Predicting sensitivity of repeated environmental sampling for Mycobacterium avium subsp. paratuberculosis in dairy herds using a Bayesian latent class model
Bibliographic record
Abstract
Between-herd transmission of Mycobacterium avium subsp. paratuberculosis (MAP) by subclinically infected cattle is an important risk which can hamper effective control of paratuberculosis. Knowledge of herd status would substantially reduce this risk; MAP positive farms can be detected with environmental sampling. The objective of this study was to compare cumulative sensitivities of annual environmental sampling with two or four samples per sampling event without knowledge of true herd status and to calculate the number of sampling events to achieve a cumulative sensitivity of at least 0.9. Data from three repeated sampling events in two study populations, one with 55 herds (two samples/event) and another with 30 herds (four samples/event) including test results, herd and sample characteristics and prior prevalence estimates, were derived from the Alberta Johne's Disease Initiative (Alberta, Canada). A recursive Bayesian latent class model was used to predict the cumulative sensitivity of repeated environmental sampling events. A sampling scheme with four samples per sampling event had a higher cumulative sensitivity than an alternative scheme with two samples. To achieve a cumulative sensitivity of at least 0.9 with 95% probability, eight sampling events with two environmental samples per set, or four sampling events with four samples per set were required. Further model assessment demonstrated that these results can only be generalized to cattle populations with a similar within-herd prevalence to those studied here (approximately 0.08). Nonetheless, these results could help predict herd-level prevalence in cattle populations after environmental testing and provide information regarding the uncertainty behind status estimates for herds repeatedly tested using environmental samples.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".