Protection from second booster vaccines and natural immunity against SARS-CoV-2 infections, 2022–2023
Bibliographic record
Abstract
We estimated the vaccine effectiveness (VE) of second monovalent and bivalent booster vaccines containing Omicron BA.1 or BA.4/BA.5 and the protection conferred by natural immunity against SARS-CoV-2 infection in Luxembourg. We conducted a test-negative case-control study among residents aged 60 years or older by integrating national socio-demographic, COVID-19 vaccination, and testing data, achieving full population coverage. Using conditional logistic regression, we estimated absolute and relative VE of monovalent and bivalent boosters and natural immunity from prior infection. Our analysis included 5,390 test-positive cases and 11,048 test-negative controls matched by week of testing between September 2022 and April 2023. Absolute VE for monovalent and bivalent boosters decreased from 64.8% and 66.6% in the first month to 1.5% and 16.5% after 5-6 months, respectively. The bivalent was superior to the monovalent booster only in individuals without natural immunity (relative VE 25.7%, 95% confidence interval 11.4%; 37.7%). Natural immunity lasted longer than vaccine-induced immunity with 80.7% protected at 4-8 months and 44.9% at 15-25 months post-infection. Both second booster vaccines provided temporary protection against SARS-CoV-2 infection; bivalent boosters offered a slight benefit over monovalent boosters. Natural immunity appears to confer longer-lasting protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".