Correlates of protection and determinants of SARS-CoV-2 breakthrough infections 1 year after third dose vaccination
Bibliographic record
Abstract
Abstract Background The emergence of new SARS-CoV-2 variants and the waning of immunity raise concerns about vaccine effectiveness and protection against COVID-19. While antibody response has been shown to correlate with the risk of infection with the original variant and earlier variants of concern, the effectiveness of antibody-mediated protection against Omicron and the factors associated with protection remain uncertain. Methods We evaluated antibody responses to SARS-CoV-2 spike (S) and nucleocapsid (N) antigens from Wuhan and variants of concern by Luminex and their role in preventing breakthrough infections 1 year after a third dose of mRNA vaccination, in a cohort of health care workers followed since the pandemic onset in Spain (N = 393). Data were analyzed in relation to COVID-19 history, demographic factors, comorbidities, vaccine doses, brand, and adverse events. Results Higher levels of anti-S IgG and IgA to Wuhan, Delta, and Omicron were associated with protection against vaccine breakthroughs (IgG against Omicron S antigen HR, 0.06, 95%CI, 0.26–0.01). Previous SARS-CoV-2 infection was positively associated with antibody levels and protection against breakthroughs, and a longer time since last infection was associated with lower protection. In addition, priming with BNT162b2 followed by mRNA-1273 booster was associated with higher antibody responses than homologous mRNA-1273 vaccination. Conclusions Data show that IgG and IgA induced by vaccines against the original strain or by hybrid immunization are valid correlates of protection against Omicron BA.1 despite immune escape and support the benefits of heterologous vaccination regimens to enhance antibodies and the prioritization of booster vaccination in individuals without recent infections.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.010 | 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".