Effect of COVID-19 vaccination coverage on transmission and mortality during Omicron dominance
Bibliographic record
Abstract
Background: During the coronavirus disease 2019 (COVID-19) pandemic, the emergence of the Omicron SARS-CoV2 variant raised concerns about reduction in vaccine effectiveness due to its higher transmissibility. Thus, using ecologic data, we assessed the population-level impact of COVID-19 vaccination coverage on COVID-19 transmission and mortality, during the period of Omicron dominance globally. Subject and methods: We used a longitudinal dataset of 110 countries over 16 months (January 2022 to April 2023). Applying random-effects regression models, we assessed the effect of monthly full vaccination coverage on the rates of newly confirmed COVID-19 cases and deaths, adjusting for country characteristics. We obtained the data from open-access databases, including the World Health Organization (WHO) COVID-19 Dashboard and the Oxford COVID-19 Government Response Tracker. Results: On average, each 1% point increase in full vaccination coverage was associated with a 1.4% reduction (95% confidence interval (CI): 0.1%–2.8%, p = 0.035) in the rate of new cases and a 5% reduction (95% CI: 3.6%–6.4%, p < 0.001) in the rate of deaths. This protective effect of vaccination was graded across the levels of vaccination coverage: compared to countries with <50% vaccination coverage, those with coverages of 50%–59%, 60%–69%, 70%–79%, and ≥80% had 20.5% (95% CI: –16.4%–45.7%), 53.8% (22.6%–72.5%), 54.3% (15.5%–75.3%), and 69.6% (38.7%–84.9%) lower rates of new cases, respectively, on average. Conclusions: Our findings suggest an important role of vaccination in mitigating the impact of pandemics, particularly despite the emergence of highly transmissible variants like Omicron.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".