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Limited research on food systems and food insecurity (FI) following disasters finds contextual differences in post-disaster food systems that shape dimensions of FI. Measurement limitations make it difficult to address FI and develop effective practices for disaster-affected communities. To develop, validate, and test a Disaster Food Security Framework (DFSF). Mixed-methods approach was used, including in-depth interviews to understand lived experiences during disasters; expert panel input to validate DFSF designed using responses from in-depth interviews; and quantitative testing of robustness of DFSF using the coronavirus disease 2019 pandemic as a disaster example. The in-depth interviews included participants from Vermont (n = 5), North Carolina (n = 3), and Oklahoma (n = 2) who had been living in those states during Hurricane Irene (2011), Hurricane Florence (2018), the Moore tornadoes (2013), and coronavirus disease 2019 pandemic (2020). The expert panel consisted of researchers and practitioners from different US geographical regions and food-related disciplines (n = 18). For the quantitative testing survey, data from 4 US states (New York, New Mexico, Vermont, and Maryland; n = 3,228) from the National Food Access and COVID Research Team was used. The outcomes from the in-depth interviews were dimensions of disaster FI, those from the expert panel was a content validity ratio, and those from the quantitative testing was the number of items and components to be included. Inductive and deductive reasoning were using when reporting on the in-depth interviews and expert panel results, including frequencies. The quantitative testing was conducted using multiple correspondence analysis. The in-depth interviews revealed four dimensions of FI: availability (supply and donation), accessibility (economic, physical, and social), acceptability (preference and health), and agency (infrastructure and self-efficacy). The panel of experts reported high content validity for the DFSF and its dimensions (content validity ratio >0.42), thus giving higher credibility to the DFSF. Multiple correspondence analysis performed on 25 food-related variables identified one component with 13 indicators representing three of the four dimensions: availability, acceptability, and accessibility, but not agency. [ABSTRACT FROM AUTHOR]

In 2020, an estimated 424,300 youth were arrested and nearly 128,000 youth were detained in the United States, with disproportionately higher rates for minoritized youth when compared to White youth.1 Although the number of youth in detention facilities has decreased since the late 1990s, a staggering number of children and adolescents enter the justice system each year. This remains a critical issue for child and adolescent psychiatrists. By the time youth enter juvenile detention, individual mental health needs are already exceptionally high. [ABSTRACT FROM AUTHOR]

Background : Numerous studies have reported existing disparities in academic medicine. The purpose of this study was to assess racial and gender disparity in academic emergency medicine (EM) faculty positions across the United States from 2007 to 2018. Objective : The primary objective was to identify the racial and ethnic and gender distributions across academic ranks in EM. The secondary objective was to describe the racial and gender proportions across different tenure tracks and degrees. Methods : We conducted a retrospective analysis using data from the Association of American Medical Colleges. Simple descriptive statistics and time series analysis were employed to assess the trends and relationship between race and gender across academic rank, type of degree, and tenure status. Results : When averaged, 75% of all faculty members were White physicians and 67.5% were male. Asian faculty members showed an increased representation in the lower academic ranks and underrepresented minority groups demonstrated a small increase. Asian faculty members demonstrated a significantly increasing trend at the level of instructor (t = 0.02; p = 0.034; 95% CI 0.05–1.03). Female faculty members showed a significantly decreasing trend over the study period (t = –0.01; p < 0.001; 95% CI 0.68–0.75). White academic physicians and male faculty members made up most of all degree types and tenure categories. Conclusions : Despite an increase in proportional representation, the underrepresentation of female faculty members and those from minority groups persists in emergency medicine. Further studies are needed to identify and address the root causes of these differences. [ABSTRACT FROM AUTHOR]

