COVID-19 vaccine effectiveness against SARS-CoV-2 infection and long-term symptoms
Bibliographic record
Abstract
Evaluation of COVID-19 vaccines post-introduction is of high importance to inform future COVID-19 vaccination policy. Shortly after the introduction of the first vaccines in the Netherlands, the VAccination Study COvid-19 (VASCO) was established for this purpose. This dissertation draws on this large observational study and focuses on the effectiveness of COVID-19 vaccination against SARS-CoV-2 infection and long-term symptoms. The first part examines the effectiveness of the primary vaccination series and various booster vaccinations against infection. It considers protection against different virus variants as well as changes in effectiveness over time. The results show that the primary vaccination series initially provided high protection against infection, but that this declined with the emergence of the Omicron variant. Booster vaccinations increased protection again, although the protection decreased within six months. The second part of the dissertation focuses on long-term symptoms following infection, namely fatigue and post-COVID condition. The analyses show that fatigue after an Omicron infection was mostly limited to the acute phase, whereas infections caused by the Delta variant were associated with more prolonged fatigue. Furthermore, the risk of post-COVID condition was low in this study, with only small differences between vaccinated and unvaccinated participants. In the discussion, methodological challenges inherent to observational research are addressed. The dissertation also provides recommendations for future research and discusses considerations for COVID-19 vaccination policy.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".