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First‐Order Reliability Analysis of Public Health Risk Assessment
Author(s) -
Hamed Maged M.
Publication year - 1997
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.1539-6924.1997.tb00857.x
Subject(s) - monte carlo method , sensitivity (control systems) , probabilistic risk assessment , random variable , probabilistic logic , parametric statistics , risk assessment , reliability (semiconductor) , statistics , probability distribution , probabilistic analysis of algorithms , standard deviation , computer science , reliability engineering , mathematics , engineering , power (physics) , physics , computer security , quantum mechanics , electronic engineering
This paper demonstrates a new methodology for probabilistic public health risk assessment using the first‐order reliability method. The method provides the probability that incremental lifetime cancer risk exceeds a threshold level, and the probabilistic sensitivity quantifying the relative impact of considering the uncertainty of each random variable on the exceedance probability. The approach is applied to a case study given by Thompson et al. (1) on cancer risk caused by ingestion of benzene‐contaminated soil, and the results are compared to that of the Monte Carlo method. Parametric sensitivity analyses are conducted to assess the sensitivity of the probabilistic event with respect to the distribution parameters of the basic random variables, such as the mean and standard deviation. The technique is a novel approach to probabilistic risk assessment, and can be used in situations when Monte Carlo analysis is computationally expensive, such as when the simulated risk is at the tail of the risk probability distribution.