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

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SRA 2013 Annual Meeting Abstracts P.4 Convertino, MC*; Liang, SL; University of Minnesota ; matteoc@umn.edu Food Safety? A Supply Chain Matter: Probabilistic Risk Model based on the Agro-Food Trade Network Food safety is a major issue for the worldwide population. Foodborne outbreaks in the USA caused in 2010 a cost of $ 152 billion related to 325,000 hospitalized persons and 5000 deaths due to a foodborne illness. To fight this increasing trend a risk-based system built upon data-driven analysis to inform the efficient targeting of efforts to minimize foodborne risks to the US consumer. Here we propose a model for the assessment of the potential total health risk of food based on the food supply chain (FSC) as a subset of the international agro- food trade network. The number of connected countries, the betweenness centrality of the exporting countries, and the average path length are the supply network variables considered. Considering the safety of each country and the network variables we introduce a global safety index (GSI) for characterizing the riskiness of each country based on local and FSC variables. The intermediary country risk, the food-pathogen health risk, and the company reliability are the second most important factors for the total health risk. Policies that act on both the supply chain variables and the safety index by means of the GSI reduce of 44% the average total health risk. This reduction is much larger than the reduction of policies focused on individual risk factors of the food life-cycle. The proposed FSC model is scalable to any level of the global food system and of- fers a novel perspective in which the global public health is conceived, monitored and regulated. P.41 Convertino, MC*; Munoz-Carpena, RMC; Kiker, GK; Perz, SP; University of Minnesota; matteoc@umn.edu Metacommunity Resilience of the Amazon Tropical Forest Facing Human and Natural Stressors Climate extremes and rapid urbanization are stressors that both shape and threat ecosystems. Thus, questions arise about future scenarios for ecosystems and how we as society can potentially control ecosystem evolution considering natural variability and human needs. Here we reproduce biodiversity patterns of the Amazon’s MAP (Madre de Dios - Acre - Pando) tropical rainforest affected by the construction of the transoceanic highway and climate change with a neutral metacommunity model at different scales and resolutions. The influence of environmental variability in species loss and richness increases with scale and decreases with tree clumpiness heterogeneity. At the ecosystem scale drought sensitivity is 37 % higher than at the plot scale where the difference in scales is of seven orders of magnitude. Conversely, the anthropic disturbance played by the road is much larger at the plot scale, and undetectable at the ecosystem scale because dispersal is not affected. A non trivial pattern is found between the species cluster size and the persistence time. Bimodal distributions of clumpiness results in highly stable species richness and persistence time distributions. The species persistence time follows a power law function whose exponent increases with the magnitude of disturbance. This power law is preserved together with the distribution of tree cover despite changes in the shape of the species richness distribution. We propose the product of the persistence time, its probability of occurrence and the average species cluster size as a measure of metacommunity risk of ecosystems as a function of its resilience. A spatial resilience index as ratio of metacommity risks in the disturbed and undisturbed case is calculated to identify the most resilient communities. Our results show that societal development pressure should consider the ecosystem tree distribution of to minimize and maximize biodiversity loss and persistence time, respectively. The spatial resilience index can be used to plan agricultural and urban expansion that preserve resilient communities. T5-C.1 Cox, T; Cox, Tony; Cox Associates and University of Colorado; tcoxdener@aol.com Possible Futures for Risk Analysis Both the field and the journal Risk Analysis have made major contributions to risk-informed policy making in recent years. Demand is high for skilled assessment and communication of risks and uncertainties, creation and validation of trustworthy risk models, less expensive and more effective procedures to implement risk management guidelines, and better principles and methods for deciding what to do when not enough is known to formulate a conventional decision analysis model. Risk analysis is responding to these and other vital conceptual and practical challenges, and will continue to do so. New and rapidly growing areas, such as infrastructure risk analysis, are likely to add to the importance and impact of the journal. At the same time, the very success of the field has created important challenges to its long-term integrity and credibility. Strong demand for risk-informed decision-making has encouraged short-cuts and methods of dubious reliability which are still branded as risk analyses. Among these are use of unvalidated guesses and input assumptions elicited from selected experts; use of unvalidated statistical and computer simulation