A Risk Analysis for Airborne Pathogens with Low Infectious Doses: Application to Respirator Selection Against Coccidioides immitis Spores

Probability models incorporating a deterministic versus stochastic infectious dose are described for estimating infection risk due to airborne pathogens that infect at low doses. Such pathogens can be occupational hazards or candidate agents for bioterrorism. Inputs include parameters for the infectious dose model, distribution parameters for ambient pathogen concentrations, the breathing rate, the duration of an exposure period, the anticipated number of exposure periods, and, if a respirator device is used, distribution parameters for respirator penetration values. Application of the models is illustrated with a hypothetical scenario involving exposure to Coccidioides immitis, a fungus present in soil in areas of the southwestern United States Inhaling C. immitis spores causes a respiratory tract infection and is a recognized occupational hazard in jobs involving soil dust exposure in endemic areas An uncertainty analysis is applied to risk estimation in the context of selecting respiratory protection with a desired degree of efficacy.     

Geographic Distributions of Extreme Weather Risk Perceptions in the United States

Weather and climate disasters pose an increasing risk to life and property in the United States. Managing this risk requires objective information about the nature of the threat and subjective information about how people perceive it. Meteorologists and climatologists have a relatively firm grasp of the historical objective risk. For example, we know which parts of the United States are most likely to experience drought, heat waves, flooding, snow or ice storms, tornadoes, and hurricanes. We know less about the geographic distribution of the perceived risks of meteorological events and trends. Do subjective perceptions align with exposure to weather risks? This question is difficult to answer because analysts have yet to develop a comprehensive and spatially consistent methodology for measuring risk perceptions across geographic areas in the United States. In this project, we propose a methodology that uses multilevel regression and poststratification to estimate extreme weather and climate risk perceptions by geographic area (i.e., region, state, forecast area, and county). Then we apply the methodology using data from three national surveys (n = 9,542). This enables us to measure, map, and compare perceptions of risk from multiple weather hazards in geographic areas across the country.     

An Empirical Model of Perceived Mortality Risks for Selected U.S. Arsenic Hot Spots

Researchers have long recognized that subjective perceptions of risk are better predictors of choices over risky outcomes than science‐based or experts’ assessments of risk. More recent work suggests that uncertainty about risks also plays a role in predicting choices and behavior. In this article, we develop and estimate a formal model for an individual's perceived health risks associated with arsenic contamination of his or her drinking water. The modeling approach treats risk as a random variable, with an estimable probability distribution whose variance reflects uncertainty. The model we estimate uses data collected from a survey given to a sample of people living in arsenic‐prone areas in the United States. The findings from this article support the fact that scientific information is essential to explaining the mortality rate perceived by the individuals, but uncertainty about the probability remains significant.     

Risk-Management and Risk-Analysis-Based Decision Tools for Attacks on Electric Power

Incident data about disruptions to the electric power grid provide useful information that can be used as inputs into risk management policies in the energy sector for disruptions from a variety of origins, including terrorist attacks. This article uses data from the Disturbance Analysis Working Group (DAWG) database, which is maintained by the North American Electric Reliability Council (NERC), to look at incidents over time in the United States and Canada for the period 1990-2004. Negative binomial regression, logistic regression, and weighted least squares regression are used to gain a better understanding of how these disturbances varied over time and by season during this period, and to analyze how characteristics such as number of customers lost and outage duration are related to different characteristics of the outages. The results of the models can be used as inputs to construct various scenarios to estimate potential outcomes of electric power outages, encompassing the risks, consequences, and costs of such outages.     

