Vaccine effectiveness dynamics against influenza and SARS-CoV-2 in community-tested patients in France 2023–2024
Bibliographic record
Abstract
Background The epidemiology of respiratory viruses and vaccine effectiveness (VE) in the community is not well described. This study assessed VE against a positive test of influenza (VEf) and SARS-CoV-2 (VECov).Methods Data from two large networks of community-based laboratories in France were collected during standard of care in the 2023-2024 epidemic season (n = 511,083 RT-PCR tests). Multiplex PCR diagnostic tests were used. Patients’ demographics and symptoms were reported in addition to viral sequencing results. Test-negative design was used to separately estimate VEf and VECov, overall and stratified, by time since vaccination and calendar week.Results Adjusted VEf by age-group, sex, presence of symptoms, PCR technique, and week of testing, was 47.6% (95% CI: 44.3%-50.7%). VEf was lower in patients ≥65 years (42.0%; 95% CI: 36.6%-46.9%) than 18-64 years (52.9%; 95% CI: 48.6%-56.8%). The adjusted VEf against type A influenza, that represented 98% of typed viruses, was 51% (95% CI: 45%-56.6%) for patients vaccinated 15 days to 3 months before testing, and 35.5% (95% CI: 24.2%-45.3%) for those vaccinated 3 to 6 months before testing. For VECov, the adjusted estimate in patients vaccinated 15 days to 3 months prior to testing were 40.6% (95% CI: 7.2%-58.6%) at week 39, 24.8% (95% CI: 4.0%-38.8%) at week 45, and dropped systematically through the epidemic season as the JN.1 variant became dominant.Conclusion This study showed moderate VEf and VECov against infection in the community and highlighted the impact of time since vaccination and age for both estimates, and the new variant emergence on VECov. These findings should be considered in future vaccination campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".