Risk Analysis最新文献

As synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists' research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM-based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

This study examined the paths through which the news frames of particulate matter (PM) influence support for governmental policies aiming to address PM. It also explored the mediating effects of anxiety and risk perception in the relationship between news frames and policy support, as well as the moderating effects of media exposure and psychological distance on the PM news framing effect. Based on an experimental design (N = 676), two groups of news frames were prepared for comparison: a narrative frame group and a numerical frame group. The results showed no significant differences in anxiety or risk perception between the two groups. Further, no significant mediating effects of anxiety or risk perception were found in the process through which PM news frames influence support for governmental policies. However, media exposure significantly moderated the effect of the narrative frame: With high (low) media exposure, the narrative frame positively (negatively) influenced policy support through risk perception. Moreover, when the level of psychological distance was low, the narrative frame positively influenced policy support through risk perception. This study contributes to the literature on news framing of PM by integrating cognitive and emotional mechanisms in forming policy attitudes.

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

The communication of extreme weather forecasts (e.g., heatwaves and extreme precipitation) is a challenge for weather forecasters and emergency managers who are tasked with keeping residents safe during often unprecedented situations. Weather models have inherent uncertainty, and the ability for potentially life-saving information to get to the people who need it most (e.g., those who need to evacuate) remains a challenge despite the proliferation of digital access to information and social media sites like Twitter. It is also unclear the role that community-based organizations and super-local governmental entities play or may play during weather events in transmitting weather information and providing assistance. In New York City, there remains robust inequality, with communities that are historically disadvantaged often suffering the highest number of deaths and level of destruction following weather events. Results from interviewing 26 New York City community leaders suggest that local organizations often act as intermediaries, passing on official weather information to members of their audience, regardless of the mission statement of their organization. Common challenges for communities in responding to extreme weather include lack of access to information, language barriers, and insufficient resources. Considerations for future weather communication strategies are discussed.

The proliferation of inaccurate and misleading information about COVID-19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID-19 protective behaviors in an armed-conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021. Structural equation modeling tested hypotheses generated by the PADM. The findings indicate that the infodemic reduces the likelihood of individuals adopting protective measures against COVID-19. Surprisingly, official announcements by accountable authorities do not moderate the relationship between the infodemic and protective responses. These results highlight the need for further research on resilience in armed-conflict countries. This study contributes to understanding armed-conflict countries' unique challenges in combating health crises. Addressing the infodemic and promoting accurate information is crucial in enhancing protective behaviors and mitigating the negative impact of misinformation. Policymakers and public health authorities can utilize these insights to develop targeted interventions and communication strategies that ensure accurate information dissemination and encourage the adoption of adequate protective measures.

Imported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway-level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container-by-container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision-making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box-level (beyond only container-level) inspection data, which could be used to estimate this correlation and other model parameters.

The concept of resilience intrinsically links with both complexity and adaptive capacity. Scholars from different fields agree on this. Still, the detailed relations between resilience, complexity, and adaptive capacity need a more thorough theoretical analysis. This article analyses resilience with the help of assumptions from complex adaptive systems (CAS) theory to answer two questions in more detail: What is the relation between resilience and complexity? How can adaptive capacity contribute to resilience? By applying basic ideas from CAS theory to the resilience discourse, the article deduces that complexity of a system is a necessary condition for resilience because complex systems consist of agents that possess adaptive capacity, whereas simple systems consist of mere elements that cannot adapt to unexpected disruptions. The relation between complexity and resilience is multidimensional. Growing complexity leads to a growing need for resilience because the chances for severe, unexpected disruptions increase. The analysis of adaptive capacities revealed that systems and the agents they consist of can possess of specialized and general adaptive capacity. General adaptive capacity is the core feature of resilience because it enables systems to cope with unexpected disruptions. System design principles such as diversity within functional groups and redundancy help to increase general adaptive capacity. The same is true on the community level for social capital and on the individual level for disaster preparedness measures because they increase coping capacities independent of specific hazards.

Although persuasive messages are designed to motivate individuals to engage in intended behaviors, they do not always work. Often, people follow previously established values and ideologies and dismiss persuasive messages. We examine how participants react to a persuasive message related to plastic pollution and how these reactions shape their willingness to recycle and reuse. Results indicate that environmental values and political ideology are associated with message derogation in distinct ways, which, in turn, affect risk perception, self-efficacy, and intention to recycle and reuse. Further, past behavior moderates the relationship between message derogation and perceived risk, but not the relationship between message derogation and self-efficacy. These results suggest that pre-existing values and ideologies play an important role in message derogation, a hitherto under-researched phenomenon that has key implications for self-reported behavioral change. Moreover, past behavior could serve as a powerful lever in steering risk perception and behavioral intent.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Culture can have a major impact on how we perceive different hazards. In the Romantic period, nature was described and portrayed as mysterious and benevolent. A deep connection to nature was perceived as important. We proposed that this romantic view would be positively related to people's risk perceptions of man-made hazards and, more specifically, to concerns about climate change. Further, we hypothesized that the Romantic perception of nature leads to a biased perception of natural hazards and that the moral component of action is of particular importance above and beyond the mere efficacy of the action. We conducted an online survey in Germany (N = 531), a country where Romanticism had a very widespread influence. The study shows that individuals with a Romantic view of nature perceived greater risks associated with climate change than those without such a view. In addition, those with a Romantic view of nature were more likely to support measures to reduce the risks of climate change, even when it is said that such measures are not effective. Finally, the study found a significantly higher positive correlation between Romantic views of nature and risk perceptions of man-made versus natural hazards. The results suggest that ideas developed during the Romantic era continue to influence hazard perception in Germany.