In search of a pan-coronavirus vaccine: next-generation vaccine design and immune mechanisms
Bibliographic record
Abstract
Members of the coronaviridae family are endemic to human populations and have caused several epidemics and pandemics in recent history. In this review, we will discuss the feasibility of and progress toward the ultimate goal of creating a pan-coronavirus vaccine that can protect against infection and disease by all members of the coronavirus family. We will detail the unmet clinical need associated with the continued transmission of SARS-CoV-2, MERS-CoV and the four seasonal coronaviruses (HCoV-OC43, NL63, HKU1 and 229E) in humans and the potential for future zoonotic coronaviruses. We will highlight how first-generation SARS-CoV-2 vaccines and natural history studies have greatly increased our understanding of effective antiviral immunity to coronaviruses and have informed next-generation vaccine design. We will then consider the ideal properties of a pan-coronavirus vaccine and propose a blueprint for the type of immunity that may offer cross-protection. Finally, we will describe a subset of the diverse technologies and novel approaches being pursued with the goal of developing broadly or universally protective vaccines for coronaviruses.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".