School nutrition programs mitigate child food insecurity across the United States. With the onset of the coronavirus disease 2019 (COVID-19) pandemic, kindergarten through grade 12 physical school campuses closed, which led to those programs transitioning to emergency feeding. The Sendai Framework for Disaster Risk Reduction has 4 action priorities that guided the assessment of school nutrition employees' emergency response during the COVID-19 pandemic. Our aim was to explore the experience of school nutrition employees as they provided emergency feeding services during the COVID-19 pandemic and evaluate their actions based on the Sendai Framework for Disaster Risk Reduction. A qualitative study with semi-structured interviews (n = 34) was conducted via videoconferencing software. A purposive sample of school nutrition employees across all 7 US Department of Agriculture regions who were involved in the COVID-19 emergency feeding response participated during April and May 2020. School nutrition employees were selected randomly for participation from those indicating willingness to be interviewed during their participation in a related survey. Recruitment continued until all 7 US Department of Agriculture regions were represented. Participants held various roles, ranging from state agency leaders to front-line supervisors, although most were district-level directors or assistant directors. Data were analyzed using a phenomenological qualitative analytic approach. Four themes emerged. First, participants described the progression of the initial shock associated with the pandemic and service disruption, the flexibility they practiced, and the development of new routines. Second, keeping people (children, coworkers, and the community) safe was highlighted. The next theme captured participants' feeling that they came "out of the shadows" as communities and stakeholders recognized their important contribution to children's food security. Finally, they shared insight on communication and accountability during the emergency feeding response. These programs demonstrated flexibility, resilience, and commitment to children during this crisis. As the pandemic continues and future disasters are considered, school nutrition programs and leaders can use the Sendai Framework for Disaster Risk Reduction's "build back better" concept to refine disaster preparedness plans and advocate for changes that will continue to combat child food insecurity in the United States. [ABSTRACT FROM AUTHOR]

• This study examines self-reported responses of 4,706 drivers to a fabricated message. • Female and young respondents highly expect to detour, change speed, or be distracted. • Familiarity with DMS increases the chance of detouring or changing speed. • Respondents expect to be distracted cognitively, visually, and manually. • Tech friendly respondents expect to be distracted more by taking pictures, texting, and calling. This article seeks to understand the potential response of drivers when they encounter compromised Dynamic Message Signs (DMS). The findings are built on the self-reported response of 4,706 participants in a Stated Preference (SP) survey conducted between November 2018 and December 2018 in the United States. The findings show the response of drivers to the "Downtown Under Terrorist Attack" message falls into route divergence, speed change, and distraction, and the likelihood of route divergence, distraction, and a slowdown is significantly more than either stopping or speeding up. The possibility of a response, however, varies depending on socioeconomic characteristics, attitudinal characteristics, and driving behavior. It is highlighted (1) female and young drivers are more probable to detour, to change speed, or to be distracted, (2) drivers who are familiar with DMS, encounter it frequently, or pay attention to its content, have a higher chance of detouring or changing speed, while they are less likely to be distracted, and (3) technology-friendly drivers are likely to detour or slow down. From the distraction model, it is further inferred that drivers are distracted cognitively, visually, and manually. The findings have implications for researchers and federal, state, and local agencies who are aware of the consequences of cybersecurity threats for the operation and profitability of the transport network. They, for example, assist transport planners in prioritizing equipment security efforts and resource allocation to the areas of greatest risk, and help to prepare contingency plans based on drivers' behavioral response. [ABSTRACT FROM AUTHOR]

In the United States, home health agencies (HHAs) provide essential services for patients recovering from post-acute care and older adults who are aging in place. During the COVID-19 pandemic, HHAs may face additional challenges caring for these vulnerable patients. Our objective was to explore COVID-19 preparedness of US HHAs and compare results by urban/rural location. Cross-sectional study. Using a stratified random sample of 978 HHAs, we conducted a 22-item online survey from April 10 to 17, 2020. Summary statistics were computed; open-ended narrative responses were synthesized using qualitative methods. Similar to national data, most responding HHAs (n = 121, 12% response rate) were for-profit and located in the South. Most HHAs had infectious disease outbreaks included in their emergency preparedness plan (76%), a staff member in charge of outbreak/disaster preparedness (84%), and had provided their staff with COVID-19 education and training (97%). More urban HHAs had cared for confirmed and recovered COVID-19 patients than rural HHAs, but urban HHAs had less capacity to test for COVID-19 than rural HHAs (9% vs 21%). Most (69%) experienced patient census declines and had a current and/or anticipated supply shortage. Rural agencies were affected less than urban agencies. HHAs have already rationed (69%) or implemented extended use (55%) or limited reuse (61%) of personal protective equipment (PPE). Many HHAs reported accessing supplemental PPE from state/local resources, donations, and do-it-yourself efforts; more rural HHAs had accessed these additional resources compared with urban HHAs. This survey reveals challenges that HHAs are having in responding to the COVID-19 pandemic, particularly among urban agencies. Of greatest concern are the declines in patient census, which drastically affect agency revenue, and the shortages of PPE and disinfectants. Without proper protection, HHA clinicians are at risk of self-exposure and viral transmission to patients and vulnerable family members. [ABSTRACT FROM AUTHOR]

