Risk Analysis最新文献

Community resilience measurement to natural hazards is becoming increasingly relevant due to the growth of federal programs and local and state resilience offices in the United States. This study introduces a methodology to co-produce an actionable resilience metric to measure locally relevant and modifiable indicators of community resilience for the state of South Carolina. The "actionable" metrics, based on the Baseline Resilience Indicators for Communities (BRIC) index, are calculated at the county and tract scale and then compared to "conventional" versions of BRIC. Actionable BRICs perform better in reliability testing than conventional BRICs. Correlations across the two scales of BRIC construction show a stronger relationship between the actionable BRICs than conventional, though all are highly correlated. When mapped, actionable BRIC shows a shifted region of low resilience in the state when compared to conventional BRIC, suggesting that actionable and conventional BRICs are distinct. Scale differences show dissimilar drivers of resilience, with county-level resilience driven by community, social, and environmental resilience and tract-level resilience driven by social and institutional resilience. Actionable tract-level BRIC appears to be the best representation of modifiable resilience for South Carolina, but it comes with trade-offs, including calculation complexity and changing geographies over time. Regardless of scale, the resulting actionable indices offer a useful tracking mechanism for the state resilience office and highlight the importance of integrating top-down and bottom-up resilience perspectives to consider local drivers of resilience. The resulting methodology can be replicated in other states and localities to produce actionable and locally relevant resilience metrics.

Vaccines can be seen as one of the greatest successes in modern medicine. Good examples are the vaccines against smallpox, polio, and measles. Unfortunately, vaccines can have side effects, but the risks are considered by the health authorities and experts to be small compared to their benefits. Nevertheless, there are many who are skeptical of vaccination, something which has been very clearly demonstrated in relation to the COVID-19 disease. Risk is the key concept when evaluating a vaccine, in relation to both its ability to protect against the disease and its side effects. However, risk is a challenging concept to measure, which makes communication about vaccines' performance and side effects difficult. The present article aims at providing new insights into vaccine risks-the understanding, perception, communication, and handling of them-by adopting what is here referred to as a contemporary risk science perspective. This perspective clarifies the relationships between the risk concept and terms like uncertainty, knowledge, and probability. The skepticism toward vaccines is multifaceted, and influenced by concerns that extend beyond the effectiveness and safety of the vaccines. However, by clarifying the relationships between key concepts of risk, particularly how uncertainty affects risk and its characterization, we can improve our understanding of this issue.

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies. This article proposes a novel multiobjective mixed-integer linear programming formulation, which results in the optimal timing of closure and reopening of states and industries in each. The three objectives being pursued include: (i) the epidemiological impact of the pandemic in terms of the percentage of the infected population; (ii) the social vulnerability index of the pandemic policy based on the vulnerability of communities to getting infected, and for losing their job; and (iii) the economic impact of the pandemic based on the inoperability of industries in each state. The proposed model is implemented on a dataset that includes 50 states, the District of Columbia, and 19 industries in the United States. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction.

COVID-19 demonstrated the complex manner in which discourses from risk science are manipulated to legitimize government action. We use Foucault's theory of Governmentality to explore how a risk science discourse shaped national and local government action during COVID-19. We theorize how national government policymakers and local government risk managers were objectified by (and subjectified themselves to) risk science models, results, and discourses. From this theoretical position we analyze a dataset, including observations of risk science discourse and 22 qualitative interviews, to understand the challenges that national government policymakers, risk scientists, and local government risk managers faced during COVID-19. Findings from our Foucauldian discourse analysis show how, through power and knowledge, competing discourses emerge in a situation that was disturbed by uncertainty-which created disturbed senders (policymakers and risk scientists) and disturbed receivers (risk managers) of risk science. First, we explore the interaction between risk science and policymakers, including how the disturbed context enabled policymakers to select discourse from risk science to justify their policies. This showed government's sociopolitical leveraging of scientific power and knowledge by positioning itself as being submissive to "follow the science." Second, we discuss how risk managers (1) were objectified by the discourse from policymakers that required them to be obedient to risk science, and paradoxically (2) used the disturbed context to justify resisting government objectification through their human agency to subjectify themselves and take action. Using these concepts, we explore the foundation of risk science influence in COVID-19.

