Age-specific Differences in Influenza A Epidemic Curves: Do Children Drive the Spread of Influenza Epidemics?
Bibliographic record
Abstract
There is accumulating evidence suggesting that children may drive the spread of influenza epidemics. The objective of this study was to quantify the lead time by age using laboratory-confirmed cases of influenza A for the 1995/1996-2005/2006 seasons from Canadian communities and laboratory-confirmed hospital admissions for the H1N1/2009 pandemic strain. With alignment of the epidemic curves locally before aggregation of cases, slight age-specific differences in the timing of infection became apparent. For seasonal influenza, both the 10-19- and 20-29-year age groups peaked 1 week earlier than other age groups, while during the fall wave of the 2009 pandemic, infections peaked earlier among only the 10-19-year age group. In the H3N2 seasons, infections occurred an average of 3.9 (95% confidence interval: 1.7, 6.1) days earlier in the 20-29-year age group than for youth aged 10-19 years, while during the fall pandemic wave, the 10-19-year age group had a statistically significant lead of 3 days compared with both younger children aged 4-9 years and adults aged 20-29 years (P < 0.0001). This analysis casts doubt on the hypothesis that younger school-age children actually lead influenza epidemic waves.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".