Development of hybrid immunity during a period of high incidence of Omicron infections
Bibliographic record
Abstract
BACKGROUND: Seroprevalence and the proportion of people with neutralizing activity (functional immunity) against SARS-CoV-2 variants were high in early 2022. In this prospective, population- based, multi-region cohort study, we assessed the development of functional and hybrid immunity (induced by vaccination and infection) in the general population during this period of high incidence of infections with Omicron variants. METHODS: We randomly selected and assessed individuals aged ≥16 years from the general population in southern (n = 739) and north-eastern (n = 964) Switzerland in March 2022. We assessed them again in June/July 2022, supplemented with a random sample from western (n = 850) Switzerland. We measured SARS-CoV-2 specific IgG antibodies and SARS-CoV-2 neutralizing antibodies against three variants (ancestral strain, Delta, Omicron). RESULTS: Seroprevalence remained stable from March 2022 (97.6%, n = 1894) to June/July 2022 (98.4%, n = 2553). In June/July, the percentage of individuals with neutralizing capacity against ancestral strain was 94.2%, against Delta 90.8% and against Omicron 84.9%, and 50.6% developed hybrid immunity. Individuals with hybrid immunity had highest median levels of anti-spike IgG antibodies titres [4518 World Health Organization units per millilitre (WHO U/mL)] compared with those with only vaccine- (4304 WHO U/mL) or infection- (269 WHO U/mL) induced immunity, and highest neutralization capacity against ancestral strain (hybrid: 99.8%, vaccinated: 98%, infected: 47.5%), Delta (hybrid: 99%, vaccinated: 92.2%, infected: 38.7%) and Omicron (hybrid: 96.4%, vaccinated: 79.5%, infected: 47.5%). CONCLUSIONS: This first study on functional and hybrid immunity in the Swiss general population after Omicron waves showed that SARS-CoV-2 has become endemic. The high levels of antibodies and neutralization support the emerging recommendations of some countries where booster vaccinations are still strongly recommended for vulnerable persons but less so for the general population.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".