Forecasting risk of tick-borne encephalitis (TBE): Using data from wildlife and climate to predict next year's number of human victims
Bibliographic record
Abstract
BACKGROUND: Over the past quarter century, the incidence of tick-borne encephalitis (TBE) has increased in most European nations. However, the number of humans stricken by the disease varies from year to year. A method for predicting major increases and decreases is needed. METHODS: We assembled a 25-y database (1984-2008) of the number of human TBE victims and wildlife and climate data for the Stockholm region of Sweden, and used it to create easy-to-use mathematical models that predict increases and decreases in the number of humans stricken by TBE. RESULTS: Our best model, which uses December precipitation and mink (Neovison vison, formerly Mustela vison) bagging figures, successfully predicted every major increase or decrease in TBE during the past quarter century, with a minimum of false alarms. However, this model was not efficient in predicting small increases and decreases. CONCLUSIONS: Predictions from our models can be used to determine when preventive and adaptive programmes should be implemented. For example, in years when the frequency of TBE in humans is predicted to be high, vector control could be intensified where infested ticks have a higher probability of encountering humans, such as at playgrounds, bathing lakes, barbecue areas and camping facilities. Because our models use only wildlife and climate data, they can be used even when the human population is vaccinated. Another advantage is that because our models employ data from previously-established databases, no additional funding for surveillance is required.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".