Scenario Tree Modeling to Analyze the Probability of Classical Swine Fever Virus Introduction into Member States of the European Union

The introduction of classical swine fever virus (CSFV) into a country free of disease without vaccination may have huge consequences in terms of both disease spread and economic losses. More quantitative insight into the main factors determining the probability of CSFV introduction (PCSFV) is needed to optimally use resources for the prevention of CSFV introduction. For this purpose a spreadsheet model was constructed that calculates the annual PCSFV into member states of the European Union (EU). The scenario pathway approach was used as most probabilities in the model are very small. Probability distributions were used to take into account inherent variability of input parameters. The model contained pathways of CSFV introduction including the import of pigs and pork products, returning livestock trucks, and contacts with wild boar. All EU member states were included as possible sources of CSFV. Default results for the Netherlands showed a mean overall annual PCSFV of approximately 0.06, indicating that the Netherlands can expect CSFV introduction on average once every 18 years from the pathways and countries included in the model. Almost 65% of this probability could be attributed to the pathway of returning livestock trucks. The most likely sources of CSFV introduction were Germany, Belgium, and the United Kingdom. Although the calculated probabilities were rather low when compared with expert estimates and recent history, the most likely causes of CSFV introduction indicated by the model were considered to be realistic. It was therefore concluded that the model is a useful tool to structure and analyze information for decision making concerning the prevention of CSFV introduction.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

A Framework for Understanding Uncertainty in Seismic Risk Assessment

A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk-based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over- or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground-motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

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.     

Probabilistic Reliability Modeling for Oil Exploration & Production (E&P) Facilities in the Tallgrass Prairie Preserve

The aging domestic oil production infrastructure represents a high risk to the environment because of the type of fluids being handled (oil and brine) and the potential for accidental release of these fluids into sensitive ecosystems. Currently, there is not a quantitative risk model directly applicable to onshore oil exploration and production (E&P) facilities. We report on a probabilistic reliability model created for onshore exploration and production (E&P) facilities. Reliability theory, failure modes and effects analysis (FMEA), and event trees were used to develop the model estimates of the failure probability of typical oil production equipment. Monte Carlo simulation was used to translate uncertainty in input parameter values to uncertainty in the model output. The predicted failure rates were calibrated to available failure rate information by adjusting probability density function parameters used as random variates in the Monte Carlo simulations. The mean and standard deviation of normal variate distributions from which the Weibull distribution characteristic life was chosen were used as adjustable parameters in the model calibration. The model was applied to oil production leases in the Tallgrass Prairie Preserve, Oklahoma. We present the estimated failure probability due to the combination of the most significant failure modes associated with each type of equipment (pumps, tanks, and pipes). The results show that the estimated probability of failure for tanks is about the same as that for pipes, but that pumps have much lower failure probability. The model can provide necessary equipment reliability information for proactive risk management at the lease level by providing quantitative information to base allocation of maintenance resources to high-risk equipment that will minimize both lost production and ecosystem damage.     

Sensitivity Analysis of a Two‐Dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated

The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo‐randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used—that is, an ANOVA‐like model and Sobol sensitivity indices—to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats.     

Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

In this article, we propose an integrated direct and indirect flood risk model for small‐ and large‐scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb‐Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input‐output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high‐ and low‐probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low‐probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high‐probability events are qualitatively different from low‐probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high‐probability and low‐probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.     

Water pollution risk associated with natural gas extraction from the Marcellus Shale

In recent years, shale gas formations have become economically viable through the use of horizontal drilling and hydraulic fracturing. These techniques carry potential environmental risk due to their high water use and substantial risk for water pollution. Using probability bounds analysis, we assessed the likelihood of water contamination from natural gas extraction in the Marcellus Shale. Probability bounds analysis is well suited when data are sparse and parameters highly uncertain. The study model identified five pathways of water contamination: transportation spills, well casing leaks, leaks through fractured rock, drilling site discharge, and wastewater disposal. Probability boxes were generated for each pathway. The potential contamination risk and epistemic uncertainty associated with hydraulic fracturing wastewater disposal was several orders of magnitude larger than the other pathways. Even in a best-case scenario, it was very likely that an individual well would release at least 200 m3 of contaminated fluids. Because the total number of wells in the Marcellus Shale region could range into the tens of thousands, this substantial potential risk suggested that additional steps be taken to reduce the potential for contaminated fluid leaks. To reduce the considerable epistemic uncertainty, more data should be collected on the ability of industrial and municipal wastewater treatment facilities to remove contaminants from used hydraulic fracturing fluid.     

