A Probabilistic Transmission Model of Salmonella in the Primary Broiler Production Chain

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%-17.4%] (no intervention), and [0.9%-5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%-43.1%] and [1.0%-5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.     

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.     

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.     

The Risks of Using Nuclear Energy Sources in Space: Some Lay Activists'Perceptions

A combination of directive and nondirective techniques was used to study the mental models of 30 lay activists regarding the risks of nuclear energy sources in space. Respondents'perceptions were compared with an "expert model" of the processes generating and controlling these risks, in terms of both the substance of their beliefs and several statistical measures of their performance. These analyses revealed a complex pattern of strengths and weaknesses. Their details are used to derive recommendations for formulating messages about these risks.     

Nuclear Safety in Ontario: A Critical Review of Quantitative Analyses

In this article quantitative analyses of CANDU nuclear generating stations are evaluated using an explicit set of criteria derived from a decision-analytic framework. A systematic search was made for relevant analyses, including both risk assessments and economic analyses. Only a small number of scientifically sound quantitative analyses that are being used to make decisions about specific safety measures or projects were located. The availability of scientifically sound quantitative data for making major energy policy decisions is even more limited, and what is available has major shortcomings. The province of Ontario is now heavily dependent on nuclear energy. Given the uncertainties surrounding the health, environmental, economic, and social consequences of nuclear energy, there is a need to assemble the information that is available within a comprehensive decision-making framework, and to decide future energy policies for the province in a public forum from a societal perspective.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Strengthening Safety Compliance in Nuclear Power Operations: A Role‐Based Approach

Safety compliance is of paramount importance in guaranteeing the safe running of nuclear power plants. However, it depends mostly on procedures that do not always involve the safest outcomes. This article introduces an empirical model based on the organizational role theory to analyze the influence of legitimate sources of expectations (procedures formalization and leadership) on workers’ compliance behaviors. The sample was composed of 495 employees from two Spanish nuclear power plants. Structural equation analysis showed that, in spite of some problematic effects of proceduralization (such as role conflict and role ambiguity), procedure formalization along with an empowering leadership style lead to safety compliance by clarifying a worker's role in safety. Implications of these findings for safety research are outlined, as well as their practical implications.     

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables “backward reasoning” when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.     

Eliciting and Combining Decision Criteria Using a Limited Palette of Utility Functions and Uncertainty Distributions: Illustrated by Application to Pest Risk Analysis

Utility functions in the form of tables or matrices have often been used to combine discretely rated decision‐making criteria. Matrix elements are usually specified individually, so no one rule or principle can be easily stated for the utility function as a whole. A series of five matrices are presented that aggregate criteria two at a time using simple rules that express a varying degree of constraint of the lower rating over the higher. A further nine possible matrices were obtained by using a different rule either side of the main axis of the matrix to describe situations where the criteria have a differential influence on the outcome. Uncertainties in the criteria are represented by three alternative frequency distributions from which the assessors select the most appropriate. The output of the utility function is a distribution of rating frequencies that is dependent on the distributions of the input criteria. In pest risk analysis (PRA), seven of these utility functions were required to mimic the logic by which assessors for the European and Mediterranean Plant Protection Organization arrive at an overall rating of pest risk. The framework enables the development of PRAs that are consistent and easy to understand, criticize, compare, and change. When tested in workshops, PRA practitioners thought that the approach accorded with both the logic and the level of resolution that they used in the risk assessments.     

Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food‐borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high‐dimensional second‐order QMRA model. The case study is a farm‐to‐fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time‐temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second‐order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.     

Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

It is widely accepted that the relationship between lightning wildfire occurrence and its influencing factors vary depending on the spatial scale of analysis, making the development of models at the regional scale advisable. In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning-caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning-caused fire in any 4 × 4-km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possible covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitude and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning-caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long-term forest fuel and fire management strategies.     

Quantitative Risk Assessment Relating to Adventitious Presence of Allergens in Food: A Probabilistic Model Applied to Peanut in Chocolate

Peanut allergy is a public health concern, owing to the high prevalence in France and the severity of the reactions. Despite peanut-containing product avoidance diets, a risk may exist due to the adventitious presence of peanut allergens in a wide range of food products. Peanut is not mentioned in their ingredients list, but precautionary labeling is often present. A method of quantifying the risk of allergic reactions following the consumption of such products is developed, taking the example of peanut in chocolate tablets. The occurrence of adventitious peanut proteins in chocolate and the dose-response relationship are estimated with a Bayesian approach using available published data. The consumption pattern is described by the French individual consumption survey INCA2. Risk simulations are performed using second-order Monte Carlo simulations, which separately propagates variability and uncertainty of the model input variables. Peanut allergens occur in approximately 36% of the chocolates, leading to a mean exposure level of 0.2 mg of peanut proteins per eating occasion. The estimated risk of reaction averages 0.57% per eating occasion for peanut-allergic adults. The 95% values of the risk stand between 0 and 3.61%, which illustrates the risk variability. The uncertainty, represented by the 95% credible intervals, is concentrated around these risk estimates. Children have similar results. The conclusion is that adventitious peanut allergens induce a risk of reaction for a part of the French peanut-allergic population. The method developed can be generalized to assess the risk due to the consumption of every foodstuff potentially contaminated by allergens.     

