Within-herd mathematical modeling of Mycobacterium avium subspecies paratuberculosis to assess the effectiveness of alternative intervention methods
Bibliographic record
Abstract
Johne's disease (JD) in cattle is caused by Mycobacterium avium subspecies paratuberculosis (MAP) and is characterized by chronic, progressive enteritis that can lead to substantial weight loss, severe diarrhea, and eventual death. Economic losses due to JD are primarily driven by reduced milk production in subclinical and clinically infected cows, but also include reduced value when sold to slaughter, and costs associated with premature culling. Controlling the transmission of JD within a dairy herd can be achieved through proactive calf management practices and reactive test-based culling. While effective, test-and-cull interventions have the potential to result in net economic losses, particularly when the intervention includes culling of low-shedding cattle. Proactive calf management practices have been observed to be effective at controlling within-herd JD prevalence. However, assessing the magnitude of effect of interventions in observational and experimental studies can be difficult due to the pathogenesis of MAP and may take many years of data to provide meaningful results. The limitations of studying JD in nature presents an opportunity to use mathematical modelling techniques to assess the effectiveness of various interventions on the simulated within-herd disease dynamics of JD. The objectives of this study were to build a within-herd compartmental disease model of JD and assess the effectiveness of three interventions: 1) strategic insemination of test-positive low-shedding adult cattle to preferentially breed beef calves, 2) using separate calving areas for low- and high-shedding dams, and 3) test-based culling of low- and high-shedding cows. Model outcomes were compared to a base case model (i.e., no interventions) under four endemic within-herd prevalences. In general, simulations of test-based culling performed best at reducing long-term within-herd prevalence of JD. Strategic insemination and separate calving area interventions were both effective and performed similarly to one another, but even when combined were not as effective as test-and-cull alone. Finally, the results from the separate calving area intervention model suggest that increased dam-calf contact time would not result in a substantial increased within-herd prevalence. Given that some of the modelled populations in this study are very small and prevalence is very low, further work is needed to assess these interventions using discrete, stochastic methods, which may result in different outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".