Global Seasonal Influenza Mortality Estimates: A Comparison of 3 Different Approaches
Bibliographic record
Abstract
Prior to updating global influenza-associated mortality estimates, the World Health Organization convened a consultation in July 2017 to understand differences in methodology and implications for results of 3 influenza mortality projects from the US Centers for Disease Control and Prevention (CDC), the Netherlands Institute for Health Service Research's Global Pandemic Mortality Project II (GLaMOR), and the Institute for Health Metrics and Evaluation (IHME). The expert panel reviewed estimates and discussed differences in data sources, analysis, and modeling assumptions. We performed a comparison analysis of the estimates. Influenza-associated respiratory death counts were comparable between CDC and GLaMOR; the IHME estimate was considerably lower. The greatest country-specific influenza-associated fold differences in mortality rate between CDC and IHME estimates and between GLaMOR and IHME estimates were among countries in Southeast Asia and the Eastern Mediterranean region. The data envelope used for the calculation was one of the major differences (CDC and GLaMOR: all respiratory deaths; IHME: lower-respiratory infection deaths). With the assumption that there is only one cause of death for each death, IHME estimates a fraction of the full influenza-associated respiratory mortality that is measured by the other 2 groups. Wide variability of parameters was observed. Continued coordination between groups could assist with better understanding of methodological differences and new approaches to estimating influenza deaths globally.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".