Evaluating COVID-19 Vaccine Efficacy Using Kaplan–Meier Survival Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Analyses of COVID-19 vaccines have become a forefront of pandemic-related research, as jurisdictions around the world encourage vaccinations as the most assured method to curtail the need for stringent public health measures. Kaplan–Meier models, a form of “survival analysis”, provide a statistical approach to improve the understanding of time-to-event probabilities of occurrence. In applications of epidemiology and the study of vaccines, survival analyses can be implemented to quantify the probability of testing positive for SARS-CoV-2, given a population’s vaccination status. In this study, a large proportion of Ontario COVID-19 testing data is used to derive Kaplan–Meier probability curves for individuals who received two doses of a vaccine during a period of peak Delta variant cases, and again for those receiving three doses during a peak time of the Omicron variant. Data consisting of 614,470 individuals with two doses of a COVID-19 vaccine, and 49,551 individuals with three-doses of vaccine, show that recipients of the Moderna vaccine are slightly less likely to test positive for the virus in a 38-day period following their last vaccination than recipients of the Pfizer vaccine, although the difference between the two is marginal in most age groups. This result is largely consistent for two doses of the vaccines during a Delta variant period, as well as an Omicron variant period. The evaluated probabilities of testing positive align with the publicly reported vaccine efficacies of the mRNA vaccines, supporting the resolution that Kaplan–Meier methods in determining vaccine benefits are a justifiable and useful approach in addressing vaccine-related concerns in the COVID-19 landscape.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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 it