Quantitative Risk Assessment of Vibrio parahaemolyticus in Finfish: A Model of Raw Horse Mackerel Consumption in Japan

The aim of this study was to evaluate the effects of implemented control measures to reduce illness induced by Vibrio parahaemolyticus (V. parahaemolyticus) in horse mackerel (Trachurus japonicus), seafood that is commonly consumed raw in Japan. On the basis of currently available experimental and survey data, we constructed a quantitative risk model of V. parahaemolyticus in horse mackerel from harvest to consumption. In particular, the following factors were evaluated: bacterial growth at all stages, effects of washing the fish body and storage water, and bacterial transfer from the fish surface, gills, and intestine to fillets during preparation. New parameters of the beta‐Poisson dose‐response model were determined from all human feeding trials, some of which have been used for risk assessment by the U.S. Food and Drug Administration (USFDA). The probability of illness caused by V. parahaemolyticus was estimated using both the USFDA dose‐response parameters and our parameters for each selected pathway of scenario alternatives: washing whole fish at landing, storage in contaminated water, high temperature during transportation, and washing fish during preparation. The last scenario (washing fish during preparation) was the most effective for reducing the risk of illness by about a factor of 10 compared to no washing at this stage. Risk of illness increased by 50% by exposure to increased temperature during transportation, according to our assumptions of duration and temperature. The other two scenarios did not significantly affect risk. The choice of dose‐response parameters was not critical for evaluation of control measures.     

Quantitative Assessment of the Risk of Release of Foot‐and‐Mouth Disease Virus via Export of Bull Semen from Israel

Various foot‐and‐mouth disease (FMD) virus strains circulate in the Middle East, causing frequent episodes of FMD outbreaks among Israeli livestock. Since the virus is highly resistant in semen, artificial insemination with contaminated bull semen may lead to the infection of the receiver cow. As a non‐FMD‐free country with vaccination, Israel is currently engaged in trading bull semen only with countries of the same status. The purpose of this study was to assess the risk of release of FMD virus through export of bull semen in order to estimate the risk for FMD‐free countries considering purchasing Israeli bull semen. A stochastic risk assessment model was used to estimate this risk, defined as the annual likelihood of exporting at least one ejaculate of bull semen contaminated with viable FMD virus. A total of 45 scenarios were assessed to account for uncertainty and variability around specific parameter estimates and to evaluate the effect of various mitigation measures, such as performing a preexport test on semen ejaculates. Under the most plausible scenario, the annual likelihood of exporting bull semen contaminated with FMD virus had a median of 1.3 * 10^?7 for an export of 100 ejaculates per year. This corresponds to one infected ejaculate exported every 7 million years. Under the worst‐case scenario, the median of the risk rose to 7.9 * 10^?5, which is equivalent to the export of one infected ejaculate every 12,000 years. Sensitivity analysis indicated that the most influential parameter is the probability of viral excretion in infected bulls.     

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.     

基于知识元的突发事件风险分析

科学地分析突发事件的风险,有利于应急管理部门正确制定应对方案,降低事件损失。突发事件风险分析中受到多因素高维数据和小样本数据信息不完备的约束,无法全面识别突发事件的风险。本文从突发事件系统观点出发,以知识元模型、投影寻踪方法和信息扩散理论为基础,提出了基于知识元的突发事件风险分析方法。该方法采用知识元模型描述了突发事件已认知的共性本体特征,通过探寻事件风险等级标准数据的最佳投影方向降低了输入元素观测数据的维数,将输入元素观测样本所包含的风险信息扩散到输出属性的风险指标论域的控制点上,从而确定了突发事件发生的风险概率。实例分析中,根据国家《地表水环境质量标准（GB3838-2002）》划分水污染风险等级,利用某湖泊8个监测点实时检测数据,分析该湖泊突发水污染事件的风险性。研究结果表明基于知识元的突发事件风险分析方法能够根据研究区域突发事件风险等级标准和观测点的样本数据,动态定量的分析和评估突发事件潜在风险,为突发事件的应急管理提供科学依据。本文提出的突发事件风险方法对于已经建立实时监测系统的危险区域分析突发事件的风险性具有一定的借鉴意义。     

