Time to establish an international vaccine candidate pool for potential highly infectious respiratory disease: a community’s view
Bibliographic record
Abstract
In counteracting highly infectious and disruptive respiratory diseases such as COVID-19, vaccination remains the primary and safest way to prevent disease, reduce the severity of illness, and save lives. Unfortunately, vaccination is often not the first intervention deployed for a new pandemic, as it takes time to develop and test vaccines, and confirmation of safety requires a period of observation after vaccination to detect potential late-onset vaccine-associated adverse events. In the meantime, nonpharmacologic public health interventions such as mask-wearing and social distancing can provide some degree of protection. As climate change, with its environmental impacts on pathogen evolution and international mobility continue to rise, highly infectious respiratory diseases will likely emerge more frequently and their impact is expected to be substantial. How quickly a safe and efficacious vaccine can be deployed against rising infectious respiratory diseases may be the most important challenge that humanity will face in the near future. While some organizations are engaged in addressing the World Health Organization's "blueprint for priority diseases", the lack of worldwide preparedness, and the uncertainty around universal vaccine availability, remain major concerns. We therefore propose the establishment of an international candidate vaccine pool repository for potential respiratory diseases, supported by multiple stakeholders and countries that contribute facilities, technologies, and other medical and financial resources. The types and categories of candidate vaccines can be determined based on information from previous pandemics and epidemics. Each participant country or region can focus on developing one or a few vaccine types or categories, together covering most if not all possible potential infectious diseases. The safety of these vaccines can be tested using animal models. Information for effective candidates that can be potentially applied to humans will then be shared across all participants. When a new pandemic arises, these pre-selected and tested vaccines can be quickly tested in RCTs for human populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".