Risk Analysis最新文献

This tutorial focuses on opportunities and challenges associated with using six large, publicly accessible spatial databases published during the last decade by US federal agencies. These databases provide opportunities for researchers to risk-inform policy by comparing community asset, demographic, economic, and social data, along with anthropogenic and natural hazard data at multiple geographic scales. The opportunities for data analysis come with challenges, including data accuracy, variations in the shape and size of data cells, spatial autocorrelation, and other issues endemic to spatial datasets. If ignored, these issues can lead to misleading results. This article briefly reviews the six databases and how agencies use them. It then focuses on the data and its limitations. Examples are provided, as are summaries of the debates surrounding these databases, followed by paths forward for improving their use. We end with a checklist that users should consider when they access any of the six spatial databases or others. We believe that these new resources can be effectively used with appropriate caution to answer user-generated questions about hazards and risks-questions that are important to both community groups and government decision-makers.

Vaccination is the most effective method of preventing and controlling the transmission of infectious diseases within populations. However, the phenomenon of waning immunity can induce periodic fluctuations in epidemic spreading. This study proposes a coupled epidemic-vaccination dynamic model to analyze the influence of immunity waning on the epidemic spreading within the context of voluntary vaccination. First, we establish an SIRSV (susceptible-infected-recovered-susceptible-vaccinated) compartment model to describe the transmission mechanism of infectious diseases based on the mean-field theory. Within this model, we incorporate a nonlinear infection rate with network topology and consider the waning natural and vaccine-induced immunity at the individual level. The evolutionary model of voluntary vaccination strategy is integrated into the SIRSV model to characterize the impact of vaccination behavior on the infectious disease transmission. We also consider two individual risk assessment methods, namely, the individual-based risk assessment (IB-RA) method and the society-based risk assessment (SB-RA) method, originating from local and global perspectives, respectively. Then, utilizing the next-generation matrix method, we derive the time-varying effective reproduction numbers of the model. Also, the theoretical analysis of optimal strategy thresholds in the individual decision-making process is also conducted. The results indicate that the thresholds obtained from the agent-based model (ABM) simulation method are consistent with the theoretical analysis, demonstrating the effectiveness of our model. Finally, we apply the coupled model to the COVID-19 pandemic in France, Germany, Italy, and the United Kingdom. This study analyzes the impact of waning immunity and provides early warning for the outbreak of the epidemics.

Communication research on scientific issues has traditionally relied on the deficit model, which posits that increasing scientific knowledge leads to public acceptance. However, this model's effectiveness is questioned due to inconclusive impacts of knowledge on acceptance. To address this, we propose a dual-process framework combining the deficit model (with scientific knowledge as a key predictor) and a normative opinion process model (where perceived majority opinion plays a crucial role) to predict people's risk/benefit perceptions and their support for genetic modification (GM). Using two national surveys in mainland China-Study 1 with 5145 laypeople and Study 2 with 12,268 scientists-we found positive and significant correlations between scientific knowledge or perceived majority opinion and GM support, mediated by risk/benefit perceptions. Importantly, the normative pathway-represented by perceived majority opinion-exerts a stronger direct and indirect impacts on GM support than scientific knowledge across both scientists and laypeople. Moreover, while the normative process shows a greater influence than the informative process on individuals' perceptions of both benefits and risks associated with GM, its prominence differs between scientists and laypeople depending on the types of perceptions-scientists are more sensitive to risk-related social norms, whereas laypeople are more concerned with norms related to benefits. The paper concludes with a discussion on the theoretical and practical implications of these findings.

Chemicals in general often evoke negative emotions (e.g., worry or fear) in consumers. This can cause consumers to avoid beneficial products and may even lead to suboptimal public policy decisions. It is, therefore, important to better understand how affective reactions to chemicals in general (ARC) form in order to be able to counteract these undesirable effects. The present research contributes to the literature on ARC by studying between-country differences in ARC. While ARC were negative in all countries in our dataset, there were practically relevant between-country differences in how negative they were. We predicted and found that consumers in higher uncertainty avoidance (UA) societies reported more negative ARC than their counterparts in lower UA societies. This effect was due to the rule orientation component rather than the anxiety component of UA. Importantly, while we found evidence for several alternative explanations for between-country variation in ARC (i.e., differences in affluence, individualism, prevalence of chemicals, and trust in consumer protection), the UA effect remained statistically significant when we controlled for other country characteristics. The present research contributes to a better understanding of how contextual factors on the society level influence consumers' ARC and in doing so advances our understanding of ARC. It also has implications for practitioners who wish to educate consumers on the risks and benefits of chemicals.

