Comparing and Contrasting MERS, SARS-CoV, and SARS-CoV-2: Prevention, Transmission, Management, and Vaccine Development
Bibliographic record
Abstract
The COVID-19 pandemic is responsible for an unprecedented disruption to the healthcare systems and economies of countries around the world. Developing novel therapeutics and a vaccine against SARS-CoV-2 requires an understanding of the similarities and differences between the various human coronaviruses with regards to their phylogenic relationships, transmission, and management. Phylogenetic analysis indicates that humans were first infected with SARS-CoV-2 in late 2019 and the virus rapidly spread from the outbreak epicenter in Wuhan, China to various parts of the world. Multiple variants of SARS-CoV-2 have now been identified in particular regions. It is apparent that MERS, SARS-CoV, and SARS-CoV-2 present with several common symptoms including fever, cough, and dyspnea in mild cases, but can also progress to pneumonia and acute respiratory distress syndrome. Understanding the molecular steps leading to SARS-CoV-2 entry into cells and the viral replication cycle can illuminate crucial targets for testing several potential therapeutics. Genomic and structural details of SARS-CoV-2 and previous attempts to generate vaccines against SARS-CoV and MERS have provided vaccine targets to manage future outbreaks more effectively. The coordinated global response against this emerging infectious disease is unique and has helped address the need for urgent therapeutics and vaccines in a remarkably short time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".