Ebola Virus Disease Vaccines: Development, Current Perspectives & Challenges
Bibliographic record
Abstract
The global outgoing outbreaks of Ebola virus disease (EVD) in different regions of Sudan, Uganda, and Western Africa have brought into focus the inadequacies and restrictions of pre-designed vaccines for use in the battle against EVD, which has affirmed the urgent need for the development of a systematic protocol to produce Ebola vaccines prior to an outbreak. There are several vaccines available being developed by preclinical trials and human-based clinical trials. The group of vaccines includes virus-like particle-based vaccines, DNA-based vaccines, whole virus recombinant vaccines, incompetent replication originated vaccines, and competent replication vaccines. The limitations and challenges faced in the development of Ebola vaccines are the selection of immunogenic, rapid-responsive, cross-protective immunity-based vaccinations with assurances of prolonged protection. Another issue for the manufacturing and distribution of vaccines involves post authorization, licensing, and surveillance to ensure a vaccine's efficacy towards combating the Ebola outbreak. The current review focuses on the development process, the current perspective on the development of an Ebola vaccine, and future challenges for combatting future emerging Ebola infectious disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".