Efficacy and potential human and economic impact of a broadly protective betacoronavirus vaccine
Bibliographic record
Abstract
Abstract Within two decades, three different betacoronaviruses (β-CoV) have spilled over from animals to humans causing high consequence diseases, the most recent causing the COVID-19 pandemic. Using computational antigen design technology, we developed a single multivalent vaccine candidate capable of broadly neutralising diverse animal and human β-CoVs. We then demonstrated that this single vaccine could protect mice from lethal infection against the three genetically distinct human SARS-CoV, MERS-CoV and SARS-CoV-2 viruses. Epidemiological modelling revealed that a single broadly protective β-CoV vaccine (BPBV) of moderate efficacy (50% against severe disease) could have averted a significant fraction of deaths during the SARS-CoV-2 pandemic, depending on the degree of clinical preparedness established pre-pandemic. Deaths averted by the BPBV varied in our simulations, ranging from 47% if the BPBV had been clinically evaluated through to a phase 2 trail, and ready for vaccine efficacy trials early during an initial outbreak, to 13% if phase 1 clinical testing of such a BPBV had not yet been initiated. Taken together we provide pre-clinical evidence of robust efficacy of a single BPBV vaccine candidate and modelling data demonstrating the significant positive impact on human health and significant economic benefit to national economies which would be achieved by clinically advancing and stockpiling a Phase 3 ready BPBV vaccine in the event of new virus outbreaks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".