Risk Analysis最新文献

{"title":"Predicting pharmaceutical supply chain disruptions before and during the COVID-19 pandemic.","authors":"Andrea C Hupman, Juan Zhang, Haitao Li","doi":"10.1111/risa.17453","DOIUrl":"https://doi.org/10.1111/risa.17453","url":null,"abstract":"<p><p>Disruptions to the pharmaceutical supply chain (PSC) have negative implications for patients, motivating their prediction to improve risk mitigation. Although data analytics and machine learning methods have been proposed to support the characterization of probabilities to inform decisions and risk mitigation strategies, their application in the PSC has not been previously described. Further, it is unclear how well these models perform in the presence of emergent events representing deep uncertainty such as the COVID-19 pandemic. This article examines the use of data-driven models to predict PSC disruptions before and during the COVID-19 pandemic. Using data on generic drugs from the pharmacy supply chain division of a Fortune 500 pharmacy benefit management firm, we have developed predictive models based on the naïve Bayes algorithm, where the models predict whether a specific supplier or whether a specific product will experience a supply disruption in the next time period. We find statistically significant changes in the relationships of nearly all variables associated with product supply disruptions during the pandemic, despite pre-pandemic stability. We present results showing how the sensitivity, specificity, and false positive rate of predictive models changed with the onset of the COVID-19 pandemic and show the beneficial effects of regular model updating. The results show that maintaining model sensitivity is more challenging than maintaining specificity and false positive rates. The results provide unique insight into the pandemic's effect on risk prediction within the PSC and provide insight for risk analysts to better understand how surprise events and deep uncertainty affect predictive models.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142111566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk incident analyses in the transportation of anhydrous ammonia as an emerging clean energy resource.","authors":"Md Ferdousul Haque Shikder, Yili Tang, Eman Almehdawe, Jefferson Cruz Araújo","doi":"10.1111/risa.17634","DOIUrl":"https://doi.org/10.1111/risa.17634","url":null,"abstract":"<p><p>Anhydrous ammonia has seen a rapid increase in demand due to recent developments in clean energy technologies. As it is a potential carrier of hydrogen, the transportation industry is currently facing significant logistic challenges as well as safety risks. Based on the causes and effects, this study categorizes incident patterns based on the historical incidents from 1971 to 2021 in the United States during transportation. Analysis of temporal patterns revealed that government regulations and improvement of safety infrastructures have made the biggest impact on lowering incident rates. Spatial analysis methods are also applied to understand the relationships between these incidents and spatial factors, such as land area, number of ammonia production facilities, total average production capacity, and total length of freight railway and highway in each US state. The spatial and temporal patterns and interpretations provide safety references to manage the growing hazardous transport in clean energy.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142111567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risks in the design of regional hydrogen hub systems: A review and commentary.","authors":"Valerie J Karplus, Ioana Iacob, Emily J Moore, M Granger Morgan","doi":"10.1111/risa.17449","DOIUrl":"https://doi.org/10.1111/risa.17449","url":null,"abstract":"<p><p>Early investments in regional hydrogen systems carry two distinct types of risk: (1) economic risk that projects will not be financially viable, resulting in stranded capital, and (2) environmental risk that projects will not deliver deep reductions in greenhouse gas emissions and through leaks, perhaps even contribute to climate change. This article systematically reviews the literature and performs analysis to describe both types of risk in the context of recent efforts in the United States and worldwide to support the development of \"hydrogen hubs\" or regional systems of hydrogen production and use. We review estimates of hydrogen production costs and projections of how future costs are likely to change over time for different production routes, environmental impacts related to hydrogen and methane leaks, and the availability and effectiveness of carbon capture and sequestration. Finally, we consider system-wide risks associated with evolving regional industrial structures, including job displacement and underinvestment in shared components, such as refueling. We conclude by suggesting a set of design principles that should be applied in developing early hydrogen hubs if they are to be a successful step toward creating a decarbonized energy system.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying the \"SOTEC\" framework of sociotechnical risk analysis to the development of an autonomous robot swarm for a public cloakroom.","authors":"Peter Winter, John Downer, James Wilson, Dhaminda B Abeywickrama, Suet Lee, Sabine Hauert, Shane Windsor","doi":"10.1111/risa.17632","DOIUrl":"https://doi.org/10.1111/risa.17632","url":null,"abstract":"<p><p>The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex \"nontechnical\" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared sanitation facilities and risk of respiratory virus transmission in resource-poor settings: A COVID-19 modeling case study.","authors":"Michael A L Hayashi, Sophia M Simon, Kaiyue Zou, Hannah Van Wyk, Mondal Hasan Zahid, Joseph N S Eisenberg, Matthew C Freeman","doi":"10.1111/risa.17633","DOIUrl":"https://doi.org/10.1111/risa.17633","url":null,"abstract":"<p><p>Water supply and sanitation are essential household services frequently shared in resource-poor settings. Shared sanitation can increase the risk of enteric pathogen transmission due to suboptimal cleanliness of facilities used by large numbers of individuals. It also can potentially increase the risk of respiratory disease transmission. As sanitation is an essential need, shared sanitation facilities may act as important respiratory pathogen transmission venues even with strict control measures such as stay-at-home recommendations in place. This analysis explores how behavioral and infrastructural conditions surrounding shared sanitation may individually and interactively influence respiratory pathogen transmission. We developed an individual-based community transmission model using COVID-19 as a motivating example parameterized from empirical literature to explore how transmission in shared latrines interacts with transmission at the community level. We explored mitigation strategies, including infrastructural and behavioral interventions. Our review of empirical literature confirms that shared sanitation venues in resource-poor settings are relatively small with poor ventilation and high use patterns. In these contexts, shared sanitation facilities may act as strong drivers of respiratory disease transmission, especially in areas reliant on shared facilities. Decreasing dependence on shared latrines was most effective at attenuating sanitation-associated transmission. Improvements to latrine ventilation and handwashing behavior were also able to decrease transmission. The type and order of interventions are important in successfully attenuating disease risk, with infrastructural and engineering controls being most effective when administered first, followed by behavioral controls after successful attenuation of sufficient alternate transmission routes. Beyond COVID-19, our modeling framework can be extended to address water, sanitation, and hygiene measures targeted at a range of environmentally mediated infectious diseases.