Assessing Potential Human Health Hazards and Benefits from Subtherapeutic Antibiotics in the United States: Tetracyclines as a Case Study

Many scientists, activists, regulators, and politicians have expressed urgent concern that using antibiotics in food animals selects for resistant strains of bacteria that harm human health and bring nearer a “postantibiotic era” of multidrug resistant “super‐bugs.” Proposed political solutions, such as the Preservation of Antibiotics for Medical Treatment Act (PAMTA), would ban entire classes of subtherapeutic antibiotics (STAs) now used for disease prevention and growth promotion in food animals. The proposed bans are not driven by formal quantitative risk assessment (QRA), but by a perceived need for immediate action to prevent potential catastrophe. Similar fears led to STA phase‐outs in Europe a decade ago. However, QRA and empirical data indicate that continued use of STAs in the United States has not harmed human health, and bans in Europe have not helped human health. The fears motivating PAMTA contrast with QRA estimates of vanishingly small risks. As a case study, examining specific tetracycline uses and resistance patterns suggests that there is no significant human health hazard from continued use of tetracycline in food animals. Simple hypothetical calculations suggest an unobservably small risk (between 0 and 1.75E‐11 excess lifetime risk of a tetracycline‐resistant infection), based on the long history of tetracycline use in the United States without resistance‐related treatment failures. QRAs for other STA uses in food animals also find that human health risks are vanishingly small. Whether such QRA calculations will guide risk management policy for animal antibiotics in the United States remains to be seen.     

A Risk Analysis Framework for Cyber Security and Critical Infrastructure Protection of the U.S. Electric Power Grid

The purpose of this article is to introduce a risk analysis framework to enhance the cyber security of and to protect the critical infrastructure of the electric power grid of the United States. Building on the fundamental questions of risk assessment and management, this framework aims to advance the current risk analysis discussions pertaining to the electric power grid. Most of the previous risk-related studies on the electric power grid focus mainly on the recovery of the network from hurricanes and other natural disasters. In contrast, a disproportionately small number of studies explicitly investigate the vulnerability of the electric power grid to cyber-attack scenarios, and how they could be prevented or mitigated. Such a limited approach leaves the United States vulnerable to foreign and domestic threats (both state-sponsored and “lone wolf”) to infiltrate a network that lacks a comprehensive security environment or coordinated government response. By conducting a review of the literature and presenting a risk-based framework, this article underscores the need for a coordinated U.S. cyber security effort toward formulating strategies and responses conducive to protecting the nation against attacks on the electric power grid.     

Quantifying Human Health Risks from Virginiamycin Used in Chickens

The streptogramin antimicrobial combination Quinupristin-Dalfopristin (QD) has been used in the United States since late 1999 to treat patients with vancomycin-resistant Enterococcus faecium (VREF) infections. Another streptogramin, virginiamycin (VM), is used as a growth promoter and therapeutic agent in farm animals in the United States and other countries. Many chickens test positive for QD-resistant E. faecium, raising concern that VM use in chickens might compromise QD effectiveness against VREF infections by promoting development of QD-resistant strains that can be transferred to human patients. Despite the potential importance of this threat to human health, quantifying the risk via traditional farm-to-fork modeling has proved extremely difficult. Enough key data (mainly on microbial loads at each stage) are lacking so that such modeling amounts to little more than choosing a set of assumptions to determine the answer. Yet, regulators cannot keep waiting for more data. Patients prescribed QD are typically severely ill, immunocompromised people for whom other treatment options have not readily been available. Thus, there is a pressing need for sound risk assessment methods to inform risk management decisions for VM/QD using currently available data. This article takes a new approach to the QD-VM risk modeling challenge. Recognizing that the usual farm-to-fork ("forward chaining") approach commonly used in antimicrobial risk assessment for food animals is unlikely to produce reliable results soon enough to be useful, we instead draw on ideas from traditional fault tree analysis ("backward chaining") to reverse the farm-to-fork process and start with readily available human data on VREF case loads and QD resistance rates. Combining these data with recent genogroup frequency data for humans, chickens, and other sources (Willems et al., 2000, 2001) allows us to quantify potential human health risks from VM in chickens in both the United States and Australia, two countries where regulatory action for VM is being considered. We present a risk simulation model, thoroughly grounded in data, that incorporates recent nosocomial transmission and genetic typing data. The model is used to estimate human QD treatment failures over the next five years with and without continued VM use in chickens. The quantitative estimates and probability distributions were implemented in a Monte Carlo simulation model for a five-year horizon beginning in the first quarter of 2002. In Australia, a Q1-2002 ban of virginiamycin would likely reduce average attributable treatment failures by 0.35 x 10(-3) cases, expected mortalities by 5.8 x 10(-5) deaths, and life years lost by 1.3 x 10(-3) for the entire population over five years. In the United States, where the number of cases of VRE is much higher, a 1Q-2002 ban on VM is predicted to reduce average attributable treatment failures by 1.8 cases in the entire population over five years; expected mortalities by 0.29 cases; and life years lost by 6.3 over a five-year period. The model shows that the theoretical statistical human health benefits of a VM ban range from zero to less than one statistical life saved in both Australia and the United States over the next five years and are rapidly decreasing. Sensitivity analyses indicate that this conclusion is robust to key data gaps and uncertainties, e.g., about the extent of resistance transfer from chickens to people.     