A dynamic pre-positioning problem is proposed to efficiently respond to victims' need for relief supplies under uncertain and dynamic demand in humanitarian relief. The problem is formulated as a multi-stage stochastic programming model that considers pre-positioning with the dynamic procurement and return decisions about relief supplies over a time horizon. To validate the advantages of dynamic pre-positioning, three additional pre-positioning strategies are presented: pre-positioning with one-time procurement and without returns, pre-positioning with one-time procurement and returns, and pre-positioning with dynamic procurement and without returns. Using data from real-world disasters in the United States in the Emergency Events Database, we present a numerical analysis to study the applicability of the proposed models. We develop a sample average approximation approach to solving the proposed model in large-scale cases. Our main contribution is that we integrate dynamic procurement and return strategies into pre-positioning to decrease both costs and shortage risks in uncertain and dynamic contexts. The results illustrate that dynamic pre-positioning outperforms the other three strategies in cost savings. It also indicates that a higher return price is particularly helpful for decreasing unmet demand. The proposed models can help relief agencies evaluate and choose the solutions that will have the greatest overall effectiveness in the context of different relief practices. • A multi-stage stochastic programming model is proposed for dynamic pre-positioning. • Return is integrated and formulated as a dynamic decision. • Sample average approximation is designed for large-scale cases. • Storm, flood and wildfire data is collected from EM-DAT database for numerical analysis. [ABSTRACT FROM AUTHOR]

Curation is the process where one finds ways to search, filter, interpret, and share the message content. Such process is dynamically enacted within one's mediated and non-mediated social operating systems. Integrating and extending the frameworks of curation, this study examines the foundation, processes, and effects of personal curation in the context of disaster management. Results based on a multi-country survey in the United States, China, and Australia showed that the use of social media and mobile devices laid an important foundation of individuals' personal curation, which include consumptive (information gathering) and productive (information sharing) curation. Moreover, personal curation in normal times consistently predicted personal curation practiced during disasters, which in turn facilitated disaster preparedness. The conceptual model of personal curation was confirmed in all three countries in spite of variations in cultural and disaster situations. Theoretical and practical implications are discussed in ways to advance research on contemporary curation and adaptive behaviors. [ABSTRACT FROM AUTHOR]

• The Trauma Resilient Communities (TRC) Model provides a trauma-responsive, equity-based framework for organizations in responding to globally and nationally traumatic events. • In early stages of implementation, organizations report knowing about trauma-informed care but have gaps in understanding and applying trauma-informed care. • Conceptualizing the organization as a collective is critical for effective, sustainable, trauma-resilient implementation. Over the last decade, the term trauma-informed care has increased in popularity as human service organizations recognize the increasing need to serve individuals who have experienced trauma and adversity (Becker-Blease, 2017). One evidence-supported approach to trauma-informed organizational culture change is the Trauma Resilient Communities (TRC) Model. The current study focuses on the early experiences of leaders and followers within organizations implementing trauma-informed change during global and national traumatic events – the COVID-19 pandemic and racial trauma from police violence. Using a constant comparative method to thematic analysis, qualitative interviews were conducted with 19 backbone agency members trained in the TRC Model in Louisville, Kentucky. Findings indicate that in the early engagement stage of implementation, two key processes are critical: leadership buy-in and integration of the three organizational realms: clients, staff, and leaders. Implications are discussed and include recommendations for connecting the three realms of the organizational system, identifying capacity barriers, and utilizing the TRC Model to create equitable and trauma-resilient organizational culture. [ABSTRACT FROM AUTHOR]

According to the Federal Highway Administration, nonrecurring congestion contributes to nearly half of the overall congestion. Temporal disruptions impact the effective use of the complete roadway, due to speed reduction and rubbernecking resulting from primary incidents that in turn provoke secondary incidents. There is an additional reduction of discharge flow caused by secondary incident that significantly increases total delay. Therefore, it is important to sequentially predict the probability of secondary incidents and develop appropriate countermeasures to reduce the associated risk. Advanced computing techniques were used to easily understand and reliably predict secondary incident occurrences that have low sample mean and a small sample size. The likelihood of a secondary incident was sequentially predicted from the point of incident response to the eventual road clearance. The quality of predictions improved with the availability of additional information. The prediction performance of the principled Bayesian learning approach to neural networks ( bnn ) was compared to the Stochastic Gradient Boosted Decision Trees ( gbdt ). A pedagogical rule extraction approach, trepan , which extracts comprehensible rules from the neural networks, improved the ability to understand secondary incidents in a simplified manner. With an acceptable accuracy, gbdt is a useful tool that presents the relative importance of the predictor variables. Unexpected traffic congestion incurred by an incident is a dominant causative factor for the occurrence of secondary incidents at different stages of incident clearance. This symbolic description represents a series of decisions that may assist emergency operators by improving their decision-making capabilities. Analyzing causes and effects of traffic incidents helps traffic operators develop incident-specific strategic plans for prompt emergency response and clearance. Application of the model in connected vehicle environments will help drivers receive proactive corrective feedback before a crash. The proposed methodology can be used to alert drivers about potential highway conditions and may increase the drivers’ awareness of potential events when no rerouting is possible, optimal or otherwise. [ABSTRACT FROM AUTHOR]

