Applying Bayesian belief networks to health risk assessment

The health risk of noncarcinogenic substances is usually represented by the hazard quotient (HQ) or target organ-specific hazard index (TOSHI). However, three problems arise from these indicators. Firstly, the HQ overestimates the health risk of noncarcinogenic substances for non-critical organs. Secondly, the TOSHI makes inappropriately the additive assumption for multiple hazardous substances affecting the same organ. Thirdly, uncertainty of the TOSHI undermines the accuracy of risk characterization. To address these issues, this article proposes the use of Bayesian belief networks (BBN) for health risk assessment (HRA) and the procedure involved is developed using the example of road constructions. According to epidemiological studies and using actual hospital attendance records, the BBN-HRA can specifically identify the probabilistic relationship between an air pollutant and each of its induced disease, which can overcome the overestimation of the HQ for non-critical organs. A fusion technique of conditional probabilities in the BBN-HRA is devised to avoid the unrealistic additive assumption. The use of the BBN-HRA is easy even for those without HRA knowledge. The input of pollution concentrations into the model will bring more concrete information on the morbidity and mortality rates of all the related diseases rather than a single score, which can reduce the uncertainty of the TOSHI.     

A probabilistic approach to exposure risk assessment

The introduction of hazardous substances into the environment has long been recognized as being a cause of several diseases in humans, wildlife, and plants. The damaging character of suspected contaminants is usually assessed via a “reject/retain” design with no explicit link between levels of exposure and intensities of the potential adverse health effects even though this connection may be important for the development of public health regulations that limit exposure to hazardous substances. Here, we propose a probabilistic approach to exposure risk assessment as a way around this typical flaw. We develop a Bayesian model using proximity to the source of an alleged contaminant as a surrogate for exposure. Subsequently, we carry out an experimental study based on simulated data to illustrate the model implementation with real world data. We also discuss a possible way of extending the model to accommodate potential heterogeneity in the spatial distribution of the focal disease.     

Probabilistic-fuzzy health risk modeling

Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.     

Stochastic environmental risk analysis: an integrated methodology for predicting cancer risk from contaminated groundwater

Stochastic environmental risk assessment considers the effects of numerous biological, chemical, physical, behavioral and physiological processes that involve elements of uncertainty and variability. A methodology for predicting health risks to individuals from contaminated groundwater is presented that incorporates the elements of uncertainty and variability in geological heterogeneity, physiological exposure parameters, and in cancer potency. An idealized groundwater basin is used to perform a parametric sensitivity study to assess the relative impact of (a) geologic uncertainty, (b) behavioral and physiological variability in human exposure and (c) uncertainty in cancer potency on the prediction of increased cancer risk to individuals in a population exposed to contaminants in household water supplied from groundwater. A two-dimensional distribution (or surface) of human health risk was generated as a result of the simulations. Cuts in this surface (fractiles of variability and percentiles of uncertainty) are then used as a measure of relative importance of various model components on total uncertainty and variability. A case study for perchloroethylene or PCE, shows that uncertainty and variability in hydraulic conductivity play an important role in predicting human health risk that is on the same order of influence as uncertainty of cancer potency.     

Integration degree of risk in terms of scene and application

Risk is a scene in the future associated with some adverse incident. Scene means something seen by a viewer, or felt by individuals or various societal groups. Any risk assessment is to model some aspects of the scene for risk. Different aspects for assessment leads to different scene. In this paper, we suggest the integration degree of risk to distinguish characters of risks with respect to the aspects. The total number of factors of a risk system determines the macro degree and the granulation scale for measuring a risk reflects the micro degree. A simple framework depends on the degrees provides an explanation of the integrated risk. The most common model for risk assessment is available for the two-freedom-degree serial risk. A case studying flood risk shows the application to explain what the risk is, where the information is incomplete and we use the information diffusion technique to estimate the risk. Project 40771007 supported by National Natural Science Foundation of China.     

