Transmission dynamics and changing epidemiology of West Nile virus
Bibliographic record
Abstract
West Nile virus (WNV) is a flavivirus that is maintained in a bird-mosquito transmission cycle. Humans, horses and other non-avian vertebrates are usually incidental hosts, but evidence is accumulating that this might not always be the case. Historically, WNV has been associated with asymptomatic infections and sporadic disease outbreaks in humans and horses in Africa, Europe, Asia and Australia. However, since 1994, the virus has caused frequent outbreaks of severe neuroinvasive disease in humans and horses in Europe and the Mediterranean Basin. In 1999, WNV underwent a dramatic expansion of its geographic range, and was reported for the first time in the Western Hemisphere during an outbreak of human and equine encephalitis in New York City. The outbreak was accompanied by extensive and unprecedented avian mortality. Since then, WNV has dispersed across the Western Hemisphere and is now found throughout the USA, Canada, Mexico and the Caribbean, and parts of Central and South America. WNV has been responsible for >27,000 human cases, >25,000 equine cases and hundreds of thousands of avian deaths in the USA but, surprisingly, there have been only sparse reports of WNV disease in vertebrates in the Caribbean and Latin America. This review summarizes our current understanding of WNV with particular emphasis on its transmission dynamics and changing epidemiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".