The International Charter ‘Space and Major Disasters’ (“Charter”) is a collaboration among Earth observation satellite owners and operators to provide remotely sensed (RS) data following natural and man-made disasters. Charter members provided post-flood and pre-flood or archived RS data after the April 2011 floods in the Midwestern US. These multispectral and RADAR images provided valuable information to end users and first responders in the flood affected states for their planning and relief efforts. This effort was successful because of the process established by the Charter for soliciting requests, and acquiring and distributing data through a network of international and national agencies, and data vendors. This paper describes the a) role played by the Charter to acquire and distribute RS data through six of its members, b) value of archived satellite data, c) need for image processing experts to process these near real-time data, and transfer them in a timely manner, and d) data acquisition and distribution policies of the Charter. Lessons learned from this response along with the policy recommendations will be helpful in future activations for maximizing the value of RS data and services provided by the Charter. [ABSTRACT FROM AUTHOR]

Background: Emergency medicine personnel frequently respond to major disasters. They expect to have an effective and efficient management system to elegantly allocate available resources. Despite claims to the contrary, experience demonstrates this rarely occurs.Objectives: This article describes privatizing disaster assessment using a single-purposed, accountable, and well-trained organization. The goal is to achieve elegant disaster assessment, rather than repeatedly exhorting existing groups to do it.Discussion: The Rapid Disaster Evaluation System (RaDES) would quickly and efficiently assess a postdisaster population's needs. It would use an accountable nongovernmental agency's teams with maximal training, mobility, and flexibility. Designed to augment the Inter-Agency Standing Committee's 2015 Emergency Response Preparedness Plan, RaDES would provide the initial information needed to avoid haphazard and overlapping disaster responses. Rapidly deployed teams would gather information from multiple sources and continually communicate those findings to their base, which would then disseminate them to disaster coordinators in a concise, coherent, and transparent way.Conclusions: The RaDES concept represents an elegant, minimally bureaucratic, and effective rapid response to major disasters. However, its implementation faces logistical, funding, and political obstacles. Developing and maintaining RaDES would require significant funding and political commitment to coordinate the numerous agencies that claim to be performing the same tasks. Although simulations can demonstrate efficacy and deficiencies, only field tests will demonstrate RaDES' power to improve interagency coordination and decrease the cost of major disaster response. At the least, the RaDES concept should serve as a model for discussing how to practicably improve our current chaotic disaster responses. [ABSTRACT FROM AUTHOR]

Disaster debris management operations make up a significant portion of recovery expenses. The following study aims to examine how the presence of a plan makes disaster debris management effective and efficient. Ninety-five counties in the United States who received major disaster declarations between 2012 and 2015 were surveyed to examine the quality of their debris management processes. Forty-nine of these counties had debris management plans while forty-six did not. Statistical tests were conducted to address discrepancies in the effectiveness and efficiency of the debris management processes between the two groups. Such tests suggest that counties with pre-disaster debris management plans were more effective. These counties recycled almost twice as much disaster debris as counties without plans, and received over three times as much Public Assistance from the Federal Emergency Management Agency (FEMA). Counties with plans also reported higher levels of perceived preparedness for future debris challenges than counties without plans. Overall, counties with pre-disaster debris management plans were partially more efficient than counties without plans. They removed more cubic yards of debris per day, but there were no statistically significant differences between the two groups in the volume of debris removed per dollar. [ABSTRACT FROM AUTHOR]