基于小波领袖多重分形分析法的股市有效性及风险检测

大多数关于股票市场有效性测量与风险侦测的研究都是基于有效市场假说。从分形市场假说出发,本文首先应用小波领袖多重分析法刻画市场波动的多重分形特性来衡量市场的有效性,然后提出一种应用市场最大波动点集的分形维数（D（h_min））及其奇异性指数（h_min）的演化特征来检测金融风险的新方法。实证结果表明：中、美、日三国在不同时期市场的有效性具有明显差异,中国市场的有效性正逐步提高,而美日两国市场的有效性水平与金融危机的发生更为密切。研究发现该方法不但可以正确地定位出金融危机发生的时点,还可以较准确地对风险进行测量。研究结果对于如何提高市场有效性,提升不同投资期限投资者的风险管理水平提供了一些启示。     

An Overview of a Multimedia Benchmarking Analysis for Three Risk Assessment Models: RESRAD, MMSOILS, and MEPAS

Multimedia modelers from the United States Environmental Protection Agency (EPA) and the United States Department of Energy (DOE) collaborated to conduct a detailed and quantitative benchmarking analysis of three multimedia models. The three models—RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)—represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. The study is performed by individuals who participate directly in the ongoing design, development, and application of the models. Model form and function are compared by applying the models to a series of hypothetical problems, first isolating individual modules (e.g., atmospheric, surface water, groundwater) and then simulating multimedia-based risk resulting from contaminant release from a single source to multiple environmental media. Study results show that the models differ with respect to environmental processes included (i.e., model features) and the mathematical formulation and assumptions related to the implementation of solutions. Depending on the application, numerical estimates resulting from the models may vary over several orders-of-magnitude. On the other hand, two or more differences may offset each other such that model predictions are virtually equal. The conclusion from these results is that multimedia models are complex due to the integration of the many components of a risk assessment and this complexity must be fully appreciated during each step of the modeling process (i.e., model selection, problem conceptualization, model application, and interpretation of results).     

A Macro-Economic Framework for Evaluation of Cyber Security Risks Related to Protection of Intellectual Property

The article is based on the premise that, from a macro-economic viewpoint, cyber attacks with long-lasting effects are the most economically significant, and as a result require more attention than attacks with short-lasting effects that have historically been more represented in literature. In particular, the article deals with evaluation of cyber security risks related to one type of attack with long-lasting effects, namely, theft of intellectual property (IP) by foreign perpetrators. An International Consequence Analysis Framework is presented to determine (1) the potential macro-economic consequences of cyber attacks that result in stolen IP from companies in the United States, and (2) the likely sources of such attacks. The framework presented focuses on IP theft that enables foreign companies to make economic gains that would have otherwise benefited the U.S. economy. Initial results are presented.     

An Empirical Agent‐Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large‐scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision‐making process. Based on the social amplification of risk framework, our agent‐based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the “risk publics” model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community‐level parameters—including social groups, relationships, and communication variables, also from survey data—are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks.     

High Production Volume Chemical Amine Oxides [C8–C20] Category Environmental Risk Assessment

An environmental assessment of amine oxides has been conducted under the OECD SIDS High Production Volume (HPV) Program via the Global International Council of Chemical Associations (ICCA) Amine Oxides Consortium. Amine oxides are primarily used in conjunction with surfactants in cleaning and personal care products. Given the lack of persistence or bioaccumulation, and the low likelihood of these chemicals partitioning to soil, the focus of the environmental assessment is on the aquatic environment. In the United States, the E-FAST model is used to estimate effluent concentrations in the United States from manufacturing facilities and from municipal facilities resulting from consumer product uses. Reasonable worst-case ratios of predicted environmental concentration (PEC) to predicted no effect concentration (PNEC) range from 0.04 to 0.003, demonstrating that these chemicals are a low risk to the environment.     