Approximate seismic risk assessment of building structures with explicit consideration of uncertainties

An approximate seismic risk assessment procedure for building structures, which involves pushover analysis that is performed utilizing a deterministic structural model and uncertainty analysis at the level of the equivalent SDOF model, is introduced. Such an approach is computationally significantly less demanding in comparison with procedures based on uncertainty analysis at the level of the entire structure, but still allows for explicit consideration of the effect of record‐to‐record variability and modelling uncertainties. A new feature of the proposed pushover‐based method is the so‐called probabilistic SDOF model. Herein, the proposed methodology is illustrated only for reinforced concrete (RC) frames, although it could be implemented in the case of any building structure, provided that an appropriate probabilistic SDOF model is available. An extensive parametric analysis has been performed within the scope of this study in order to develop a probabilistic SDOF model, which could be used for the seismic risk assessment of both code‐conforming and old, that is, non code‐conforming RC frames. Based on the results of risk analysis for the four selected examples, it is shown that the proposed procedure can provide conservative estimates of seismic risk with reasonable accuracy, in spite of the employed simplifications and the relatively small number of Monte Carlo simulations with Latin hypercube sampling, which are performed at the level of the SDOF model. An indication of the possible default values of dispersion measures for limit‐state intensities in the case of low to medium‐height RC frames is also presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Kriging metamodeling in seismic risk assessment based on stochastic ground motion models

An efficient computational framework is presented for seismic risk assessment within a modeling approach that utilizes stochastic ground motion models to describe the seismic hazard. The framework is based on the use of a kriging surrogate model (metamodel) to provide an approximate relationship between the structural response and the structural and ground motion parameters that are considered as uncertain. The stochastic character of the excitation is addressed by assuming that under the influence of the white noise (used within the ground motion model) the response follows a lognormal distribution. Once the surrogate model is established, a task that involves the formulation of an initial database to inform the metamodel development, it is then directly used for all response evaluations required to estimate seismic risk. The model prediction error stemming from the metamodel is directly incorporated within the seismic risk quantification and assessment, whereas an adaptive approach is developed to refine the database that informs the metamodel development. The ability to efficiently obtain derivative information through the kriging metamodel and its utility for various tasks within the probabilistic seismic risk assessment is also discussed. As an illustrative example, the assessment of seismic risk for a benchmark four‐story concrete office building is presented. The potential that ground motions include near‐fault characteristics is explicitly addressed within the context of this example. The implementation of the framework for the same structure equipped with fluid viscous dampers is also demonstrated. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessment and management of risk in subsurface hydrology: A review and perspective

Uncertainty plagues every effort to model subsurface processes and every decision made on the basis of such models. Given this pervasive uncertainty, virtually all practical problems in hydrogeology can be formulated in terms of (ecologic, monetary, health, regulatory, etc.) risk. This review deals with hydrogeologic applications of recent advances in uncertainty quantification, probabilistic risk assessment (PRA), and decision-making under uncertainty. The subjects discussed include probabilistic analyses of exposure pathways, PRAs based on fault tree analyses and other systems-based approaches, PDF (probability density functions) methods for propagating parametric uncertainty through a modeling process, computational tools (e.g., random domain decompositions and transition probability based approaches) for quantification of geologic uncertainty, Bayesian algorithms for quantification of model (structural) uncertainty, and computational methods for decision-making under uncertainty (stochastic optimization and decision theory). The review is concluded with a brief discussion of ways to communicate results of uncertainty quantification and risk assessment.     

Evaluation of a recently proposed record selection and scaling procedure for low‐rise to mid‐rise reinforced concrete buildings and its use for probabilistic risk assessment studies

This paper evaluates a recent record selection and scaling procedure of the authors that can determine the probabilistic structural response of buildings behaving either in the elastic or post‐elastic range. This feature marks a significant strength on the procedure as the probabilistic structural response distribution conveys important information on probability‐based damage assessment. The paper presents case studies that show the utilization of the proposed record selection and scaling procedure as a tool for the estimation of damage states and derivation of site‐specific and region‐specific fragility functions. The method can be used to describe exceedance probabilities of damage limits under a certain target hazard level with known annual exceedance rate (via probabilistic seismic hazard assessment). Thus, the resulting fragility models can relate the seismicity of the region (or a site) with the resulting building performance in a more accurate manner. Under this context, this simple and computationally efficient record selection and scaling procedure can be benefitted significantly by probability‐based risk assessment methods that have started to be considered as indispensable for developing robust earthquake loss models. Copyright © 2013 John Wiley & Sons, Ltd.     

