Within-herd mathematical modeling of Mycobacterium avium subspecies paratuberculosis to assess the effectiveness of alternative intervention methods
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».