Immune histories and natural infection protection during the omicron era
Bibliographic record
Abstract
BACKGROUND: Past immunological events can either enhance or compromise an individual's future immune protection. This study investigated how different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) natural infection histories before an omicron infection, with or without vaccination, influence protection against subsequent omicron reinfection. METHODS: Three national, matched, retrospective cohort studies were conducted in Qatar from February 28, 2020, to August 12, 2024 to compare incidence of omicron reinfection between individuals with two omicron infections (omicron double-infection cohort) and those with one (omicron single-infection cohort); the omicron double-infection cohort with individuals who had a pre-omicron infection followed by an omicron reinfection (pre-omicron-omicron double-infection cohort); and the pre-omicron-omicron double-infection cohort with the omicron single-infection cohort. RESULTS: Here we show that, in the first study, comparing the omicron double-infection cohort to the omicron single-infection cohort, the adjusted hazard ratio (aHR) is 1.27 (95% CI: 1.13-1.43); 0.93 (95% CI: 0.68-1.28) for the unvaccinated and 1.34 (95% CI: 1.18-1.52) for the vaccinated. In the second study, comparing the omicron double-infection cohort to the pre-omicron-omicron double-infection cohort, the aHR is 1.37 (95% CI: 1.13-1.65); 1.12 (95% CI: 0.63-1.97) for the unvaccinated and 1.42 (95% CI: 1.16-1.74) for the vaccinated. In the third study, comparing the pre-omicron-omicron double-infection cohort to the omicron single-infection cohort, the aHR is 0.97 (95% CI: 0.92-1.03); 0.75 (95% CI: 0.66-0.85) for the unvaccinated and 1.03 (95% CI: 0.97-1.09) for the vaccinated. CONCLUSIONS: Immune history shapes protection against omicron reinfection, with pre-omicron-omicron immunity enhancing protection, while repeated similar exposures reduce protection against new variants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".