Vaccines for emerging pathogens: prospects for licensure
Bibliographic record
Abstract
Globally, there are a number of emerging pathogens. For most, there are no licensed vaccines available for human use, although there is ongoing research and development. However, given the extensive and increasing list of emerging pathogens and the investment required to bring vaccines into clinical use, the task is huge. Overlaid on this task is the risk of anti-microbial resistance (AMR) acquisition by micro-organisms which can endow a relatively harmless organism with pathogenic potential. Furthermore, climate change also introduces a challenge by causing some of the insect vectors and environmental conditions prevalent in tropical regions to begin to spread out from these traditional areas, thus increasing the risk of migration of zoonotic disease. Vaccination provides a defence against these emerging pathogens. However, vaccines for pathogens which cause severe, but occasional, disease outbreaks in endemic pockets have suffered from a lack of commercial incentive for development to a clinical standard, encompassing Phase III clinical trials for efficacy. An alternative is to develop such vaccines to request US Emergency Use Authorization (EUA), or equivalent status in the United States, Canada and the European Union, making use of a considerable number of regulatory mechanisms that are available prior to licensing. This review covers the status of vaccine development for some of the emerging pathogens, the hurdles that need to be overcome to achieve EUA or an equivalent regional or national status and how these considerations may impact vaccine development for the future, such that a more comprehensive stockpile of promising vaccines can be achieved.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".