Estimated impact of aggressive empirical antiviral treatment in containing an outbreak of pandemic influenza <scp>H</scp>1<scp>N</scp>1 in an isolated <scp>F</scp>irst <scp>N</scp>ations community
Bibliographic record
Abstract
BACKGROUND: The 2009 influenza A (H1N1) pandemic was mild by historical standards, but was more severe in isolated Canadian Indigenous communities. Oseltamivir was used aggressively for outbreak control in an isolated northern Ontario First Nations community. We used mathematical modeling to quantify the impact of antiviral therapy on the course of this outbreak. METHODS: We used both a Richards growth model and a compartmental model to evaluate the characteristics of the outbreak based on both respiratory visits and influenza-like illness counts. Estimates of best-fit model parameters, including basic reproductive number (R0 ) and antiviral efficacy, and simulations, were used to estimate the impact of antiviral drugs compared to social distancing interventions alone. RESULTS: Using both approaches, we found that a rapidly growing outbreak slowed markedly with aggressive antiviral therapy. Richards model turning points occurred within 24 hours of antiviral implementation. Compartmental models estimated antiviral efficacy at 70-95%. Plausible estimates of R from both modeling approaches ranged from 4·0 to 15·8, higher than published estimates for southern Canada; utilization of aggressive antiviral therapy in this community prevented 962-1757 cases of symptomatic influenza and as many as 114 medical evacuations in this community. CONCLUSION: Although not advocated in other settings in Canada, aggressive antiviral therapy markedly reduced the impact of a pandemic-related influenza A (H1N1) outbreak in an isolated Canadian First Nations community in northern Ontario, Canada. The differential risk experienced by such communities makes tailored interventions that consider risk and lack of access to medical services, appropriate.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads 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".