Exploring the potential public health benefits of universal influenza vaccine
Bibliographic record
Abstract
Background: Broadly protective, long-lasting universal influenza vaccines are under development in response to low-moderate seasonal vaccine effectiveness, frequent genetic changes in circulating viruses and extended turnaround for vaccine manufacture. Because a long-lasting vaccine might be less effective than a seasonal vaccine that has been matched to current circulating strains, the public health impact of its introduction should be evaluated.Methods: A modified agent-based model (ABM) examined multi-year effects of a universal vaccine among 18 to 49-year-olds, given in Year 1 only. The proportion of vaccinated 18 to 49-year-olds who received universal vaccine was varied from 0% to 100%. Model parameters were drawn from US databases and the medical literature. Outcomes were 4-year cumulative and annual influenza cases as well as annual cases averted/100,000 population for 3 age groups, 0–17 years, 18–49 years and 50+ years.Results: In Year 1 when universal vaccine was given to 50% or 100% of all vaccinated 18 to 49-year-olds, more influenza cases occurred, compared to no universal vaccine, but fewer cases occurred in Years 2–4 as overall protection increased. Cumulative averted cases over 4 years in 18 to 49-year-olds were 892/100,000 and 1,687/100,000 population for the 50% and 100% universal vaccine for 18 to 49-year-olds scenarios, respectively, with additional benefits to children and older adults through indirect effects.Conclusions: In ABM, the universal vaccine with a conservative VE estimate given once to 18 to 49-year-olds reduced influenza cases among all age groups in Years 2–4 following its introduction. Reduced influenza burden may occur sooner if VE of universal vaccines exceeds that assumed in these models.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".