Partitioned multiobjective risk modeling of carcinogenic compounds in groundwater

Quantifying human cancer risk arising from exposure to contaminated groundwater is complicated by the many hydrogeological, environmental, and toxicological uncertainties involved. In this study, we used Monte Carlo simulation to estimate cancer risk associated with tetrachloroethene (PCE) dissolved in groundwater by linking three separate models for: (1) reactive contaminant transport; (2) human exposure pathways; and (3) the PCE cancer potency factor. The hydrogeologic model incorporates an analytical solution for a one-dimensional advective–dispersive–reactive transport equation to determine the PCE concentration in a water supply well located at a fixed distance from a continuous source. The pathway model incorporates PCE exposure through ingestion, inhalation, and dermal contact. The toxicological model combines epidemiological data from eight rodent bioassays of PCE exposure in the form of a composite cumulative distribution frequency curve for the human PCE cancer potency factor. We assessed the relative importance of individual model variables through their correlation with expected cancer risk calculated in an ensemble of Monte Carlo simulations with 20,000 trials. For the scenarios evaluated, three factors were most highly correlated with cancer risk: (1) the microbiological decay constant for PCE in groundwater, (2) the linear groundwater pore velocity, and (3) the cancer potency factor. We then extended our analysis beyond conventional expected value risk assessment using the partitioned multiobjective risk method (PMRM) to generate expected-value functions conditional to a 1 in 100,000 increased cancer risk threshold. This approach accounts for low probability/high impact outcomes separately from the conventional unconditional expected values. Thus, information on potential worst-case outcomes can be quantified for decision makers. Using PMRM, we evaluated the cost-benefit relationship of implementing several postulated risk management alternatives intended to mitigate the expected and conditional cancer risk. Our results emphasize the importance of hydrogeologic models in risk assessment, but also illustrate the importance of integrating environmental and toxicological uncertainty. When coupled with the PMRM, models integrating uncertainty in transport, exposure, and potency constitute an effective risk assessment tool for use within a risk-based corrective action (RBCA) framework.     

Probability-credibility health risk assessment under uncertain environment

The ability to describe variables in a health risk model through probability theory enables us to estimate human health risk. These types of risk assessment are interpreted as probabilistic risk assessment (PRA). Generally, PRA requires specific estimate of the parameters of the probability density of the input variables. In all circumstances, such estimates of the parameters may not be available due to the lack of knowledge or information. Such types of variables are treated as uncertain variables. These types of information are often termed uncertainty which are interpreted through fuzzy theory. The ability to describe uncertainty through fuzzy set theory enables us to process both random variable and fuzzy variable in a single framework. The method of processing aleatory and epistemic uncertainties into a same framework is coined as hybrid method. In this paper, we are going to talk about such type of hybrid methodology for human health risk assessment. Risk assessment on human health through different pathways of exposure has been attempted many a times combining Monte Carlo analysis and extension principle of fuzzy set theory. The emergence of credibility theory enables transforming fuzzy variable into credibility distribution function which can be used in those hybrid analyses. Hence, an attempt, for the first time, has been made to combine probability theory and credibility theory to estimate risk in human health exposure. This method of risk assessment in the presence of credibility theory and probability theory is identified as probabilistic-credibility method (PCM). The results obtained are then interpreted through probability theory, unlike the other hybrid methodology where the results are interpreted in terms of possibility theory. The results obtained are then compared with probability-fuzzy risk assessment (PFRA) method. Generally, decision under hybrid methodology is made on the index of optimism. An optimistic decision maker estimates from the \(\alpha\)-cut at 1, whereas a pessimistic decision maker estimates from the \(\alpha\)-cut at 0. The PCM is an optimistic approach as the decision is always made at \(\alpha\)=1.     

