Early warning signal for river‐borne diseases with almost no data
Bibliographic record
Abstract
Abstract Effective management of emerging river‐borne diseases requires early prediction of pathogen spatial distributions. However, data on pathogen locations are notoriously rare in the beginning of disease outbreaks and insufficient to feed existing predictive models. We extended hidden Markov models (HMM) to exploit detailed spatially structured data on environmental covariates that are readily available for many aquatic systems, augmented by arbitrarily sparse spatial data on actual pathogen occurrence, to predict pathogen distribution over large geographical scales. The extended HMM predicted the spread of whirling disease in the Oldman River, Canada, with 0.7 area under the receiver operating characteristic curve (AUC) in the absence of any disease test result, provided that the status of a single pixel can be estimated correctly. The AUC increased with the number of used test results, until it reached 0.9 when 100 test results were used. The model has the potential to provide early warning signals for emerging diseases prior to, or at the early stages of, their emergence in a river.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".