Bibliographic record
Abstract
ISEE-214 Abstract: The incidence of enteric infections of people in Canada varies seasonally, and will therefore be expected to change in response to climate changes. In order to explore this possibility further, we investigated the potential shift in the seasonal patterns of the enteric pathogens in Canada. The data consists of three enteric pathogens, Salmonella, E. coli and Campylobacter, from the notifiable disease registry for two Canadian provinces, Alberta and Newfoundland-Labrador for the years 1992 to 2000. The project looks at the relationship between weekly occurrence of enteric illness and temperature, looking particularly at the effect of seasonal adjustments on the estimated models. This paper also explores different types of methodology for the analysis of time series of counts using generalized additive models and generalized linear models with regression splines. These methods are becoming widely used for environmental time series because they permit flexible adjustments for non-linear confounding effects of time trends, seasonality and weather variables. The results reveal a significant increase in temperatures and illness for the province of Alberta and this increase is more pronounced in summer compared to winter months. Newfoundland-Labrador on the other hand suggests very little response due to temperature but may be a result of a large number of under-reported cases of illness in the province and further analysis using zero-inflated methods should be considered. To conclude the different modeling procedures for time series were used and for the different pathogens and provinces the appropriate models ranged from threshold models, generalized additive models and generalized linear models. The results from the modeling suggest that with an increase in temperature in Canada there is a high risk of an increase in enteric illness. The results suggest an increased occurrence of enteric disease with increasing temperature. Since increased summer temperatures are widely projected for many parts of Canada, a change in the pattern of gastro-intestinal disease occurrence is possible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".