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Probabilistic Inversion in Priority Setting of Emerging Zoonoses

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  • Dorota Kurowicka
  • Catalin Bucura
  • Roger Cooke
  • Arie Havelaar

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.

Suggested Citation

  • Dorota Kurowicka & Catalin Bucura & Roger Cooke & Arie Havelaar, 2010. "Probabilistic Inversion in Priority Setting of Emerging Zoonoses," Risk Analysis, John Wiley & Sons, vol. 30(5), pages 715-723, May.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:5:p:715-723
    DOI: 10.1111/j.1539-6924.2010.01378.x
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    References listed on IDEAS

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    1. Du, C. & Kurowicka, D. & Cooke, R.M., 2006. "Techniques for generic probabilistic inversion," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1164-1187, March.
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    Cited by:

    1. R. E. J. Neslo & W. Oei & M. P. Janssen, 2017. "Insight into “Calculated Risk”: An Application to the Prioritization of Emerging Infectious Diseases for Blood Transfusion Safety," Risk Analysis, John Wiley & Sons, vol. 37(9), pages 1783-1795, September.
    2. Hong Yao & Xin Qian & Hong Yin & Hailong Gao & Yulei Wang, 2015. "Regional Risk Assessment for Point Source Pollution Based on a Water Quality Model of the Taipu River, China," Risk Analysis, John Wiley & Sons, vol. 35(2), pages 265-277, February.

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