A Snapshot of the Global Race for Vaccines Targeting SARS-CoV-2 and the COVID-19 Pandemic
Bibliographic record
Abstract
A novel coronavirus SARS-CoV-2 causing Coronavirus disease 2019 (COVID-19) has entered the human population and has spread rapidly around the world in the first half of 2020 causing a global pandemic. The virus uses its spike glycoprotein receptor-binding domain to interact with host cell angiotensin-converting enzyme 2 (ACE2) sites to initiate a cascade of events that culminate in severe acute respiratory syndrome in some individuals. In efforts to curtail viral spread, authorities initiated far-reaching lockdowns that have disrupted global economies. The scientific and medical communities are mounting serious efforts to limit this pandemic and subsequent waves of viral spread by developing preventative vaccines and repurposing existing drugs as potential therapies. In this review, we focus on the latest developments in COVID-19 vaccine development, including results of the first Phase I clinical trials and describe a number of the early candidates that are emerging in the field. We seek to provide a balanced coverage of the seven main platforms used in vaccine development that will lead to a desired target product profile for the "ideal" vaccine. Using tales of past vaccine discovery efforts that have taken many years or that have failed, we temper over exuberant enthusiasm with cautious optimism that the global medical community will reach the elusive target to treat COVID-19 and end the pandemic.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".