Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

SRA 2013 Annual Meeting Abstracts
T2-D.3 Taft, SC*; Hines, SA; Chappie, DJ; Janke, RJ; Lindquist,
HA; Ernst, HS; U.S. Environmental Protection Agency; Battelle
Memorial Institute; taft.sarah@epa.gov
Assessment of relative potential for Legionella species
inhalation exposure from common water uses
The intentional or accidental introduction of microbial
contaminants such as Legionella into a drinking water system
could pose a health hazard to water customers. The Legionella
species have been identified as important waterborne
pathogens in terms of disease morbidity and mortality. A
preliminary exposure assessment of Legionella spp. drinking
water contamination was conducted to better understand
potential inhalation exposure pathways, develop a means of
prioritizing exposure pathways for further assessment, estimate
potential inhalation exposure doses, and identify critical
knowledge gaps for further study. Potentially complete
exposure pathways were compiled, and a screening level
exposure assessment was conducted for pathways where
inhalation doses could be quantitatively estimated.
Considerable variability in the calculated exposure doses was
identified between the exposure pathways, with the doses
differing by over five orders of magnitude in each of the
evaluated exposure scenarios. The exposure pathways that have
been epidemiologically associated with legionellosis
transmission (ultrasonic and cool mist humidifiers) were
assessed to have higher estimated inhalation doses than
pathways where epidemiological evidence of transmission has
been less strong (faucet and shower) or absent (toilets and
therapy pool). This presentation will describe the complex
assessment design and methodology to demonstrate
applicability of these assessments to a wide range of microbial
contaminants. While ingestion exposures of contaminated
drinking water historically have been examined, there are still
major gaps in the understanding of inhalation and dermal
exposures of aerosolized pathogens during common uses of
water.
P.56 Takeshita, J*; Gamo, M; National Institute of Advanced
Industrial Science and Technology (AIST);
jun-takeshita@aist.go.jp
Proposing a framework of QAAR approaches for
predicting the toxicity of chemical substances: A case
study on predicting and extrapolating the missing NOEL
values
We propose a Quantitative Activity–Activity Relationship
(QAAR) model to predict the unknown toxicity values of
chemical substances in animal testing data from the existing
acute oral toxicity and 28 days repeated dose toxicity studies.
In view of the global movement to reduce animal testing to
assesse and manage the chemical risk, OECD has been saying
the aggressive use of statistical methods for predicting the
toxicity of chemical substances. As one of the most popular
statistical methods, there is the Quantitative Structure-Activity
Relationship (QSAR). On the other hand, the Quantitative
Activity-Activity Relationship (QAAR) was introduced to
estimate unknown toxicity values from the relationship between
difference toxicity endpoints. For example, suppose that a
target substance and there exist in vivo data of some endpoints.
When we would like to know the toxicity of every endpoint, we
have been considering that the QAAR works well. The QAAR
enables us to predict any endpoint's toxicity from the existing in
vivo data. When we deal with an existing substance, we may
face the situation like above since there are not a little
literature information on the substance. In this study, we first
develop a QAAR by using covariance structure analysis. Our
model is based on correlations among organ-specific NOEL
values that are included in the training set. The major
advantage of the model is that it enables us to make
estimations with the confidence intervals. Secondly, we predict
the missing NOEL values of the substances for which NOEL
data for some organs but not all on 28 days repeated dose
toxicity studies are available. Finally, we extrapolate every
NOEL values of the substances that have only acute oral
toxicity studies from the LD50 values.
T4-I.2 Talabi, S; Carnegie Mellon Unversity;
mtalabi@andrew.cmu.edu
Improving Nuclear Power Plant Construction Cost
Learning Curves by Implementing Organizational
Learning Tools for Risk Identification and Risk
Assessment
The nuclear industry has been historically plagued with
considerable technology deployment risks, with project cost
and schedule overruns presenting a significant risk to nuclear
plant investors. Although several risk management practices
have been put in place, considerable cost and schedule
excursions have continued to occur in the construction of
recent nuclear power plant projects. No evidence of learning
has been seen based on an analysis of the cost trends for
nuclear power plant construction. This research seeks to
challenge the lack of a demonstrated learning curve in nuclear
power plant construction cost, and to propose that such a
learning curve can be established through the implementation
of organizational learning tools for risk identification and risk
assessment. An analogy is drawn between the nuclear
industry’s development of a learning curve as a response to
safety challenges which were brought to light by the Three-Mile
Island plant accident, and the potential for a similar learning
curve to address construction cost challenges. A method is
developed to use documented risk occurrence trends on a
sample of nuclear steam generator replacement projects to
develop a potential learning curve. We define a learning
coefficient based on risk management performance, and show
that various functional groups supporting the deployment of
steam generator replacement projects have lower rates of cost
overruns as their learning coefficients increase. This trend
demonstrates a potential for learning.
T4-H.1 Tambe, M*; Shieh, E; Univ of Southern California;
tambe@usc.edu
Stackelberg Games in Security Domains: Evaluating
Effectiveness of Real-World Deployments
A Stackelberg game is a game theoretic model that assumes
two players, a leader (defender) and follower (attacker). The
leader plays a strategy first (where the follower is able to
observe the leader’s strategy) and the follower subsequently
decides his own strategy based on the leader’s strategy.
Stackelberg games have been in active use for resource
deployment scheduling systems by law enforcements around
the US. Sites include LAX International Airport (LAX), assisting
the LAX Airport police in scheduling airport entrance
checkpoints and canine patrols of the terminals, the Federal Air
Marshals Service (FAMS) to schedule marshals on international
flights, the United States Coast Guard (USCG) in scheduling
patrols around Ports of Boston, NY/NJ, and LA/LB, the Los
Angeles Sheriff’s Department (LASD) for patrolling Metro
trains, and others. Measuring the effectiveness of the
Stackelberg game-based applications is a difficult problem
since we cannot rely on adversaries to cooperate in evaluating
the models and results, and there is (thankfully) very little data
available about the deterrence of real-world terrorist attacks.
The best available evidence of the Stackelberg game-based
applications’ effectiveness in providing optimum security at
minimum cost includes: (1) computer simulations of
checkpoints and canine patrols, (2) tests against human
subjects, including USC students, an Israeli intelligence unit,
and on the Internet Amazon Turk site, (3) comparative analysis
of predictability of schedules and methodologies before and
after implementation of a Stackelberg strategy, (4) Red Team,
(5) capture rates of guns, drugs, outstanding arrest warrants
and fare evaders, and (6) user testimonials. Even as we
continue evaluations in additional security domains, the body of
answers to “how well do Stackelberg game-based applications
work?” enables policy makers to have confidence in allocating
and scheduling security resources using applications based on
the Stackelberg game model.
December 8-11, 2013 - Baltimore, MD