Premium
Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems
Author(s) -
Hood G. M.,
Barry S. C.,
Martin P. A. J.
Publication year - 2009
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2009.01323.x
Subject(s) - bayesian network , exposition (narrative) , representation (politics) , sensitivity (control systems) , computer science , tree (set theory) , bayesian probability , component (thermodynamics) , degrees of freedom (physics and chemistry) , theoretical computer science , matrix (chemical analysis) , conditional probability , artificial intelligence , machine learning , data mining , mathematics , engineering , statistics , art , mathematical analysis , physics , materials science , literature , quantum mechanics , electronic engineering , politics , political science , law , composite material , thermodynamics
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.