Comparative effectiveness of one versus two doses of COVID-19 vaccines in Qatar: Evidence of converging protection over time
Bibliographic record
Abstract
BACKGROUND: Supply constraints during the coronavirus disease 2019 (COVID-19) pandemic led to vaccination strategies that prioritized first-dose coverage. To evaluate the merit of this approach, this study compared the development of protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe COVID-19 following a single dose versus two doses across three widely used vaccine platforms. METHODS: National, matched, test-negative case-control analyses were conducted in Qatar between December 1, 2020, and December 18, 2021, to evaluate vaccine effectiveness. The one-dose analysis included 227,309 cases and 4,170,786 controls; the two-dose analysis included 234,314 cases and 6,445,858 controls. RESULTS: For BNT162b2, single-dose effectiveness against infection increased steadily from 9.9 % (95 % CI: 6.7-13.0 %) in the first two weeks post-vaccination to 71.5 % (95 % CI: 45.5-85.1 %) by month 3, closely approaching the 74.5 % (95 % CI: 72.9-76.0 %) effectiveness observed after the two-dose primary series. Similar trends were observed for mRNA-1273 and ChAdOx1 nCoV-19, with mRNA-1273 reaching two-dose levels of effectiveness as early as month 2. In contrast to the gradual buildup of protection against infection, single-dose effectiveness against severe, critical, or fatal COVID-19 increased rapidly for all three vaccines, exceeding 85 % by day 21 and closely matching the protection achieved after two doses. CONCLUSION: A single COVID-19 vaccine dose provides rapid, strong protection against severe outcomes, supporting first-dose prioritization during supply constraints. The slower development of protection against infection highlights the second dose's role in accelerating the immune response. Antigen dose appears to influence the speed of protection buildup.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".