Resurgence of Interest in Eastern Equine Encephalitis Virus Vaccine Development
Bibliographic record
Abstract
Eastern equine encephalitis virus (EEEV; Family Togaviridae), is an endemic pathogen first isolated in 1933 with distribution primarily in the eastern US and Canada. The virus has caused periodic outbreaks in both humans and equines along the eastern seaboard and through the southern coastal states. While the outbreaks caused by EEEV have been sporadic and varied geographically since the discovery of the virus, it has continued to expand its range moving into the Midwest states as well. Additionally, one of the largest outbreaks was recorded in 2019 prompting concerns that outbreaks were becoming larger and more frequent. Because the virus can cause serious disease and because it is transmissible by both mosquitoes and aerosol, there has been renewed interest in identifying potential options for vaccines. Currently, there are no licensed vaccines and control relies completely on the use of personal protective measures and integrated vector control which have limited effectiveness for the EEEV vectors. Several vaccine candidates are currently being developed; this review will describe the multiple options under consideration for future development and assess their relative advantages and disadvantages.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".