With COVID-19 moving toward an endemic phase, it is worthwhile to identify lessons from the pandemic that can promote the effective strengthening of national health systems. We look at a single country, Poland, and compare it with the European Union (EU) to contrast approaches and outcomes. Among possible relevant indices, we examine characteristics of COVID-19-related mortality and excess all-cause mortality from March 2020 to February 2022. We demonstrate that both the numbers of COVID-related deaths and all-cause deaths in Poland were much higher than the EU average for most months in the study period. We juxtapose the percentage of fully vaccinated population and cumulative COVID-19 deaths per million people for EU Member States and show that typically higher vaccination rates are accompanied by lower mortality. We also show that, in addition to medical science, the use of a risk science toolbox would have been valuable in the management of the COVID-19 pandemic in Poland. Better and more widespread understanding of risk perception of the pandemic and the COVID-19 vaccines would have improved managing vaccine hesitancy, potentially leading to more effective pro-vaccination measures.

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.

The COVID-19 pandemic presented serious risks to the health and financial wellbeing of millions of people across the world. While many individuals adapted to these challenges through a variety of prosocial and protective behaviors (e.g., social distancing, working from home), many others also engaged in dishonest behaviors (e.g., lying to obtain vaccines or furlough payments). Hence, the COVID-19 pandemic provided a unique context in which to obtain a better understanding of the relationship between risk and dishonesty. Across three preregistered studies, we assessed whether objective risk and perceived risk influenced the decision to behave dishonestly in order to gain access to vaccines and furlough payments during a pandemic. We also assessed the extent to which such dishonesty was deterred by the probability of the dishonesty being detected. We found that heightened health risk perceptions were positively related with lying to obtain a vaccine (Studies 1 and 2), but found no evidence of the same relationship between financial risk perceptions and lying to access furlough payments (Study 2). We also found that the probability of dishonesty being detected had a negative relationship with dishonest behavior (Study 3). In addition, across the three studies, we found that (i) dishonesty was consistently evident in approximately one-third of all of our samples, and (ii) greater dishonesty was associated with older age. We discuss how our findings could be utilized by policy makers to better deter and detect dishonest behaviors during future similar crises.

Exploring transmission risk of different routes has major implications for epidemic control. However, disciplinary boundaries have impeded the dissemination of epidemic information, have caused public panic about "air transmission," "air-conditioning transmission," and "environment-to-human transmission," and have triggered "hygiene theater." Animal experiments provide experimental evidence for virus transmission, but more attention is paid to whether transmission is driven by droplets or aerosols and using the dichotomy to describe most transmission events. Here, according to characteristics of experiment setups, combined with patterns of human social interactions, we reviewed and grouped animal transmission experiments into four categories-close contact, short-range, fomite, and aerosol exposure experiments-and provided enlightenment, with experimental evidence, on the transmission risk of severe acute respiratory syndrome coronavirus (SARS-COV-2) in humans via different routes. When referring to "air transmission," context should be showed in elaboration results, rather than whether close contact, short or long range is uniformly described as "air transmission." Close contact and short range are the major routes. When face-to-face, unprotected, horizontally directional airflow does promote transmission, due to virus decay and dilution in air, the probability of "air conditioning transmission" is low; the risk of "environment-to-human transmission" highly relies on surface contamination and human behavior based on indirect path of "fomite-hand-mucosa or conjunctiva" and virus decay on surfaces. Thus, when discussing the transmission risk of SARS-CoV-2, we should comprehensively consider the biological basis of virus transmission, environmental conditions, and virus decay. Otherwise, risk of certain transmission routes, such as long-range and fomite transmission, will be overrated, causing public excessive panic, triggering ineffective actions, and wasting epidemic prevention resources.

The new coronavirus disease 2019 (COVID-19) has become a complex issue around the world. As the disease advancing and death rates are continuously increasing, governments are trying to control the situation by implementing different response policies. In order to implement appropriate policies, we need to consider the behavior of the people. Risk perception (RP) is a critical component in many health behavior change theories studies. People's RP can shape their behavior. This research presents a system dynamics (SD) model of the COVID-19 outbreak considering RP. The proposed model considers effective factors on RP, including different media types, awareness, and public acceptable death rate. In addition, the simplifying assumption of permanent immunity due to infection has been eliminated, and reinfection is considered; thus, different waves of the pandemic have been simulated. Using the presented model, the trend of advancing and death rates due to the COVID-19 pandemic in Iran can be predicted. Some policies are proposed for pandemic management. Policies are categorized as the capacity of hospitals, preventive behaviors, and accepted death rate. The results show that the proposed policies are effective. In this case, reducing the accepted death rate was the most effective policy to manage the pandemics. About 20% reduction in the accepted death rate causes about 23% reduction in cumulative death and delays at epidemic peak. The mean daily error in predicting the death rate is less than 10%.