The unpredictability of the epidemics caused by new, unknown viruses, combined with differing responsibilities among government departments, often leads to a prisoner's dilemma in epidemic information governance. In this context, the whistle-blower effect in the health departments leads to delayed reporting to avoid potential retaliation, and the cry-wolf effect in the administrative departments results in sustained observation to avoid ineffective warnings. To address these challenges, we employ game theory to analyze the dynamics of epidemic information governance and focus on two external governance mechanisms-superior accountability and media supervision-that can help resolve the prisoner's dilemma during and after an outbreak. Our analysis indicates that it is necessary to increase the strategic coordination of whistle-blowers in the short-term decision-making during the outbreak. From a long-term evolution perspective, maintaining optimal levels of superior accountability and media supervision is essential to overcoming the prisoner's dilemma. Media supervision works more slowly in the implement effectiveness than more direct superior accountability. This paper highlights the crucial roles of the whistle-blower effect and the cry-wolf effect in coordination failures of epidemic information governance during outbreaks of unknown viruses. It clarifies the strategic coordination pathways between expert systems and bureaucratic systems and emphasizes the importance of superior accountability and media supervision to enable effective, collaborative epidemic information governance.

The COVID-19 shows significant "catastrophe" characteristics. It has put tremendous pressure on various countries' government finances. A few studies have realized that insurance could be applied in the rescue of catastrophic epidemics to relieve government pressure and improve rescue efficiency. However, most of these studies are based on qualitative analysis, with few quantitative calculations to prove whether it is feasible. Therefore, this article discusses the insurability of epidemic catastrophe insurance and proposes a novel quantitative methodology that measures insurance funds, estimates pandemic-induced losses, and integrates reinsurance analysis to evaluate its effectiveness. Based on epidemic loss data collected from public information in China, the empirical study shows that China's epidemic catastrophe insurance fund can reach 50 billion yuan 5 years after its establishment and over 120 billion 10 years later, which can cover the losses caused by mild and severe epidemics. The epidemic catastrophe fund is capable of meeting claims requirements and effectively covering epidemics of varying severities. Furthermore, the reinsurance model demonstrates that insurers can transfer risks at a relatively reasonable cost, thereby covering losses from extreme epidemics. The findings reveal the effectiveness of epidemic catastrophe insurance, suggesting that worldwide countries incorporate epidemics into their catastrophe insurance to aid government in responding to future catastrophic epidemics.

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

On April 28, 2004, the United Nations Security Council unanimously adopted United Nations Security Council Resolution (UNSCR) 1540. It requires countries to develop and enforce legal and regulatory measures against the proliferation of weapons of mass destruction (WMDs) and their means of delivery, with a focus on the spread to nonstate actors. To date, compliance with UNSCR 1540 has been challenging. Data included in the UNSCR 1540 Committee 2016 report indicate that approximately 35 countries, or 18% of the UN member states, have implemented 70% of the Resolution's requirements. This article uses a multimethod approach to evaluate compliance with UNSCR 1540, including key-word analysis of existing literature to identify compliance factors and a quantitative evaluation method, based on weighting and scoring of these factors by the authors. The model was vetted by a panel of experts and tested on a sample of 12 countries showing that the compliance scores derived from the model correspond to the experts' wholistic judgments about compliance and agreement with the scores of more complex models.

This study explores the risk management challenges associated with safety-critical systems required to execute specific missions. The working component experiences degradation governed by a continuous-time discrete-state Markov chain, whose failure leads to an immediate system breakdown and safety losses. To enhance system survivability, a limited number of identical spares are available for online replacement throughout the mission. At the same time, the mission abort action arises promptly upon encountering excessive safety hazards. To strike an optimal balance between mission completion and system survivability, we delve into the adaptive scheduling of component replacements and mission termination decisions. The joint decision problem of interest constitutes a finite-time Markov decision process with resource limitation, under which we analyze a series of structural properties related to spare availability and component conditions. In particular, we establish structured control-limit policies for both spare replacement and mission termination decisions. For comparison purposes, we evaluate the performance of various heuristic policies analytically. Numerical experiments conducted on the driver system of radar equipment validate the superior model performance in enhancing operational performance while simultaneously mitigating hazard risks.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of the system.