Effectiveness of mRNA COVID-19 Vaccines and Hybrid Immunity in Preventing SARS-CoV-2 Infection and Symptomatic COVID-19 Among Adults in the United States
Bibliographic record
Abstract
BACKGROUND: Understanding protection against SARS-CoV-2 infection by vaccine and hybrid immunity is important for informing public health strategies as new variants emerge. METHODS: We analyzed data from 3 cohort studies spanning 1 September 2022 to 31 July 2023 to estimate COVID-19 vaccine effectiveness (VE) against SARS-CoV-2 infection and symptomatic COVID-19 among adults with and without prior infection in the United States. Participants collected weekly nasal swabs irrespective of symptoms, participated in annual blood draws, and completed periodic surveys, which included vaccination status and infection history. Swabs were tested molecularly for SARS-CoV-2. VE was estimated by Cox proportional hazards models for the hazard ratios of infections, adjusting for covariates. VE was calculated considering prior infection and recency of vaccination. RESULTS: Among 3344 adults, the adjusted VE of a bivalent vaccine against infection was 37.2% (95% CI, 12.3%-55.7%) within 7 to 59 days of vaccination and 21.1% (95% CI, -0.5% to 37.1%) within 60 to 179 days of vaccination when compared with participants who were unvaccinated or had received an original monovalent vaccine dose ≥180 days prior. Overall, the adjusted VE of a bivalent vaccine against infection, in conjunction with prior infection, was 62.2% (95% CI, 46.0%-74.5%) within 7 to 179 days of vaccination and 39.4% (95% CI, 12.5%-61.6%) at ≥180 days when compared with naive participants who were unvaccinated or had received a monovalent vaccine dose ≥180 days prior. CONCLUSIONS: Adults with prior infection and recent vaccination had high protection against infection and symptomatic illness. Recent vaccination alone provided moderate 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 | 0.005 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".