Intraseasonal Waning of Influenza Vaccine Effectiveness and Optimal Timing for Seasonal Influenza Vaccination Programs - Scoping Review Protocol
Bibliographic record
Abstract
In the northern hemisphere, including Canada, influenza vaccination is generally recommended prior to the beginning of the season, usually in October, as it takes approximately two weeks for the vaccine to provide maximum protection (NACI, 2024; ACIP, 2024; JCVI, 2024). Some jurisdictions recommend earlier vaccination (July to September) for certain groups, including children and pregnant individuals in their third trimester, to provide early protection for both children and newborns (ACIP, 2024; JCVI, 2024).. In Canada, the majority of influenza vaccine uptake occurs between October and November, and influenza activity typically peaks in January and February – although post-COVID-19 pandemic, the peak has been observed earlier in December and January (Table S1). Current seasonal influenza vaccines vary in effectiveness due to epidemiological factors such as the match between circulating and vaccine strains, as well as host-related factors such as age, pre-existing vaccine and/or infection-induced immunity, and underlying comorbidities (Dhakal et al., 2019; Beran et al., 2021). The optimal timing of influenza vaccination depends on several factors, including transmission patterns and viral evolution of influenza, individual factors (such as travel and pregnancy), waning immunity, and public health logistics. Key considerations for program timing include seasonal influenza circulation patterns, historical trends, peak immunity, duration of protection, strain match, target groups, vaccine availability, and opportunities for co-administration. For example, vaccinating too early (e.g., August-September) potentially risks waning immunity before the peak of influenza season, while vaccinating too late (e.g., December-January) may leave many individuals vulnerable to infection before immunity develops. Such considerations will become increasingly important with the introduction of combination vaccines, such as combination COVID-19 and influenza vaccines, where the optimal timing of vaccination against more than one disease will need to be considered. The current review aims to: 1) summarize recent evidence on intra-seasonal waning protection conferred by seasonal influenza vaccines; and 2) scope existing research and regulatory discussions regarding the optimal timing for seasonal influenza vaccination programs.
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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 | 0.054 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".