A scenario model to support freedom from African swine fever virus in Western Canada populated with data from Canada West Swine Health Intelligence Network and CanSpotASF
Notice bibliographique
Résumé
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. It enhanced the passive surveillance by adding new surveillance components one by one. The first enhancement was "the risk-based early detection testing (rule-out testing)" where cases with eligible conditions were tested for ASF virus (ASFv). The rule-out testing started in 2020 and we have over 3 years data in the Canada West Swine Health Intelligence Network (CWSHIN) data repository on ASFv rule-out testing. The objectives were to develop and assess a scenario tree model (STM) for ASFv freedom to support the claim that commercial swine in Western Canada were free from ASFv; to assess if the CWSHIN repository data may be useful in the STM; and to assess if the rule-out testing had contributed to more evidence of freedom from ASFv than we would have had with passive surveillance alone. To compare and assess different scenarios we used the model's ability to accumulate evidence of freedom from the July quarter in 2020 to the July quarter in 2023 (13 quarters). We concluded that a method to assess the ability of a scenario and model to accumulate evidence of freedom, based on increasing probability of freedom that approached an equilibrium and a system sensitivity of at least 0.2-0.3, was useful. The CWSHIN data repository was critical to populate the model because it included both number of clinical assessments by herd veterinarians (from quarterly surveys); routine diagnostic data (pathology examinations) and ASF testing results from laboratories. The take home message was that the probability that a herd veterinarian will report a suspicion of ASF to the Canadian Food Inspection Agency (CFIA) (seVet) affected the outcomes of the STM. If the probability that a herd veterinarian reports to CFIA is low (seVet=0.01), then pathology examination and CanSpotASF will improve the STM outcomes. If the probability that a herd veterinarian reports to CFIA is high (seVet is 0.7 or higher), then there is no need for additional pathology and CanSpotASF testing. We expect the actual probability of reporting and subsequent value of additional pathology and testing to between these two scenarios.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».