risk models and risk projections; presentation of authoritative-looking outcome probabilities and confidence intervals which hide key uncertainties about their underlying assumptions; and ad hoc risk scoring, rating, and ranking procedures deployed without critically assessing whether they actually lead to improved risk management decisions. A strong journal will help to overcome these and other methodological and practical challenges. By showcasing excellent work, encouraging the development and application of sound methods, and explaining and exposing important risk analyses to the scrutiny of a community which cares about genuinely trustworthy and valuable analysis, we can dodge the pitfalls and magnify the value of risk analysis in supporting better policy decisions. M3-C.2 Cox, T; Cox Associates and University of Colorado; tcoxdenver@aol.com Adapting Risk Management to Reduce Regret Two principles for choosing among alternative risk management policies are: (a) Seek to maximize ex ante expected social utility (roughly equivalent to expected net benefit); and (b) Seek to minimize ex post regret, defined as the difference between the maximum value (or net benefit) that could have been achieved, as assessed in hindsight, and the value that actually was achieved. We show that these two principles typically lead to different recommended choices, for both individuals and groups, especially when there are uncertainties or disagreements about probabilities or preferences. Under these realistic conditions of conflict and uncertainty, effective policy-making requires learning to make choices that adaptively reduce or minimize regret. Risk-cost-benefit and expected utility maximization principles that instead seek to identify the best next action, using realistically imperfect information, are subject to over-fitting and other biases that typically over-estimate the net benefits from costly interventions. We discuss conditions under which policy-making can be improved by switching from trying to maximize expected net benefits to trying to minimize ex post regret. This change helps to resolve some long-standing difficulties in risk-cost-benefit analysis, such as how to avoid over- or under-discounting of far future effects and how to decide what to do when future preferences and effects of current actions are highly uncertain. December 8-11, 2013 - Baltimore, MD
SRA 2013 Annual Meeting Abstracts W4-J.3 Cox, T; Cox Associates and University of Colorado; TCoxDenver@aol.com Have Historical Reductions in Ozone and Fine Particulate Matter Caused Reductions in Mortality Rates? Between 1999 and 2010, levels of air pollutants in counties in Texas changed significantly, with fine particulate matter (PM2.5) and coarser particulate matter (PM10) declining by over 25% in some counties, and ozone exhibiting large variations from year to year. This history provides an opportunity to compare changes in average ambient pollutant levels from year to year in different counties to corresponding changes in all-cause, cardiovascular, and respiratory mortality rates. We test the hypothesis that changes in historical pollution levels caused or help to predict changes in mortality rates. The hypothesis of a significant linear relation between changes in pollutant concentrations from year to year and corresponding changes in all-cause, cardiovascular, or respiratory disease risks is not supported by the historical data from Texas counties. Nonparametric tests (Spearman’s rank correlations) also show no significant ordinal associations between yearly changes in pollutant levels and corresponding changes in disease mortality rates. These findings suggest that predicted substantial short-term human benefits of incremental life-saving from reducing PM2.5 and ozone, predicted by the United States Environmental Protection Agency (EPA) and others based on statistical models of exposure-response associations elsewhere, may not hold in Texas counties. This possibility emphasizes both the potential for heterogeneity in air pollution health effects across regions, and also the high potential value of information from accountability research that compares model-based predictions of health benefits from reducing air pollutants to historical records of what has actually occurred. T1-A.3 Cuite, CL*; Hallman, WK; Rutgers, The State University; cuite@aesop.rutgers.edu Social Media and Food Crisis Communication This study explores how to use social media to effectively communicate with the public about food risks. Using an Internet-based survey with a national sample of 1904 adults, we tested how the format of a URL affects how people respond to a message and the effectiveness of different sources of a social media message. We randomly assigned participants to see one of four “tiny” URLs at the end of a social-media style message (in which no source was identified) concerning a food contamination event. The URLs are from actual government tweets, two with usa.gov (go.usa.gov/YXNC and 1.usa.gov/RvvLKI) and two with random text (is.gd/FfQIDl and ow.ly/emXG7). The two URLS with “usa.gov” were significantly more likely to be perceived as being from the government (F (3, 1167) 20.78, p
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Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

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