Social Amplification of Wildfire Risk: The Role of Social Interactions and Information Sources

Wildfire is a persistent and growing threat across much of the western United States. Understanding how people living in fire‐prone areas perceive this threat is essential to the design of effective risk management policies. Drawing on the social amplification of risk framework, we develop a conceptual model of wildfire risk perceptions that incorporates the social processes that likely shape how individuals in fire‐prone areas come to understand this risk, highlighting the role of information sources and social interactions. We classify information sources as expert or nonexpert, and group social interactions according to two dimensions: formal versus informal, and generic versus fire‐specific. Using survey data from two Colorado counties, we empirically examine how information sources and social interactions relate to the perceived probability and perceived consequences of a wildfire. Our results suggest that social amplification processes play a role in shaping how individuals in this area perceive wildfire risk. A key finding is that both “vertical” (i.e., expert information sources and formal social interactions) and “horizontal” (i.e., nonexpert information and informal interactions) interactions are associated with perceived risk of experiencing a wildfire. We also find evidence of perceived “risk interdependency”—that is, homeowners’ perceptions of risk are higher when vegetation on neighboring properties is perceived to be dense. Incorporating social amplification processes into community‐based wildfire education programs and evaluating these programs’ effectiveness constitutes an area for future inquiry.     

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system‐based for high‐consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward‐looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high‐consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.     

Ranking the Risks from Multiple Hazards in a Small Community

Natural hazards, human-induced accidents, and malicious acts have caused great losses and disruptions to society. After September 11, 2001, critical infrastructure protection has become a national focus in the United States and is likely to remain one for the foreseeable future. Damage to the infrastructures and assets could be mitigated through predisaster planning and actions. A systematic methodology was developed to assess and rank the risks from these multiple hazards in a community of 20,000 people. It is an interdisciplinary study that includes probabilistic risk assessment (PRA), decision analysis, and expert judgment. Scenarios are constructed to show how the initiating events evolve into undesirable consequences. A value tree, based on multi-attribute utility theory (MAUT), is used to capture the decisionmaker's preferences about the impacts on the infrastructures and other assets. The risks from random failures are ranked according to their expected performance index (PI), which is the product of frequency, probabilities, and consequences of a scenario. Risks from malicious acts are ranked according to their PI as the frequency of attack is not available. A deliberative process is used to capture the factors that could not be addressed in the analysis and to scrutinize the results. This methodology provides a framework for the development of a risk-informed decision strategy. Although this study uses the Massachusetts Institute of Technology campus as a case study of a real project, it is a general methodology that could be used by other similar communities and municipalities.     

The nature conservancy's forest bank: a market-based tool for protecting our working forestland

Non-industrial forest lands represent thirty percent of the U.S.'s private forest land. Totaling 118 million acres, these areas constitute the bulk of land harvested for timber and are suffering from rapid turnover and fragmentation—a critical economic concern for the owners and a problem for the environment health of the land. Only five percent of non-industrial forest land owners have a management plan for their woods, and this number is much lower for parcels under one hundred acres.In an effort to become more creative in preserving this forest land and other areas, The Nature Conservancy has begun to develop creative tools for achieving its conservation goals. The Center for Compatible Economic Development (CCED), within the conservancy, was developed to generate economically viable ways of conserving threatened areas. One of the promising concepts developed by CCED is The Forest Bank.The Forest Bank was designed to offer landowners a new option that gives them the financial liquidity they need in the short-term, while managing their resources sustainably over the long-term. It builds on the local focus that the Nature Conservancy has already established. At the same time, the Forest Bank uses the brand identity of the conservancy to grow a business model that can be effective at protecting forest ecosystems throughout the United States and the world.     

Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit-Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI‐L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk‐reducing effectiveness of WHTI‐L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI‐L, and a range of casualty cost estimates based on the willingness‐to‐pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI‐L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14–26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5–6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit‐cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.