Sensitivity of Physiologically Based Pharmacokinetic Models to Variation in Model Parameters: Methylene Chloride

The parameters in a physiologically based pharmacokinetic (PBPK) model of methylene chloride were varied systematically, and the resulting variation in a number of model outputs was determined as a function of time for mice and humans at several exposure concentrations. The importance of the various parameters in the model was highly dependent on the conditions (concentration, species) for which the simulation was performed and the model output (dose surrogate) being considered. Model structure also had a significant impact on the results. For sensitivity analysis, particular attention must be paid to conservation equations to ensure that the variational calculations do not alter mass balance, introducing extraneous effects into the model. All of the normalized sensitivity coefficients calculated in this study ranged between −1.12 and 1, and most were much less than 1 in absolute value, indicating that individual input errors are not greatly amplified in the outputs. In addition to ranking parameters in terms of their impact on model predictions, time-dependent sensitivity analysis can also be used as an aid in the design of experiments to estimate parameters by predicting the experimental conditions and sampling points which will maximize parameter identifiability.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

On the product selection and plant dimensioning problem under uncertainty

We present a two-stage full recourse model for strategic production planning under uncertainty, whose aim consists of determining product selection and plant dimensioning. The main uncertain parameters are the product price, demand and production cost. The benefit is given by the product net profit over the time horizon minus the investment depreciation and operation costs. The Value-at-Risk and the reaching probability are considered as risk measures in the objective function to be optimized as alternatives to the maximization of the expected benefit over the scenarios. The uncertainty is represented by a set of scenarios. The problem is formulated as a mixed 0–1 Deterministic Equivalent Model. The strategic decisions to be made in the first stage are represented by 0–1 variables. The tactical decisions to be made in the second stage are represented by continuous variables. An approach for problem solving based on a splitting variable mathematical representation via scenario is considered. The problem uses the Twin Node Family concept within the algorithmic framework known as Branch-and-Fix Coordination for satisfying the nonanticipativity constraints. Some computational experience is reported.     

风险集聚类邻避冲突事件随机演化情景分析

随着中国现代城市管理社区制的推行,由公共设施引发的社区冲突逐步凸显,其中,风险集聚类邻避设施成为引发社区冲突的主要诱因。邻避冲突事件演化过程的复杂性和随机扰动要求现有研究不应局限于确定环境下事件的分析,而应在更加真实的不确定环境下对冲突本身展开探讨。鉴于此,本文以演化博弈论为理论基础,引入高斯白噪声随机干扰项,构建风险集聚类邻避冲突事件中营建企业与周边民众两类群体的随机演化博弈模型,对比分析在无政府监管与政府监管情景下群体策略选择行为的随机演化过程,并利用Matlab进行数值仿真。研究发现:(1)无政府监管情景下,当营建企业采取强硬策略收益小于成本,且周边民众采取抗争策略成本大于收益时,(合作,妥协)是其演化均衡策略组合;营建企业与周边民众策略选择演化速度与初始策略选择概率密切相关。(2)政府监管情景下,当政府监管力度大于营建企业采取强硬策略收益与采取合作策略收益之差,且政府监管力度大于周边民众采取抗争策略收益与采取妥协策略收益之差时,(合作,妥协)是其唯一策略演化均衡点;政府监管力度对营建企业策略选择有显着影响,而对周边民众策略选择无显着影响。(3)随机因素对风险集聚类邻避冲突中营建...     

Managing Potential Health Risks from Electric Powerlines: A Decision Analysis Caught in Controversy