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new end-user-oriented and dynamic approach to post-disaster resilience quantification for individual facilities.","authors":"Gemma Cremen","doi":"10.1111/risa.17637","DOIUrl":"https://doi.org/10.1111/risa.17637","url":null,"abstract":"<p><p>Community recovery from a disaster is a complex process, in which the importance of different types of infrastructure functionality can change over time. Most of the myriad of metrics available for measuring disaster resilience do not capture the dynamic importance of functionality explicitly, however. This means that very different recovery trajectories of a given infrastructure can correspond to the same resilience value, regardless of variations in its utility over time. While some efforts have been made to integrate features of time dependency into individual facility resilience quantification, the resulting metrics either capture only a limited set of temporal instances throughout the post-disaster phase or do not offer a way to prioritize time steps in line with variations in the importance of facility functionality. This study proposes a novel, straightforward metric for component-level post-disaster resilience quantification that overcomes the aforementioned limitations. The metric involves a dynamic weighting component that enables stakeholders to place varying emphasis on different temporal points throughout the recovery process. The end-user-centered approach to resilience quantification facilitated by the metric allows for flexible, context-specific interpretations of infrastructure functionality importance that may vary across different communities. The metric is demonstrated through a hypothetical case study of infrastructure facilities with varying degrees of importance across the post-disaster recovery period, which showcases its versatility relative to a previously well-established measurement of component-level resilience. The proposed metric has significant potential for use in stakeholder-driven approaches to decision making on critical infrastructure (as well as other types of built environment) recovery and resilience.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conceptualizing social risk in relation to climate change and assisted ecosystem adaptation.","authors":"Stewart Lockie, Victoria Graham, Bruce Taylor, Umberto Baresi, Kirsten Maclean, Gillian Paxton, Karen Vella","doi":"10.1111/risa.17635","DOIUrl":"https://doi.org/10.1111/risa.17635","url":null,"abstract":"<p><p>Realizing positive social and environmental outcomes from assisted ecosystem adaptation requires the management of complex, uncertain, and ambiguous risks. Using assisted coral reef adaptation as a case study, this article presents a conceptual framework that defines social impacts as the physical and cognitive consequences for people of planned intervention and social risks as potential impacts transformed into objects of management through assessment and governance. Reflecting on its multiple uses in the literature, we consider \"social risk\" in relation to risks to individuals and communities, risks to First Peoples, risks to businesses or project implementation, possibilities for amplified social vulnerability, and risk perceptions. Although much of this article is devoted to bringing clarity to the different ways in which social risk manifests and to the multiple characters of risk and uncertainty, it is apparent that risk governance itself must be an inherently integrative and social process.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainability risk management: Exploring the role of artificial intelligence capabilities through an information-processing lens.","authors":"Kai Yuan Kong, Kum Fai Yuen","doi":"10.1111/risa.17448","DOIUrl":"https://doi.org/10.1111/risa.17448","url":null,"abstract":"<p><p>The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.","authors":"Yan Song, Baiqing Sun, Yunwu Han, Zhenming Xing","doi":"10.1111/risa.17450","DOIUrl":"https://doi.org/10.1111/risa.17450","url":null,"abstract":"<p><p>As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142018442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability.","authors":"En-Hsuan Lu, Lucie C Ford, Ivan Rusyn, Weihsueh A Chiu","doi":"10.1111/risa.17451","DOIUrl":"10.1111/risa.17451","url":null,"abstract":"<p><p>There are two primary sources of uncertainty in the interpretability of toxicity values, like the reference dose (RfD): estimates of the point of departure (POD) and the absence of chemical-specific human variability data. We hypothesize two solutions-employing Bayesian benchmark dose (BBMD) modeling to refine POD determination and combining high-throughput toxicokinetic modeling with population-based toxicodynamic in vitro data to characterize chemical-specific variability. These hypotheses were tested by deriving refined probabilistic estimates for human doses corresponding to a specific effect size (M) in the Ith population percentile (HD_M ^I) across 19 Superfund priority chemicals. HD_M ^I values were further converted to biomonitoring equivalents in blood and urine for benchmarking against human data. Compared to deterministic default-based RfDs, HD_M ^I values were generally more protective, particularly influenced by chemical-specific data on interindividual variability. Incorporating chemical-specific in vitro data improved precision in probabilistic RfDs, with a median 1.4-fold reduction in uncertainty variance. Comparison with US Environmental Protection Agency's Exposure Forecasting exposure predictions and biomonitoring data from the National Health and Nutrition Examination Survey identified chemicals with margins of exposure nearing or below one. Overall, to mitigate uncertainty in regulatory toxicity values and guide chemical risk management, BBMD modeling and chemical-specific population-based human in vitro data are essential.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}