Early COVID-19 and protection from Omicron in a highly vaccinated population in Ontario, Canada: a matched prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Predictions regarding the on-going burden of SARS-CoV-2, and vaccine recommendations, require an understanding of infection-associated immune protection. We assessed whether early COVID-19 provided protection against Omicron infection. METHODS: We enrolled a cohort of adults in Ontario, Canada, with COVID-19 prior to October 2020 (early infection, EI), and a matched cohort with COVID-19 testing and a negative PCR (non-EI). Participants completed baseline surveys then surveys every two weeks until January 2023. Multivariable Cox regression was used to assess factors associated with COVID-19 infection during the first 14 months of Omicron. RESULTS: Overall, 624 EI (70%) and 175 (77%) non-EI participants met criteria for analysis; 590 (95%) EI and 164 (94%) non-EI had received at least 2 COVID-19 vaccine doses prior to Omicron. Of 624 EI, 175 (28%) had one SARS-CoV-2 re-infection and 8 (1.3%) had two, compared to 84 (48%) non-EI participants with one, 5 (2.9%) with two and 1 (0.6%) with 3 infections (P < 0.0001). In multivariable analysis of risk factors for Omicron infection, the overall hazard ratio (HR, 95%CI) associated with EI was 0.56 (0.43-0.74); HRs for BA.1/2, BA.4/5 and mixed BA.5/BQ.1/XBB periods were 0.66 (0.45-0.97), 0.44 (0.28-0.68) and 0.71 (0.32-1.56). EI and BA.1/2 infection combined reduced later Omicron infection (HR 0.07 (0.03-0.21) compared to no prior infection. Older age, non-White ethnicity, no children in household, and lower neighbourhood income were associated with reduced risk of infection. CONCLUSIONS: In our highly vaccinated population, early SARS-CoV-2 infection was associated with a 44% reduction in symptomatic COVID-19 during the first 14 months of Omicron, providing significant protection against re-infection for more than 2 years.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".