Clinical and phylogenetic influenza dynamics for the 2019-20 season in the global influenza hospital surveillance network (GIHSN) – Pilot study
Bibliographic record
Abstract
BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) has operated with the aim of investigating epidemiological and clinical factors related to severe influenza-related hospitalisations. STUDY DESIGN: A common GIHSN core protocol for prospective patient enrolment was implemented. Hospital personnel completed a standardized questionnaire regarding the included patients' medical history, compiled a hospitalisation summary, collected an upper respiratory swab sample for laboratory diagnosis, and genome sequencing was performed for a subset of samples. Patient data were compared according to influenza subtype, lineage, and phylogenetic groups using the Fisher's exact test. RESULTS: From September 2019 to May 2020, 8791 patients aged ≥5 years were included. Among them, 3021 (34.4%) had a laboratory-confirmed influenza diagnosis. Influenza A(H1N1)pdm09 dominated the season among all age groups, while the B/Victoria-like lineage accounted for over half of the infections among younger age groups (5-49 years). Sequencing of the hemagglutinin segment was possible for 623 samples and revealed an influenza A and B clade frequency among severe influenza hospitalisations similar to other medically attended surveillance networks, such as the WHO GISRS. No phylogenetic clustering was observed among hemagglutinin substitutions depending on the administration of supplemental oxygen or vaccine failure. CONCLUSIONS: The GIHSN confirms its ability as an international hospital-based active surveillance network to provide valuable information on influenza infection dynamics in hospital settings. Increasing the number of participating sites and compiling more complete data, such as genome sequencing, will allow the exploration of associations between viral factors, vaccine protection, and disease severity.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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; 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".