Severity Scale of Influenza and Acute Respiratory Illness Hospitalizations to Support Viral Genomic Surveillance: A Global Influenza Hospital Surveillance Network Pilot Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to establish a Severity Scale for influenza and other acute respiratory infections (ARI), requiring hospitalization, for surveillance and research purposes (the SevScale). Such a scale could aid the interpretation of data gathered from disparate settings. This could facilitate pooled analyses linking viral genetic sequencing data to clinical severity, bringing insights to inform influenza surveillance and the vaccine strain selection process. METHODS: We used a subset of data from the Global Influenza Hospital Surveillance Network database, including data from different geographical areas and income levels. To quantify the underlying concept of severity, an item response model was developed using 16 indicators of severity related to the hospital stay. Each patient in the dataset was assigned a Severity Score and a Severity Category (low, medium, or high severity). Finally, we compared the model scores across different subgroups. RESULTS: Data from 9 countries were included, covering between 4 and 11 seasons from 2012 to 2022, with a total of 96,190 ARI hospitalizations. Not for all severity indicators data were available for all included seasons. Subgroups with a high percentage of patients in the high Severity Category included influenza A(H1N1)pdm09, age ≥ 50, lower-middle income countries, and admission since the start of the COVID-19 pandemic. CONCLUSIONS: The initial model successfully highlighted severity disparities across patient subgroups. Repeating this exercise with new, more complete data would allow recalibration and validation of the current model. The SevScale proved to be a promising method to define severity for influenza vaccine strain selection, surveillance, and research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".