Seasonal variation of acute gastro-intestinal illness by hydroclimatic regime and drinking water source: a retrospective population-based study
Bibliographic record
Abstract
Acute gastro-intestinal illness (AGI) is a major cause of mortality and morbidity worldwide and an important public health problem. Despite the fact that AGI is currently responsible for a huge burden of disease throughout the world, important knowledge gaps exist in terms of its epidemiology. Specifically, an understanding of seasonality and those factors driving seasonal variation remain elusive. This paper aims to assess variation in the incidence of AGI in British Columbia (BC), Canada over an 11-year study period. We assessed variation in AGI dynamics in general, and disaggregated by hydroclimatic regime and drinking water source. We used several different visual and statistical techniques to describe and characterize seasonal and annual patterns in AGI incidence over time. Our results consistently illustrate marked seasonal patterns; seasonality remains when the dataset is disaggregated by hydroclimatic regime and drinking water source; however, differences in the magnitude and timing of the peaks and troughs are noted. We conclude that systematic descriptions of infectious illness dynamics over time is a valuable tool for informing disease prevention strategies and generating hypotheses to guide future research in an era of global environmental change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".