Probabilistic inversion in priority setting of emerging zoonoses

Risk Anal. 2010 May;30(5):715-23. doi: 10.1111/j.1539-6924.2010.01378.x. Epub 2010 Mar 15.

Abstract

This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi-criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a distribution over attribute weights is found that optimally reproduces the expert rankings. This model is validated in three ways. First, consistency of expert rankings is checked, second, a complete model fitted using all expert data is found to adequately reproduce observed expert rankings, and third, the model is fitted to subsets of the expert data and used to predict rankings in out-of-sample expert data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Health Priorities*
  • Humans
  • Models, Theoretical
  • Probability*
  • Reproducibility of Results
  • Zoonoses*