The changing landscape of respiratory viruses contributing to respiratory hospitalisations: results from a hospital-based surveillance in Quebec, Canada, 2012-13 to 2021-22
Bibliographic record
Abstract
Abstract Background A comprehensive description of the combined effect of SARS-CoV-2 and respiratory viruses (RV) other than SARS-CoV-2 (ORV) on hospitalisations is lacking. Aim To compare viral etiology of acute respiratory infections (ARI) hospitalisations before and during two pandemic years from a surveillance network in Quebec, Canada. Method We compared detection of ORV and SARS-CoV-2 during 2020-21 and 2021-22 to 8 pre-pandemic influenza seasons in patients hospitalised with ARI who were tested systematically by a multiplex PCR. Results During pre-pandemic influenza seasons, overall RV detection was 92.7% (1,493) (48.3% respiratory syncytial virus (RSV)) in children and 62.8% (4,339) (40.1% influenza) in adults. Overall RV detection in 2020-21 was 58.6% (29) in children (all ORV) and 43.7% (333) in adults (3.4% ORV, 40.3% SARS-CoV2, both including coinfections). In 2021-22 overall RV detection was 91.0% (201) in children (82.8% ORV, 8.1% SARS-CoV-2, both including coinfections) and 55.5% (527) in adults (14.1% ORV, 41.4% SARS-CoV-2, both including coinfections). Virtually no influenza was detected in 2020-21 and in 2021-22 up to epi-week 2022-9 presented here; no RSV was detected in 2020-21. In 2021-22, detection of RSV was comparable to pre-pandemic years but with an unusually early season. There were significant differences in ORV and SARS-CoV-2 detection between time periods and age groups. Conclusion Significant continuous shifts in age distribution and viral etiology of ARI hospitalisations occurred during two pandemic years. This reflects evolving RV epidemiology and underscores the need for increased scrutiny of ARI hospitalisation etiology to inform tailored public health recommendations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".