"Consumer-Directed" Health Plans (CDHPs), those with high deductibles and personal medical accounts, are intended to reduce health care spending through greater patient cost exposure. Prior research agrees that in the first year, CDHPs reduce spending. There is little research and in it results are mixed regarding the impact of CDHPs over the longer term. We add to this literature with an intent-to-treat, difference-in-differences analysis of health care spending over up to three years post CDHP offer among 13 million person-years of data from 54 large US firms, half of which offered CDHPs. To strengthen the identification, we balance observables over time within firm, by developing weights through a machine learning algorithm, generalized boosted regression. We find that spending is reduced for those in firms offering CDHPs in all three years post offer relative to firms continuing to offer lower-deductible plans. The reductions are driven by spending decreases in outpatient care and pharmaceuticals, with no evidence of increases in emergency department or inpatient care over the three-year window. [ABSTRACT FROM AUTHOR]

This study examines how Web 2.0 applications were used during a catastrophic wildfire in the Western United States that claimed two human lives, more than 18,000 acres of land and nearly 350 homes. The study sheds light on how Web 2.0 applications were applied as a tool to transmit information while the disaster was unfolding. This research highlights unique nonprofit cases that inform the role and reliability of Web 2.0 applications during a crisis, and the roles that nonprofit organizations and the general public play while facing a dire emergency. In the cases presented, Web 2.0 applications served as a bridge between first responders, the population in immediate wildfire danger, and the citizens who were trying to help, resulting in saved lives, property, and natural resources. By combining existing literature and collected qualitative data, the researchers argue that Web 2.0 applications represent flexible communication tools for transmission of timely information during a crisis situation. [ABSTRACT FROM AUTHOR]

Nursing homes (NHs) provide care in a congregate setting for residents at high risk of severe outcomes from SARS-CoV-2 infection. In spring 2020, NHs were implementing new guidance to minimize SARS-CoV-2 spread among residents and staff. To assess whether telephone and video-based infection control assessment and response (TeleICAR) strategies could efficiently assess NH preparedness and help resolve gaps. We incorporated Centers for Disease Control and Prevention COVID-19 guidance for NH into an assessment tool covering 6 domains: visitor restrictions; health care personnel COVID-19 training; resident education, monitoring, screening, and cohorting; personal protective equipment supply; core infection prevention and control (IPC); and communication to public health. We performed TeleICAR consultations on behalf of health departments. Adherence to each element was documented and recommendations provided to the facility. Health department–referred NHs that agreed to TeleICAR consultation. We assessed overall numbers and proportions of NH that had not implemented each infection control element (gap) and proportion of NH that reported making ≥1 change in practice following the assessment. During April 13 to June 12, 2020, we completed TeleICAR consultations in 629 NHs across 19 states. Overall, 524 (83%) had ≥1 implementation gap identified; the median number of gaps was 2 (interquartile range: 1-4). The domains with the greatest number of facilities with gaps were core IPC practices (428/625; 68%) and COVID-19 education, monitoring, screening, and cohorting of residents (291/620; 47%). TeleICAR was an alternative to onsite infection control assessments that enabled public health to efficiently reach NHs across the United States early in the COVID-19 pandemic. Assessments identified widespread gaps in core IPC practices that put residents and staff at risk of infection. TeleICAR is an important strategy that leverages infection control expertise and can be useful in future efforts to improve NH IPC. [ABSTRACT FROM AUTHOR]

Hurricanes are among the most devastating natural disasters in the United States, causing billions of dollars of property damage and insured losses. During extreme wind events, unsecured objects in jobsites can easily become airborne debris, which results in substantial loss to construction projects and neighboring communities. Towards a systematic disaster preparedness in construction jobsites, this paper presents a novel vision-based digital twinning and threat assessment framework. We encode the context of disaster risk into deep-learning architectures to identify and analyze the characteristics and impacts of potential wind-borne debris in construction site digital twin models. Case studies on nine piles of construction materials are presented to demonstrate and discuss the fidelity of the proposed computational modules. The proposed methods are expected to help provide heads up for practitioners to quickly recognize, localize, and assess potential wind-borne derbies in construction jobsites, and thereby implementing hurricane preparedness in an effective and timely manner. • A new AI-based framework for construction site disaster preparedness. • Digital twinning of potential wind-borne debris and 3D point cloud segmentation. • Case studies were conducted to evaluate the performance of the proposed method. • Potential for effective risk-informed decision making for disaster preparedness. [ABSTRACT FROM AUTHOR]