Simulation of the spread of infectious diseases in a geographical environment

The study of mathematical models for the spread of infectious diseases is an important issue in epidemiology. Given the fact that most existing models cannot comprehensively depict heterogeneities (e.g., the population heterogeneity and the distribution heterogeneity) and complex contagion patterns (which are mostly caused by the human interaction induced by modern transportation) in the real world, a theoretical model of the spread of infectious diseases is proposed. It employs geo-entity based cellular automata to simulate the spread of infectious diseases in a geographical environment. In the model, physical geographical regions are defined as cells. The population within each cell is divided into three classes: Susceptible, Infective, and Recovered, which are further divided into some subclasses by states of individuals. The transition rules, which determine the changes of proportions of those subclasses and reciprocal transformation formulas among them, are provided. Through defining suitable spatial weighting functions, the model is applied to simulate the spread of the infectious diseases with not only local contagion but also global contagion. With some cases of simulation, it has been shown that the results are reasonably consistent with the spread of infectious diseases in the real world. The model is supposed to model dynamics of infectious diseases on complex networks, which is nearly impossible to be achieved with differential equations because of the complexity of the problem. The cases of simulation also demonstrate that efforts of all kinds of interventions can be visualized and explored, and then the model is able to provide decision-making support for prevention and control of infectious diseases. Supported by Postdoctoral Foundation of China (Grant No. 20070410552) and Youth Fund of Institute of Policy and Management (IPM), the Chinese Academy of Sciences (Grant No. O700481Q01)     

Exploring the role of risk perception in influencing flood losses over time

ABSTRACT

What implications do societies’ risk perceptions have for flood losses? This study uses a stylized, socio-hydrological model to simulate the mutual feedbacks between human societies and flood events. It integrates hydrological modelling with cultural theory and proposes four ideal types of society that reflect existing dominant risk perception and management: risk neglecting, risk monitoring, risk downplaying and risk controlling societies. We explore the consequent trajectories of flood risk generated by the interactions between floods and people for these ideal types of society over time. The results suggest that flood losses are substantially reduced when awareness-raising attitudes are promoted through inclusive, participatory approaches in the community. In contrast, societies that rely on top-down hierarchies and structural measures to protect settlements on floodplains may still suffer significant losses during extreme events. This study illustrates how predictions formed through social science theories can be applied and tested in hydrological modelling.     

Exploring the role of risk perception in influencing flood losses over time

ABSTRACT

What implications do societies’ risk perceptions have for flood losses? This study uses a stylized, socio-hydrological model to simulate the mutual feedbacks between human societies and flood events. It integrates hydrological modelling with cultural theory and proposes four ideal types of society that reflect existing dominant risk perception and management: risk neglecting, risk monitoring, risk downplaying and risk controlling societies. We explore the consequent trajectories of flood risk generated by the interactions between floods and people for these ideal types of society over time. The results suggest that flood losses are substantially reduced when awareness-raising attitudes are promoted through inclusive, participatory approaches in the community. In contrast, societies that rely on top-down hierarchies and structural measures to protect settlements on floodplains may still suffer significant losses during extreme events. This study illustrates how predictions formed through social science theories can be applied and tested in hydrological modelling.     

Probabilistic seismic demand model for pounding risk assessment

Earthquake‐induced pounding of adjacent structures can cause severe structural damage, and advanced probabilistic approaches are needed to obtain a reliable estimate of the risk of impact. This study aims to develop an efficient and accurate probabilistic seismic demand model (PSDM) for pounding risk assessment between adjacent buildings, which is suitable for use within modern performance‐based engineering frameworks. In developing a PSDM, different choices can be made regarding the intensity measures (IMs) to be used, the record selection, the analysis technique applied for estimating the system response at increasing IM levels, and the model to be employed for describing the response statistics given the IM. In the present paper, some of these choices are analyzed and evaluated first by performing an extensive parametric study for the adjacent buildings modeled as linear single‐degree‐of‐freedom systems, and successively by considering more complex nonlinear multi‐degree‐of‐freedom building models. An efficient and accurate PSDM is defined using advanced intensity measures and a bilinear regression model for the response samples obtained by cloud analysis. The results of the study demonstrate that the proposed PSDM allows accurate estimates of the risk of pounding to be obtained while limiting the number of simulations required. Copyright © 2016 John Wiley & Sons, Ltd.     

