Deterministic model for the role of antivirals in controlling the spread of the H1N1 influenza pandemic
Bibliographic record
Abstract
A deterministic model is designed and used to theoretically assess \nthe impact of antiviral drugs in controlling the spread of the 2009 swine influenza \npandemic. In particular, the model considers the administration of the \nantivirals both as a preventive as well as a therapeutic agent. Rigorous analysis \nof the model reveals that its disease-free equilibrium is globally-asymptotically \nstable under certain conditions involving having the associated reproduction \nnumber less than unity. Furthermore, the model has a unique endemic equilibrium \nif the reproduction threshold exceeds unity. The model provides a \nreasonable fit to the observed H1N1 pandemic data for the Canadian province \nof Manitoba. Numerical simulations of the model suggest that the singular \nuse of antivirals as preventive agents only makes a limited population-level \nimpact in reducing the burden of the disease in the population (except if the \neffectiveness level of this “prevention-only” strategy is high). On the other \nhand, the combined use of the antivirals (both as preventive and therapeutic \nagents) resulted in a dramatic reduction in disease burden. Based on the \nparameter values used in these simulations, even a moderately-effective combined \ntreatment-prevention antiviral strategy will be sufficient to eliminate the \nH1N1 pandemic from the province.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".