© 2010 Canadian Medical Association or its licensors
Bibliographic record
Abstract
The emergence and global spread of pandemic H1N1influenza led the World Health Organization todeclare a pandemic on June 11, 2009. As the pan-demic spreads, countries will need to make decisions about strategies to mitigate and control disease in the face of uncertainty. For novel infectious diseases, accurate estimates of epidemi-ologic parameters can help guide decision-making. A key para-meter for any new disease is the basic reproductive number (R0), defined as the average number of new cases created by a single primary case in a susceptible population. R0 affects the growth rate of an epidemic and the final number of infected people. It also informs the optimal choice of control strategies. Other key parameters that affect use of resources, disease burden and soci-etal costs during a pandemic are duration of illness, rate of hos-pital admission and case-fatality rate. Early in an epidemic, the case-fatality rate may be underestimated because of the tempo-ral lag between onset of infection and death; the delay between initial identification of a new case and death may lead to an apparent increase in deaths several weeks into an epidemic that is an artifact of the natural history of the disease. We used data from initial reports of laboratory- confirmed pandemic H1N1 influenza to estimate epidemiologic parame-ters for pandemic H1N1 influenza. The parameters included R0, incubation period and duration of illness. We also esti-mated risk of hospital admission and case-fatality rates, which can be used to estimate the burden of illness likely to be asso-ciated with this disease. Methods Data collection We collected individual-level data on laboratory-confirmed cases of pandemic H1N1 influenza in the province of Ontario, Canada, with a reported date of symptom onset between
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.879 | 0.799 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".