Urban risks due to climate change in the Andean municipality of Pasto,Colombia: A Bayesian network approach

The current trends of climate change will increase people's exposure to urban risks related to events such as landslides, floods, forest fires, food production, health, and water availability, which are stochastic and very localized in nature. This research uses a Bayesian network (BN) approach to analyze the intensity of such urban risks for the Andean municipality of Pasto, Colombia, under climate change scenarios. The stochastic BN model is linked to correlational models and local scenarios of representative concentration trajectories (RCP) to project the possible risks to which the municipality of Pasto will be exposed in the future. The results show significant risks in crop yields, food security, water availability and disaster risks, but no significant risks on the incidence of acute diarrheal diseases (ADD) and acute respiratory infections (ARI), whereas positive outcomes are likely to occur in livestock production, influenced by population growth. The advantage of the BN approach is the possibility of updating beliefs in the probabilities of occurrence of events, especially in developing, intermediate cities with information-limited contexts.     

A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six‐step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.     

A Risk Assessment Model for Enterotoxigenic Staphylococcus aureus in Pasteurized Milk: A Potential Route to Source‐Level Inference

This article describes a probabilistic model that quantifies hazards that arise from Staphylococcus aureus in milk that is sold as pasteurized in the United Kingdom. The model is centered on coupled dynamics for S. aureus populations, staphylococcal enterotoxins, and the concentration of alkaline phosphatase throughout the milk chain. The chain includes farm collection and storage of pooled milk, further pooling for off‐farm processing, high temperature short time thermal processing, and possible postprocess contamination. The model is implemented as a Bayesian belief network. The results indicate that milk sold as pasteurized is relatively safe with respect to the hazards associated with S. aureus and that most risk is associated with small scale on‐farm processing. An additional analysis of likelihood ratios shows that alkaline phosphatase concentrations in filler tank milk are a good indicator of potential hazards and that these concentrations, in conjunction with other measurements, can be used effectively to discriminate over possible failure modes. The ability to discriminate over potential failure modes can support preemptive actions, such as maintenance or hygiene, which assist with milk chain management and, over extended periods, accumulate to drive improved safety, efficiency, and security.     

Assessing Major Accident Risks to Support Land‐Use Planning Using a Severity‐Vulnerability Combination Method: A Case Study in Dagushan Peninsula,China

Major accident risks posed by chemical hazards have raised major social concerns in today's China. Land‐use planning has been adopted by many countries as one of the essential elements for accident prevention. This article aims at proposing a method to assess major accident risks to support land‐use planning in the vicinity of chemical installations. This method is based on the definition of risk by the Accidental Risk Assessment Methodology for IndustrieS (ARAMIS) project and it is an expansion application of severity and vulnerability assessment tools. The severity and vulnerability indexes from the ARAMIS methodology are employed to assess both the severity and vulnerability levels, respectively. A risk matrix is devised to support risk ranking and compatibility checking. The method consists of four main steps and is presented in geographical information‐system‐based maps. As an illustration, the proposed method is applied in Dagushan Peninsula, China. The case study indicated that the method could not only aid risk regulations on existing land‐use planning, but also support future land‐use planning by offering alternatives or influencing the plans at the development stage, and thus further enhance the roles and influence of land‐use planning in the accident prevention activities in China.     

乡村农户贫困状态特征因子筛选与分析框架：兼论我国乡村振兴路径选择

本文构建了刻画乡村农户贫困状态的特征因子的提取算法和分析框架,旨在实现和帮助完善家庭农场的持续发展,尤其是由贫困农户组成或参与的家庭农场在发展需要的融资方面的乡村信用评估,推动有效地制订对应政策和落地方案,巩固脱贫攻坚成果和防止返贫。本文的最大亮点是以“分类与回归树”(CART)分析和“吉布斯抽样”(Gibbs Sampling)的人工智能算法为工具,对乡村农户贫困状态的特征因子提取建立了对应的框架和分析流程。基于国内某地区乡村建档立卡数据库的31,116个样本,实证研究筛选出12个刻画乡村农户贫困状态高度关联的特征因子,并进一步对特征因子的有效性进行了ROC曲线和AUC测试。结果表明以特征因子分析框架为基础,建设配套的乡村信用评估体系是支持乡村振兴的可持续性最佳解决途径之一,除了能够为乡村贫困户获得持续工作的基本技能或生产环境的改善提升上得到持续性的融资支持提供评估依据和数据支持,也能为乡村和城镇“传帮带”等生产和商务平台的建立提供可持续的基础性数据和信用分析的动态支持。     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.     

Using a Consultant Workshop Model to Improve Performance Management in Chinese Autism Agencies: A Preliminary Study

ABSTRACT

This study investigated the outcomes of using a consultant workshop model to help implement performance management (PM) methods in selected Chinese autism agencies. A need for improvement of educational services in Chinese autism agencies was identified. However, the theory and methods of organizational behavior management (OBM) in general and PM in particular are still foreign in the Chinese autism community. The primary research question of the current study was whether first-line teacher performance in Chinese autism agencies could be improved by the use of a consultant workshop model to train management staff to implement a PM system. Four autism agencies in different Chinese provinces participated in this study. Results demonstrated that after the implementation of the PM system through a consultant workshop model in the participating agencies, the performance of first-line teachers in these agencies improved in several important areas. The intervention appeared modestly effective. Implications of the outcome data and future directions for practitioners and researchers are discussed.