An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.     

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.     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

The Use of Telematics Devices to Improve Automobile Insurance Rates

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay‐as‐you‐drive sample of insureds. We include distance traveled per year as part of an offset in a zero‐inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.     

Mercury,Polychlorinated Biphenyls,Selenium, and Fatty Acids in Tribal Fish Harvests of the Upper Great Lakes

The Chippewa Ottawa Resource Authority monitors fish contaminants in Anishinaabe (Great Lake Native American) tribal fisheries. This article updates previously reported trends in two persistent bioaccumulative toxic (PBT) substances that are the primary contributors to consumption advisory limits for these fish: methylmercury (MeHg) and polychlorinated biphenyls (PCBs). Also, we report, for the first time, an analysis of nutritional benefit bioindicators and metrics in these same Upper Great Lakes fish harvests: selenium (Se) and omega‐3 fatty acids (PUFA‐3s). A novel risk/benefit quantification originally presented by Ginsberg et al. is reported here to characterize the tradeoffs between fatty acid benefits and toxic MeHg health outcomes. We also report a Se benefit metric to characterize the possible protective value against MeHg neurotoxicity based on Ralston et al. Congruent with Anishinaabe cultural motivations to consume fish from their ancestral fisheries, nutritional content was high in locally caught fish and, in some respects, superior to farmed/store‐bought fish. These Great Lakes fish still contained levels of PBTs that require careful education and guidance for consumers. However, the contaminant trends suggest that these fish need not be abandoned as important (both culturally and nutritionally) food sources for the Anishinaabe who harvested them.     

A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

Strategic Grading in the Product Acquisition Process of a Reverse Supply Chain

Most recommerce providers have moved to a quality‐dependent process for the acquisition of used products. They acquire the products via websites at which product holders submit upfront quality statements and receive quality‐dependent acquisition prices for their used devices. Motivated by this development of reverse logistics practice, the aim of this study is to analyze the product assessment process of a recommerce provider in detail. To this end, we first propose a sequential bargaining model with complete information which captures the individual behavior of the recommerce provider and the product holder. We determine the optimal strategies of the product holder and the recommerce provider in this game. We find that the resulting strategies lead to an efficient allocation, although the recommerce provider can absorb most of the bargaining potential due to his last mover advantage. In a second step, we relax the assumption of complete information and include uncertainty about the product holder's valuation of the product. We show the trade‐off underlying the recommerce provider's optimal counteroffer decision and analyze the optimal strategy, using a logistic regression approach on a real‐life dataset of nearly 6,000 product submissions. The results reveal a significant improvement potential, compared to the currently applied strategy.