Original Article: A composite epidemic curve for seasonal influenza in Canada with an international comparison
Bibliographic record
Abstract
BACKGROUND: Empirical data on laboratory-confirmed seasonal influenza is limited by very low and possibly non-systematic case ascertainment as well as geographical variation. OBJECTIVE: To provide a visual representation of an influenza epidemic at the community and regional level using empirical data and to describe the epidemic characteristics. METHODS: Weekly influenza A confirmations were obtained from the Canadian FluWatch program and American FluView program for the 1997/1998-2006/2007 seasons; 1- year data were also available for Europe (FluNet, WHO). For seasons where at least 80% of the influenza A strains were antigenically similar, a composite epidemic curve was created by centring the local epidemics relative to their epidemic midpoint. RESULTS: The range in timing of the regional peaks varied from 5 to 13 weeks. Once the epidemic curves were centred relative to their peak, the composite epidemic curves were similar for Canada, the United States and Europe, and the epidemic growth rates were similar for most subgroups (city size; regions; H1N1 versus H3N2 seasons). During the exponential growth period, the number of cases increased by a factor of 1.5-2.0 per week, averaging 1.8. Exponential growth was evident approximately 10 weeks before the peak. Evidence of sustained transmission occurred from mid-September to early June. DISCUSSION: The shape of the composite curve created in this study clearly demonstrates a consistency in the epidemic pattern across geographically disparate locales. Laboratory confirmation will likely play an increasing role in the development of better methods for early detection and summary measures of influenza activity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".