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• W2030158920 endingPage "1698" @default.
• W2030158920 startingPage "1686" @default.
• W2030158920 abstract "Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems—such as a combination of a serological survey and systematically collected observations—it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums." @default.
• W2030158920 created "2016-06-24" @default.
• W2030158920 creator A5038749297 @default.
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• W2030158920 date "2009-12-01" @default.
• W2030158920 modified "2023-10-15" @default.
• W2030158920 title "Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems" @default.
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• W2030158920 doi "https://doi.org/10.1111/j.1539-6924.2009.01323.x" @default.
• W2030158920 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19948004" @default.
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