Timing of Breakthrough Infection Risk After Vaccination Against SARS-CoV-2
Bibliographic record
Abstract
Abstract Background Provision of safe and effective vaccines has been a remarkable public health achievement during the SARS-CoV-2 pandemic. The effectiveness and durability of protection of the first two doses of SARS-CoV-2 vaccines is an important area for study, as are questions related to optimal dose combinations and dosing intervals. Methods We performed a case-cohort study to generate real-world evidence on efficacy of first and second dose of SARS-CoV-2 vaccines, using a population-based case line list and vaccination database for the province of Ontario, Canada between December 2020 and October 2021. Risk of infection after vaccination was evaluated in all laboratory-confirmed vaccinated SARS-CoV-2 cases, and a 2% sample of vaccinated controls, evaluated using survival analytic methods, including construction of Cox proportional hazards models. Vaccination status was treated as a time-varying covariate. Results First and second doses of SARS-CoV-2 vaccine markedly reduced risk of infection (first dose efficacy 68%, 95% CI 67% to 69%; second dose efficacy 88%, 95% CI 87 to 88%). In multivariable models, extended dosing intervals were associated with lowest risk of breakthrough infection (HR for redosing 0.64 (95% CI 0.61 to 0.67) at 6-8 weeks). Heterologous vaccine schedules that mixed viral vector vaccine first doses with mRNA second doses were significantly more effective than mRNA only vaccines. Risk of infection largely vanished during the time period 4-6 months after the second vaccine dose, but rose markedly thereafter. Interpretation A case-cohort design provided an efficient means to identify strong protective effects associated with SARS-CoV-2 vaccination, particularly after the second dose of vaccine. However, this effect appeared to wane once more than 6 months had elapsed since vaccination. Heterologous vaccination and extended dosing intervals improved the durability of immune response.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".