Victor J del Rio Vilas | PubMed Health

PubMed2007

Diagnosing scrapie in sheep: a classification experiment.

Scrapie is a neuro-degenerative disease in small ruminants. A data set of 3113 records of sheep reported to the Scrapie Notifications Database in Great Britain has been studied. Clinical signs were recorded as present/absent in each animal by veterinary officials (VO) and a post-mortem diagnosis was made. In an attempt to detect healthy animals within the set of suspects using only the clinical signs, 18 classification methods were applied ranging from simple linear classifiers to classifier ensembles such as Bagging, AdaBoost and Random Forests. The results suggest that the clinical classification by the VO was adequate as no further differentiation within the set of suspects was feasible.

Animals↗

PubMed2005

A case study of capture-recapture methodology using scrapie surveillance data in Great Britain.

We applied capture-recapture methodology (CRC) to data from three surveillance sources (statutory notification, abattoir survey (AS) and fallen stock (FS) survey) to estimate the number of holdings infected with scrapie in Great Britain and to assess the sensitivity of the surveillance network. Between January 1, 2002 and March 31, 2003, 144 holdings were identified by the three sources. Using CRC modelling techniques, we estimated a minimum lower bound for the total number of holdings infected as 642. A biologically plausible positive dependence between the statutory reporting and the fallen stock survey was found statistically significant. The sensitivity of the three sources combined was very low. The integration of the three overlapping sources provided a better understanding of the interactions within the surveillance network. However, the scarcity of the data and reduced overlapping among sources only allowed for very cautious inferences to be drawn about the true proportion of scrapie affected holdings in the national population. Future surveys and surveillance activities should be planned such that the resulting data can be used more effectively as part of CRC modelling approaches.

Agriculture↗