How Safe is That Shrimp? @RISK Weighs Health Risks of Seafood after Deepwater Horizon Spill
Southeast Louisiana is home to a large population of Vietnamese Americans who rely heavily on the shrimp caught in the Gulf for their livelihood and as a food source. When the Deepwater Horizon oil spill happened in in April 2010, this group was particularly concerned. While the U.S. Food and Drug Administration (FDA) conducted risk assessments on seafood contaminant levels and health risks, the Vietnamese community worried that the risk assessment conducted by the FDA did not accurately take into account their much higher levels of shrimp consumption and lower-than-national-average body weight. They were also concerned that the FDA did not source specimens from the key areas where they commonly fished for shrimp.
At the request of a prominent Vietnamese community organization, Dr. Jeffrey Wickliffe, Associate Professor of Global Environmental Health Sciences at the Tulane University School of Public Health and Tropical Medicine, conducted a targeted health risk analysis on the Vietnamese shrimping community and their potential for heightened risk from the Deepwater Horizon spill. He and his colleagues collected key data, including concentrations of polycyclic aromatic hydrocarbons (PAHs) in shrimp; daily shrimp intake rates of surveyed Vietnamese community members; assumed durations of exposure that people had to PAHs; individual’s self-reported body weights; and averaging times (the time used to average out the dose of PAHs). The model for these inputs was simulated 10,000 times, and a sensitivity analysis was conducted to determine the most influential parameters. The analysis revealed that the concentration of chemicals and the daily shrimp intake rate were the most influential in determining risk levels, however, “The study showed that the shrimp were really low in PAHs overall,” says Dr. Wickliffe. “In fact, the testing did not actually detect any of the known carcinogens.”
Nonetheless, to be extremely conservative in their analysis, Dr. Wickliffe and his team modeled health risks using even more cautious assumptions about the presence and carcinogenicity of the PAHs. It was only under the very most conservative approach that excessive health risks (> 1 in 10,000 at the 99th percentile) were seen, and even then, they appeared only in the extreme upper tail of the modeled risk distribution. While these results are reassuring in terms of the overall health risk posed to the Vietnamese American shrimping community, Wickliffe’s team cautions that this approach is not currently tenable for policy-based chemical risk assessment because of the dearth of knowledge regarding the toxicology of these modeled compounds.
Dr. Wickliffe uses @RISK in all the courses he teaches. “Probabilistic analysis is where the regulatory agencies are going with risk assessments,” says Dr. Wickliffe. “So for students who are getting a degree in public health and environmental health sciences, this is the kind of training they need—they need to know how to conduct this kind of risk assessment.”
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Excerpted from Financial Models Using Simulation and
Optimization 1, by Wayne Winston, published by Palisade Corporation
Suppose we are a long distance phone carrier (letʹs use the name BTT). Clearly, Most people value the first few minutes of long distance calls they purchase each month more than they value later minutes. How can we take advantage of this
when pricing long distance calls? If we charge a single price for each long distance
minute, then few people are going to make lots of long distance phone calls. This is
because consumers attach less value to each additional minute phoned during a
month. Alternatively, however, we can try the two-part tariff approach. The two-part
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Mode of Continuous Data
Q: I'm displaying the mode of my data, and it seems to be very far from the tallest bar in the histogram. What is wrong? How does @RISK compute the mode of a continuous distribution?
The traditional definition of the mode of discrete data is the most frequently occurring value of the variable. An analogous definition works well for most theoretical continuous distributions: you have a smooth probability density curve pdf(x), and the mode is simply the value of x where the pdf(x) is highest. But for continuous data in simulation results, it's unusual to have identical data points, and therefore a new definition is needed.
Different authorities use different definitions and therefore find different modes; the way you bin the data can also change which value you call the mode. @RISK uses the method of dividing the n simulated data points into k bins, each with n/k consecutive data points, and then look at the widths of the bins. The narrowest bin is the one where the points are clustered closest together, which means that the probability density is greatest in that bin, so the mode must be there.
» Read the complete answer here
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Why do IT projects fail?
by Nick Martindale, Economia Magazine, January 5, 2015
High-profile failures have cast doubt on the ability of both the public and private sector to incorporate cutting-edge IT. Author Nick Martindale argues it’s often the business plan, not the technology, that needs a reboot. In the discussion, Laurits Søgaard Nielsen, CEO of Virtual Reporting and @RISK user and customer, talks about his work with the Danish government to deploy a business case modeler package to help it make objective decisions around whether or not to sign off new programs.
» Read the full article here
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Mode of Continuous Data
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