Binomial model on seismic risk analysis

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Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective

In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis, to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment, risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and the results are discussed comparatively.     

A new nonlinear risk assessment model based on an improved projection pursuit

Experience has shown that researchers and engineers are unable to construct ideal models for risk assessment and make optimal decisions in situations with insufficient data. A nonlinear risk assessment model is therefore proposed in this study based on an improved projection pursuit model (IPPM) for use in situations where insufficient data are available. A new projection index is initially proposed based on the maximum entropy principle in order to extract more information from original multidimensional data before a nonlinear risk assessment function is constructed using differential equation modeling. This function can be applied to all risk assessment problems after performing standardization and dimension reduction for the indicators. Five marine environmental risk assessment experiments for naval activity are then performed to train and validate the IPPM, as well as a traditional projection pursuit model using different numbers of training samples. The results of this analysis show that the IPPM is reliable, robust, and consistent, and can improve risk assessments by between 4.3 and 43.7% depending on performance criteria. Satisfactory results are obtained from the IPPM using just 12 training samples, and an acceptable result is still obtained if this number is reduced to just ten. Application of an IPPM therefore represents a valuable tool for risk assessment in situations where data is insufficient.     

Changing of flood risk due to climate and development in Huaihe River basin,China

Assessing the response of flood risk caused by climate change and social development is very important in terms of determining high risk areas in different periods as well as making disaster mitigating plans. We establish a flood risk assessment model based on geographic information system and natural disaster risk assessment theory. In order to compare the index value in different periods and spaces, we utilize the spatial and temporal standardization method to standardized index. To avoid one-sidedness caused by using one weight calibration method only, we employ the least square method to synthesize weights determine by the Analytic Hierarchy Process (AHP) method and the Entropy weight method. We adopt the observed data of the Huaihe River basin from 1960 to 2010 to assess the changing of flood risk between period I (1960–1980) and period II (1980–2010). After pre-processing the atmosphere–ocean coupled global circulation models (AOGCM) data, including bias correction and downscaling, we use the corrected data to predict the flood risk during future period III (2010–2040). The results show that high risk areas and moderate to high risk areas during period I take up 17.68 and 33.88 % of the total area of the Huaihe River basin, respectively. During period II, the high risk areas show an increasing percent change of 1.93 % and a decreasing trend in moderate to high risk areas of 3.8 %. Compared with period II, the high risk areas and the moderate to high risk areas during period III show an increasing trend of 8.02 and 0.77 %, which is the result of the combined effects of climate change and social development. The results presented here can provide useful information for decision-makers.     

A comparative study of numerical approaches to risk assessment of contaminant transport

In risk analysis, a complete characterization of the concentration distribution is necessary to determine the probability of exceeding a threshold value. The most popular method for predicting concentration distribution is Monte Carlo simulation, which samples the cumulative distribution function with a large number of repeated operations. In this paper, we first review three most commonly used Monte Carlo (MC) techniques: the standard Monte Carlo, Latin Hypercube sampling, and Quasi Monte Carlo. The performance of these three MC approaches is investigated. We then apply stochastic collocation method (SCM) to risk assessment. Unlike the MC simulations, the SCM does not require a large number of simulations of flow and solute equations. In particular, the sparse grid collocation method and probabilistic collocation method are employed to represent the concentration in terms of polynomials and unknown coefficients. The sparse grid collocation method takes advantage of Lagrange interpolation polynomials while the probabilistic collocation method relies on polynomials chaos expansions. In both methods, the stochastic equations are reduced to a system of decoupled equations, which can be solved with existing solvers and whose results are used to obtain the expansion coefficients. Then the cumulative distribution function is obtained by sampling the approximate polynomials. Our synthetic examples show that among the MC methods, the Quasi Monte Carlo gives the smallest variance for the predicted threshold probability due to its superior convergence property and that the stochastic collocation method is an accurate and efficient alternative to MC simulations.