Finding the real case-fatality rate of H5N1 avian influenza
Bibliographic record
Abstract
BACKGROUND: Accurate estimation of the case-fatality (CF) rate, or the proportion of cases that die, is central to pandemic planning. While estimates of CF rates for past influenza pandemics have ranged from about 0.1% (1957 and 1968 pandemics) to 2.5% (1918 pandemic), the official World Health Organization estimate for the current outbreak of H5N1 avian influenza to date is around 60%. METHODS AND RESULTS: The official estimate of the H5N1 CF rate has been described by some as an over-estimate, with little relevance to the rate that would be encountered under pandemic conditions. The reasons for such opinions are typically: (i) numerous undetected asymptomatic/mild cases, (ii) under-reporting of cases by some countries for economic or other reasons, and (iii) an expected decrease in virulence if and when the virus becomes widely transmitted in humans. Neither current data nor current literature, however, adequately supports these scenarios. While the real H5N1 CF rate could be lower than the current estimate of 60%, it is unlikely that it will be at the 0.1-0.4% level currently embraced by many pandemic plans. We suggest that, based on surveillance and seroprevalence studies conducted in several countries, the real H5N1 CF rate should be closer to 14-33%. CONCLUSIONS: Clearly, if such a CF rate were to be sustained in a pandemic, H5N1 would present a truly dreadful scenario. A concerted and dedicated effort by the international community to avert a pandemic through combating avian influenza in animals and humans in affected countries needs to be a global priority.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".