COVID-19 vaccines: rapid development, implications, challenges and future prospects
Bibliographic record
Abstract
COVID-19 has affected millions of people and put an unparalleled burden on healthcare systems as well as economies throughout the world. Currently, there is no decisive therapy for COVID-19 or related complications. The only hope to mitigate this pandemic is through vaccines. The COVID-19 vaccines are being developed rapidly, compared to traditional vaccines, and are being approved via Emergency Use Authorization (EUA) worldwide. So far, there are 232 vaccine candidates. One hundred and seventy-two are in preclinical development and 60 in clinical development, of which 9 are approved under EUA by different countries. This includes the United Kingdom (UK), United States of America (USA), Canada, Russia, China, and India. Distributing vaccination to all, with a safe and efficacious vaccine is the leading priority for all nations to combat this COVID-19 pandemic. However, the current accelerated process of COVID-19 vaccine development and EUA has many unanswered questions. In addition, the change in strain of SARS-CoV-2 in UK and South Africa, and its increasing spread across the world have raised more challenges, both for the vaccine developers as well as the governments across the world. In this review, we have discussed the different type of vaccines with examples of COVID-19 vaccines, their rapid development compared to the traditional vaccine, associated challenges, and future prospects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".