Approaches to epidemiologic surveillance, diagnosis and prevention of West Nile fever in the Kyiv region
Bibliographic record
Abstract
West Nile virus was first identified in Uganda in 1937 and remains a significant global threat to public health today. This virus is able to adapt to different ecosystems and spread geographically, especially in temperate climate areas of Europe and North America. Currently, cases of West Nile fever are reported in the United States, southern Canada, Mexico, Central and South America and the Caribbean, as well as in Africa, the Middle East, southern Europe, India and Indonesia, etc. West Nile fever has become more relevant in Ukraine, including in the Kyiv region in the last 10 years. According to data from the State Institution "Public Health Center of the Ministry of Health of Ukraine", 99 cases of West Nile fever were registered in Ukraine in 2024, 43 of them among residents of the Kyiv region. The manifestations of the disease vary in severity from mild and asymptomatic forms to fever clinic symptoms and central nervous system damage. However, there is no specific treatment for West Nile fever. Timely detection of West Nile virus disease cases requires improved laboratory diagnostics, national standards, and the development of an appropriate system of surveillance for West Nile fever at the national level. The authors present in this manuscript the results of the analysis of other countries' experience, the presence of a surveillance system in Ukraine, approaches to the laboratory diagnosis of West Nile fever, vectors control of West Nile virus on the example of the Kyiv region.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".