title: Demonstrating freedom from disease using multiple complex data sources 1: a new methodology based on scenario trees.
abstract: Current methods to demonstrate zone or country freedom from disease are based on either quantitative analysis of the results of structured representative surveys, or qualitative assessments of multiple sources of evidence (including complex non-representative sources). This paper presents a methodology for objective quantitative analysis of multiple complex data sources to support claims of freedom from disease. Stochastic scenario tree models are used to describe each component of a surveillance system (SSC), and used to estimate the sensitivity of each SSC. The process of building and analysing the models is described, as well as techniques to take into account any lack of independence between units at different levels within a SSC. The combination of sensitivity estimates from multiple SSCs into a single estimate for the entire surveillance system is also considered, again taking into account lack of independence between components. A sensitivity ratio is used to compare different components of a surveillance system. Finally, calculation of the probability of country freedom from the estimated sensitivity of the surveillance system is illustrated, incorporating the use and valuation of historical surveillance evidence.
authors.affiliation: Department of Agriculture and Food, P.O. 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: M
authors.initials: M
authors.lastname: Greiner
mesh_terms: D000820:Animal Diseases / Q000453:epidemiology*; D000818:Animals; D003663:Decision Trees; D004812:Epidemiologic Methods; D008433:Mathematics; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance / Q000662:veterinary*; D013269:Stochastic Processes