title: Demonstrating freedom from disease using multiple complex data sources 2: case study--classical swine fever in Denmark.
abstract: A method for quantitative evaluation of surveillance for disease freedom has been presented in the accompanying paper (Martin et al., 2007). This paper presents an application of the methods, using as an example surveillance for classical swine fever (CSF) in Denmark in 2005. A scenario tree model is presented for the abattoir-based serology component of the Danish CSF surveillance system, in which blood samples are collected in an ad hoc abattoir sampling process, from adult pigs originating in breeding herds in Denmark. The model incorporates effects of targeting (differential risk of seropositivity) associated with age and location (county), and disease clustering within herds. A surveillance time period of one month was used in the analysis. Records for the year 2005 were analysed, representing 25,332 samples from 3528 herds; all were negative for CSF-specific antibodies. Design prevalences of 0.1-1% of herds and 5% of animals within an infected herd were used. The estimated mean surveillance system component (SSC) sensitivities (probability that the SSC would give a positive outcome given the animals processed and that the country is infected at the design prevalences) per month were 0.18, 0.63 and 0.86, for among-herd design prevalences of 0.001, 0.005 and 0.01. The probabilities that the population was free from CSF at each of these design prevalences, after a year of accumulated negative surveillance data, were 0.91, 1.00 and 1.00. Targeting adults and herds from South Jutland was estimated to give approximately 1.9, 1.6 and 1.4 times the surveillance sensitivity of a proportionally representative sampling program for these three among-herd design prevalences.
authors.affiliation: Department of Agriculture and Food, PO Box 1231, Bunbury, Western Australia 6231, Australia. tmartin@agric.wa.gov.au
authors.forename: P A J
authors.initials: PA
authors.lastname: Martin
authors.forename: A R
authors.initials: AR
authors.lastname: Cameron
authors.forename: K
authors.initials: K
authors.lastname: Barfod
authors.forename: E S G
authors.initials: ES
authors.lastname: Sergeant
authors.forename: M
authors.initials: M
authors.lastname: Greiner
mesh_terms: D000003:Abattoirs; D000367:Age Factors; D000818:Animals; D000914:Antibodies, Viral / Q000097:blood*; D006691:Classical Swine Fever / Q000453:epidemiology*; D006692:Classical Swine Fever Virus / Q000276:immunology; D016000:Cluster Analysis; D003663:Decision Trees; D003718:Denmark / Q000453:epidemiology; D004797:Enzyme-Linked Immunosorbent Assay / Q000379:methods / Q000662:veterinary; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance / Q000662:veterinary*; D016036:Seroepidemiologic Studies; D013269:Stochastic Processes; D013552:Swine
chemical_list: D000914:Antibodies, Viral