Over the past 20 years, several epidemiological studies have found an association between exposure to electromagnetic fields (EMFs) and health effects, including childhood leukemia and adult brain cancer. However, experts strongly disagree about whether this association is causal and, if so, how strong it is. In this article, we examine several alternatives to reduce EMFs from sources of the California power grid, including undergrounding distribution and transmission lines and reconfiguring or rephasing lines. The alternatives were evaluated in terms of the potential health risk reduction, cost, impacts on service reliability, property values, and many other consequences. Because of the uncertainty about an EMF-health link, the main effort was to determine the sensitivity of the decisions to the probability and seriousness of an EMF hazard. User-friendly computer models were developed to allow stakeholders to change the model assumptions and parameters to analyze the impacts of their own assumptions and estimates on the decision. The analysis clearly demonstrated that only four of the many concerns raised by the stakeholders could make a difference in the decision: health risks, costs, service reliability, and property values. Whether undergrounding, moderate alternatives for EMF reduction, or no change was the best decision depended on a few key factors, including the probability that EMF exposure is a hazard, the severity of this hazard, how the EMF reduction measures are financed, and the impacts on property values. While the analysis did not resolve the EMF issues, it showed that even in the most controversial settings, a little analysis goes a long way to clarifying the issues and to focus the debate.     

Subjective-Probability-Based Scenarios for Uncertain Input Parameters: Stratospheric Ozone Depletion

Risk analysis often depends on complex, computer-based models to describe links between policies (e.g., required emission-control equipment) and consequences (e.g., probabilities of adverse health effects). Appropriate specification of many model aspects is uncertain, including details of the model structure; transport, reaction-rate, and other parameters; and application-specific inputs such as pollutant-release rates. Because these uncertainties preclude calculation of the precise consequences of a policy, it is important to characterize the plausible range of effects. In principle, a probability distribution function for the effects can be constructed using Monte Carlo analysis, but the combinatorics of multiple uncertainties and the often high cost of model runs quickly exhaust available resources. This paper presents and applies a method to choose sets of input conditions (scenarios) that efficiently represent knowledge about the joint probability distribution of inputs. A simple score function approximately relating inputs to a policy-relevant output—in this case, globally averaged stratospheric ozone depletion—is developed. The probability density function for the score-function value is analytically derived from a subjective joint probability density for the inputs. Scenarios are defined by selected quantiles of the score function. Using this method, scenarios can be systematically selected in terms of the approximate probability distribution function for the output of concern, and probability intervals for the joint effect of the inputs can be readily constructed.     

Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates

In flood risk analysis, limitations in the multivariate statistical models adopted to model the hydraulic load have restricted the probability of a defense suffering structural failure to be expressed conditionally on a single hydraulic loading variable. This is an issue at the coastal level where multiple loadings act on defenses with the exact combination of loadings dictating their failure probabilities. Recently, a methodology containing a multivariate statistical model with the flexibility to robustly capture the dependence structure between the individual loadings was used to derive extreme nearshore loading conditions. Its adoption will permit the incorporation of more precise representations of a structure's vulnerability in future analyses. In this article, a fragility representation of a shingle beach, where the failure probability is expressed over a three‐dimensional loading parameter space—water level, wave height, and period—is derived at two localities. Within the approach, a Gaussian copula is used to capture any dependencies between the simplified geometric parameters of a beach's shape. Beach profiles are simulated from the copula and the failure probability, given the hydraulic load, determined by the reformulated Bradbury barrier inertia parameter model. At one site, substantial differences in the annual failure probability distribution are observed between the new and existing approaches. At the other, the beach only becomes vulnerable after a significant reduction of the crest height with its mean annual failure probability close to that presently predicted. It is concluded that further application of multivariate approaches is likely to yield more effective flood risk management.     

Probabilistic Assessment of the Failure Risk of the Europa Clipper Spacecraft due to Radiations

The Europa mission approved in 2019 is still in the development phase. It is designed to conduct a detailed reconnaissance of that moon of Jupiter as it could possibly support life as we know it. This article is based on a top-down approach (mission → system → subsystems → components) to model the probability of mission failure. The focus here is on the case where the (uncertain) radiation load exceeds the (uncertain) capacity of critical subsystems of the spacecraft. The model is an illustrative quantification of the uncertainties about (1) the complex external radiation environment in repeated exposures, (2) the effectiveness of the shielding in different zones of the spacecraft, and (3) the components’ capacities, by modeling all three as dynamic random variables. A simulation including a sensitivity analysis is used to obtain the failure probability of the whole mission in forty-five revolutions around Jupiter. This article illustrates how probabilistic risk analysis based on engineering models, test results and expert opinions can be used in the early stages of the design of space missions when uncertainties are large. It also describes the optimization of the spacecraft design, taking into account the decisionmakers’ risk attitude and the mission resource constraints.