Strengthening Vaccine Regulation: Insights from COVID-19 Vaccines, Best Practices, and Lessons for Future Public Health Emergencies
Bibliographic record
Abstract
Background: The COVID-19 pandemic necessitated immediate regulatory vaccine approvals to facilitate timely global access. The prevailing differences in economies and resources and the varying maturity of the regulatory systems worldwide resulted in different levels of capacity to ensure vaccine quality, safety, and efficacy. In addition to the Emergency Use Authorization or equivalent by some advanced regulatory agencies, the WHO issued Emergency Use Listings (EULs), among other tools, to streamline and expedite regulatory approvals globally. This study aimed to assess the regulatory strategies and best practices adopted during the COVID-19 vaccine approvals and gather lessons for future emergency preparedness. Methods: A mixed-method approach employing qualitative desk reviews and a cross-sectional study collected data from 194 national regulatory authorities (NRAs) across all WHO regions. Results: Three main approval processes were identified: procurement-driven, reliance-based, and independent evaluations. Wealthier countries with more mature regulatory systems were found to spend a longer time issuing approvals, primarily due to being the initial assessors of the vaccines’ quality, safety, and efficacy. Furthermore, various regulatory flexibilities and best practices centered around regulatory reliance, rolling reviews, fast-tracking reviews, and employing digital tools were identified. Notably, the WHO’s EULs were essential in facilitating the timely approval of vaccines globally, including in low- and middle-income countries. Conclusions: The findings suggest a significant turn in vaccine regulation theories and practice, emphasizing balancing speed with scientific validity. This necessitates the creation of thorough provisions for emergency preparedness, regulatory reliance, and administrative flexibility in regulatory practices worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".