Lessons from CanSpotASF: Moving towards risk-based African Swine Fever surveillance with rule-out testing in Western Canada
Bibliographic record
Abstract
African swine fewer (ASF) is a serious disease present in Africa, Eurasia, and the Caribbean but not in continental North America. CanSpotASF describes the ASF surveillance in Canada as a phased in approach. The first enhancement to the passive surveillance was the risk-based early detection testing (rule-out testing) where eligible cases were tested for ASF virus (ASFv). The objective was to describe how the eligibility criteria were applied to cases in western Canada. In particular, to assess if cases tested for ASFv had eligible conditions and if pathology cases with eligible conditions were tested for ASFv based on the data collated by Canada West Swine Health Intelligence Network (CWSHIN) from British Columbia, Alberta, Saskatchewan, and Manitoba. The study period was August 2020 to December 2022 and the data included two study laboratories. We found that over 90% of cases tested for ASFv had eligible conditions as defined in CanSpotASF. The eligibility criteria were applied at three stages of the disease investigation process: 1) the clinical presentation in the herd; 2) at the initial laboratory assessment; and 3) the final pathology diagnosis. At the two study laboratories the proportion of all submitted cases (culture, serology, PCR, pathology) tested for ASFv was very low 1%. However, in the pathology cases specifically targeted in CanSpotASF, and the proportion of tested cases was 12%. In addition, for eligible pathology cases (eligible diagnosis or test) the proportion tested was higher 15%. These results indicated that CanSpotASF targeted herds with submissions for pathological examination and to some degree eligible conditions which would be herds with health issues (known or unknown). We interpret this as a first step towards risk-based surveillance with health as the defining factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".