Seasonality of Influenza and Optimizing Timing of Vaccination: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Influenza continues to be a major global public health concern, with substantial regional variability in its seasonality and vaccine effectiveness. While temperate regions experience predictable winter peaks, tropical and subtropical countries exhibit complex patterns, including monsoon-related and year-round activity. This variability poses challenges to the current vaccination strategies, which are often guided by broad hemispheric recommendations rather than localized epidemiological data. This systematic review and meta-analysis aimed to assess global influenza seasonality, evaluate the timing of vaccination programs, and explore the impact of misalignment on vaccine effectiveness (VE), with a particular focus on India. We searched five databases (PubMed, Scopus, Medline, Google Scholar, and Cochrane Library), and a total of 32 studies were included in the qualitative synthesis, with 12 eligible for meta-analysis. The pooled VE across studies was 27.22% (95% CI: 17.36-37.08%), with significant heterogeneity (I² = 93.2%, p < 0.001). While modest, this level of protection still averts cases when coverage is high, and the marked heterogeneity (I² = 93.2%) signals that timing, age mix, and strain match substantially influence real-world impact. The quality of the included studies was assessed with the Cochrane Risk of Bias tool 2.0 and the Newcastle-Ottawa Scale. The meta-regression identified age and timing of vaccination as key contributors to variability in vaccine effectiveness. India exemplifies the challenge of heterogeneity within a single country. Northern regions such as Jammu and Kashmir display winter peaks (December-March), while central and eastern cities, including Delhi, Lucknow, and Kolkata, experience monsoon-season peaks (July-September), and South Indian cities such as Chennai and Vellore peak influenza activity during the late monsoon (October-November). However, influenza vaccination in India is delivered primarily through private sector providers, often beginning in April, and is not part of a centrally coordinated campaign, which may not align with regional peaks. This misalignment likely contributes to suboptimal VE in several regions. The findings underscore the urgent need to re-evaluate vaccination strategies globally and within India. Adoption of region-specific vaccination schedules - tailored to local influenza seasonality, evidence-informed, and supported by robust surveillance accounting for climate, geography, and population dynamics - could significantly enhance vaccine performance and reduce disease burden. This study is registered with PROSPERO, CRD42024611680.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".