To inform future policies and disaster preparedness plans in the vulnerable nursing home setting, we need greater insight into the relationship between nursing homes' (NHs') quality and the spread and severity of COVID-19 in NH facilities. We therefore extend current evidence on the relationships between NH quality and resident COVID-19 infection rates and deaths, taking into account NH structural characteristics and community characteristics. Cross-sectional study. 15,390 Medicaid- and Medicare-certified NHs. We obtained and merged the following data sets: (1) COVID-19 weekly data reported by each nursing home to the Centers for Disease Control and Prevention's National Healthcare Safety Network, (2) Centers for Medicare & Medicaid Services Five Star Quality Rating System, (3) county-level COVID-19 case counts, (4) county-level population data, and (5) county-level sociodemographic data. Among 1-star NHs, there were an average of 13.19 cases and 2.42 deaths per 1000 residents per week between May 25 and December 20, 2020. Among 5-star NHs, there were an average of 9.99 cases and 1.83 deaths per 1000 residents per week. The rate of confirmed cases of COVID-19 was 31% higher among 1-star NHs compared with 5-star NHs [model 1: incidence rate ratio (IRR) 1.31, 95% confidence interval (CI) 1.23-1.39], and the rate of COVID-19 deaths was 30% higher (IRR 1.30, 95% CI 1.20, 1.41). These associations were only partially explained by differences in community spread of COVID-19, case mix, and the for-profit status and size of NHs. We found that COVID-19 case and death rates were substantially higher among NHs with lower star ratings, suggesting that NHs with quality much below average are more susceptible to the spread of COVID-19. This relationship, particularly with regard to case rates, can be partially attributed to external factors: lower-rated NHs are often located in areas with greater COVID-19 community spread and serve more socioeconomically vulnerable residents than higher-rated NHs. [ABSTRACT FROM AUTHOR]

Abstract: Industrial fire and explosion hazards due to accidents in fuel storage units have gained a considerable attention in recent years. Both, the regulatory bodies and scientific communities are heavily concerned about the proper safety measures to avoid such calamities in the future. This paper aims to bring some essentials related to the hazards that arose from recent fuel storage fire accidents occurring in Buncefield, UK (2005), Puerto Rico, USA (2009) and Sitapura, India (2009). The potential similarities behind occurrence of these accidents are studied. The applicability of various methods (models) and also computer simulations to estimate the safety distances according to the international standards for both explosion and fire hazard are verified. The overpressures caused by the Vapor Cloud Explosions (VCE) and radiation fluxes emitted by the fires are considered for respective explosion and fire hazard estimations. The prime focus is placed on the regulations laid down by the National Fire Protection Agency of the United States and the European Norms. [Copyright &y& Elsevier]

Abstract: Considerable attention is focused on plans for sheltering or evacuating the population of the US national capital region in response to a regional emergency such as a terrorist attack or natural disaster. Such planning engages multiple disciplines spanning infrastructure engineering, emergency management, health care, mass communication, water and food supply, logistics, and others. Knowledge of population behaviors should influence the many dimensions of protection, prevention, response, and recovery. Of particular interest are the behaviors and needs of the resident and non-resident populations in the aftermath of a regional disaster, including those at home, at work, and traveling. The authors deployed a 30-min telephone survey to 2700 residents of the region to gain knowledge of their intended behaviors in the event of a variety of potential dirty bomb attacks. The survey provides a unique foundation for the current paper. The paper will identify and model the assumptions of population behaviors that most affect agency priorities for emergency planning including regional sheltering and evacuation following a radiological disaster such as a dirty bomb. The technical approach assessed several planning initiatives across performance criteria derived from strategic plans and applied combinations of behavioral assumptions to vary the relative importance of each criterion. The results reveal the behavioral scenarios that are most significant to the prioritization of planning initiatives and identify the highest and lowest priority initiatives across the criteria used. [Copyright &y& Elsevier]

Abstract: We have tested a linear regression model to identify significant predictors of pre-evacuation delay in a sample of evacuees enrolled in the World Trade Center Evacuation Study. We have found that pre-evacuation delay was greater when there were more environmental cues, more seeking out of information, and more pre-evacuation actions. Additionally, higher perceived risk was predictive of shorter pre-evacuation delay times. These findings are compared and contrasted with an analysis of participants in the National Institute of Standards and Technology investigation of the World Trade Center disaster, recently reported by Kuligowski and Mileti (2009). Both studies reported factors associated with pre-evacuation delay that were similar to those associated with community evacuation. Additionally, we found that greater knowledge and greater emergency preparedness were associated with greater perception of risk. Greater emergency preparedness was negatively related to pre-evacuation delay within World Trade Center Tower I, but within World Trade Center Tower II, the relation between emergency preparedness and pre-evacuation delay was positive. These findings have implications for training of occupants of high-rise buildings. [Copyright &y& Elsevier]

