Vaccine Candidates against Coronavirus Infections. Where Does COVID-19 Stand?
Bibliographic record
Abstract
Seven years after the Middle East respiratory syndrome (MERS) outbreak, a new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) made its first appearance in a food market in Wuhan, China, drawing an entirely new course to our lives. As the virus belongs to the same genus of MERS and SARS, researchers have been trying to draw lessons from previous outbreaks to find a potential cure. Although there were five Phase I human vaccine trials against SARS and MERS, the lack of data in humans provided us with limited benchmarks that could help us design a new vaccine for Coronavirus disease 2019 (COVID-19). In this review, we showcase the similarities in structures of virus components between SARS-CoV, MERS-CoV, and SARS-CoV-2 in areas relevant to vaccine design. Using the ClinicalTrials.gov and World Health Organization (WHO) databases, we shed light on the 16 current approved clinical trials worldwide in search for a COVID-19 vaccine. The different vaccine platforms being tested are Bacillus Calmette-Guérin (BCG) vaccines, DNA and RNA-based vaccines, inactivated vaccines, protein subunits, and viral vectors. By thoroughly analyzing different trials and platforms, we also discuss the advantages and disadvantages of using each type of vaccine and how they can contribute to the design of an adequate vaccine for COVID-19. Studying past efforts invested in conducting vaccine trials for MERS and SARS will provide vital insights regarding the best approach to designing an effective vaccine against COVID-19.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".