Change in Age profile of Respiratory Syncytial Virus disease over the course of annual epidemics: a multi-national study
Bibliographic record
Abstract
OBJECTIVES: We aimed to study whether the percentwise age distribution of RSV cases changes over time during annual epidemics. METHODS: We used surveillance data (2008-2019) from the Netherlands, Lyon (France), Portugal, Singapore, Ecuador, South Africa, and New Zealand. In each country, every season was divided into "epidemic quarters", i.e. periods corresponding to each quartile of RSV cases. Multinomial logistic regression models were fitted to evaluate whether the likelihood of RSV cases being aged <1 or ≥5 years (vs. 1 to <5) changed over time within a season. RESULTS: In all countries, RSV cases were significantly more likely to be aged <1 year in the 4th vs. 1st epidemic quarter; the relative risk ratio [RRR] ranged between 1.35 and 2.56. Likewise, RSV cases were significantly more likely to be aged ≥5 years in the 4th vs. 1st epidemic quarter (except in Singapore); the RRR ranged from 1.75 to 6.70. The results did not change when stratifying by level of care or moving the lower cut-off to 6 months. CONCLUSIONS: The age profile of RSV cases shifts within a season, with infants and adolescents, adults, and the elderly constituting a higher proportion of cases in the later phases of annual epidemics. These findings may have implications for RSV prevention policies with newly approved vaccines.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".