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On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment
Author(s) -
Hamed Maged M.,
Bedient Philip B.
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.tb00848.x
Subject(s) - percentile , statistics , probability distribution , random variable , cumulative distribution function , mathematics , sensitivity (control systems) , log normal distribution , normal distribution , probabilistic logic , risk assessment , joint probability distribution , coverage probability , probabilistic risk assessment , probability density function , econometrics , computer science , confidence interval , engineering , computer security , electronic engineering
A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first‐order reliability method (FORM) (1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. (4) on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. (4) The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. 4 than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.