Abstract: The field of disaster risk management is relatively new and takes a structured approach to managing uncertainty related to the threat of natural and man-made disasters. Disaster risk management consists primarily of risk assessment and the development of strategies to mitigate disaster risk. This paper will discuss how increasing both Earth observation data and information technology capabilities can contribute to disaster risk management, particularly in Belize. The paper presents the results and recommendations of a project conducted by an international and interdisciplinary team of experts at the 2009 session of the International Space University in NASA Ames Research Center (California, USA). The aim is to explore the combination of current, planned and potential space-aided, airborne, and ground-based Earth observation tools, the emergence of powerful new web-based and mobile data management tools, and how this combination can support and improve the emerging field of disaster risk management. The starting point of the project was the World Bank’s Comprehensive Approach to Probabilistic Risk Assessment (CAPRA) program, focused in Central America. This program was used as a test bed to analyze current space technologies used in risk management and develop new strategies and tools to be applied in other regions around the world. [ABSTRACT FROM AUTHOR]

Abstract: Through a national survey of counties (n =435) this study benchmarks how counties communicate disaster information to Spanish-speaking Hispanics. The study found that most counties employ at least one channel to communicate Spanish-language disaster information. Counties, however, believe that the Federal Emergency Management Agency and state emergency management agencies are most responsible and capable of developing and providing Spanish-language disaster information. [Copyright &y& Elsevier]

Abstract: As coastal disasters become more frequent and costly, a full assessment of costs becomes more important. This paper aims to identify costs of coastal disasters to human, social, built and natural capital and their associated services at the local site of a disaster and in the regions and nations that respond for relief and recovery. The spatial and temporal magnitude and scale of costs is captured differently in typical cost accounting and a more comprehensive approach, full-cost accounting. The difference between these approaches will be demonstrated using Hurricane Katrina (2005) as a case study, though we do not attempt to perform a full-cost accounting of this actual event. We examine how disaster planning and preparedness becomes more cost effective when the full cost of disasters is calculated. A full-cost accounting of coastal disasters sets the stage for rigorous comparisons of strategies for post-disaster development. The rudimentary analysis of this paper indicates that continued population development as well as the maintenance of current settlements in particular regions along the coasts may not be in the national interest. In this way, full-cost accounting could help reduce vulnerability to future disasters. [Copyright &y& Elsevier]

Abstract: A survey of U.S. public relations practitioners (n =126) found that three-quarters of their employer organizations had a written crisis communications plan and that organizations, as a whole, were reasonably prepared to engage in crisis communications. Preparedness was measured based on the presence of a crisis plan as well as indices related to tactics, training, the maintenance of contact lists, and media monitoring. Preparedness was found to be positively correlated to organization size, the level of autonomy, and delegation of authority within the organization, and the process orientation of the organization, but not organization type nor involvement in international versus domestic-only operations. Practitioners from organizations with plans had lower assessments of their relationships with publics, but greater confidence in their ability to respond. [Copyright &y& Elsevier]

Abstract: Discussions at the national level about critical infrastructure and its vulnerability are more common now following the events of September 11. The National Emergency Supply Agency organised a training exercise to test the logistics of food supply following passage of a plume containing radioactive material over a district in Finland. This short paper summarises the problems that had to be faced and suggests a way forward for improving emergency preparedness in this area. [Copyright &y& Elsevier]

Examines the use of public relations, particularly situational theory of publics, for assessing response to the New Madrid earthquake prediction affecting the New Madrid region of central United States. Features of situational theory; Background to the New Madrid earthquake prediction; Public response to earthquakes; Demographic descriptors of situational publics.

• Construct a method to determine the release category using backpropagation neural network (BPNN) classifier. • Optimise Neural Network using Genetic algorithm (GA) and simulated annealing (SA). • Complete the missing monitoring data using Particle swarm optimisation (PSO). After a severe nuclear accident, the source term is typically unknown. Therefore, great importance is attached to obtaining source term information for subsequent emergency planning. The purpose of the study involved performing a rapid determination of the release category using the gamma dose rate monitoring data over a short period during an accident. The release categories PWR1–PWR9 in 'The Reactor Safety Study' of the United States were used as a reference. The International Radiological Assessment System (InterRAS) was used to construct a nuclear accident model and generate the required simulation data. After a series of experiments, appropriate parameters were selected to construct the backpropagation neural network (BPNN) classifier to estimate the release category. The genetic algorithm (GA) and simulated annealing (SA) algorithm were used to search for the weights and thresholds of the BPNN classifier in advance, and this avoided the problem wherein bad initial values can cause the classifier to fall into local minimums, decreased training time, and improved prediction accuracy from 98.36% to 99.10%. With respect to the possible absence of gamma dose rate monitoring data, particle swarm optimisation (PSO) was used to complete the missing data, thereby ensuring that the classifier can normally predict the release category. After testing, in the absence of 4 of the total 16 gamma dose rate data, the classifier can still maintain a prediction accuracy of more than 80%. [ABSTRACT FROM AUTHOR]

Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster management is still challenging. Sentiment analysis of social media data helps detect the concerns of affected people and understand individuals' responses on the ground at unprecedented scales, but the method is known to be domain-dependent. The same words or expressions can indicate opposite sentiments in different domains. This paper proposes a domain-specific sentiment analysis approach specifically for tweets posted during hurricanes (DSSA-H). DSSA-H can retrieve hurricane-relevant tweets with a trained supervised-learning classifier, Random Forest (RF), and classify the sentiment of hurricane-relevant tweets based on a domain-adversarial neural network (DANN). We built a dataset of tweets posted during six recent hurricanes and applied the DSSA-H approach for sentiment analysis. After evaluation, we found that each classifier (i.e., RF and DANN) outperforms baseline classifiers and that DSSA-H outperforms two high-performing general sentiment classification approaches when classifying sentiments of tweets posted during hurricanes. We also applied DSSA-H in examining sentiment patterns across six recent hurricanes in the U.S. This domain-specific sentiment analysis approach can be used by the first responders and affected communities to more accurately and rapidly detect crises and emergent events, allocate resources, and assess disaster's impact during hurricanes. DSSA-H contributes to an intelligent and adaptive disaster information system for the data-rich human and the built environment system. • DSSA-H retrieves meaningful tweets relevant to hurricanes precisely. • DSSA-H applies a recurrent neural network with domain adaption to classify sentiments. • DSSA-H outperforms general sentiment analysis approaches in classifying hurricane-relevant tweets. • DSSA-H is useful to detect small-scale crises that trigger negative sentiments in hurricanes. • DSSA can be trained and applied to sentiment analysis of short blogs related to other extreme events. [ABSTRACT FROM AUTHOR]

Abstract: Local emergency managers are charged by the U.S. Department of Homeland Security to increase the number of community members with emergency plans and kits. This outcome remains unrealized in part based upon in-depth interviews with 37 emergency managers in eight states revealing that emergency managers conceptualize communication as dissemination rather than as a process of research and evaluation. This study calls for local emergency managers to conduct outcome evaluations of risk communication campaigns to increase public participation. [Copyright &y& Elsevier]

• Successful disaster management requires data describing disaster impact and extent of damage. • Aerial data collection is resource-intensive and requires offline post-processing. • We introduce convolutional neural network (CNN) models for ground object detection. • The models recognize building roofs, cars, vegetation, debris, and flooded areas. • We achieve 80.69% and 74.48% mAP for high and low altitude footage, respectively. Accurate and timely access to data describing disaster impact and extent of damage is key to successful disaster management (a process that includes prevention, mitigation, preparedness, response, and recovery). Airborne data acquisition using helicopter and unmanned aerial vehicle (UAV) helps obtain a bird's-eye view of disaster-affected areas. However, a major challenge to this approach is robustly processing a large amount of data to identify and map objects of interest on the ground in real-time. The current process is resource-intensive (must be carried out manually) and requires offline computing (through post-processing of aerial videos). This research introduces and evaluates a series of convolutional neural network (CNN) models for ground object detection from aerial views of disaster's aftermath. These models are capable of recognizing critical ground assets including building roofs (both damaged and undamaged), vehicles, vegetation, debris, and flooded areas. The CNN models are trained on an in-house aerial video dataset (named Volan2018) that is created using web mining techniques. Volan2018 contains eight annotated aerial videos (65,580 frames) collected by drone or helicopter from eight different locations in various hurricanes that struck the United States in 2017–2018. Eight CNN models based on You-Only-Look-Once (YOLO) algorithm are trained by transfer learning, i.e., pre-trained on the COCO/VOC dataset and re-trained on Volan2018 dataset, and achieve 80.69% mAP for high altitude (helicopter footage) and 74.48% for low altitude (drone footage), respectively. This paper also presents a thorough investigation of the effect of camera altitude, data balance, and pre-trained weights on model performance, and finds that models trained and tested on videos taken from similar altitude outperform those trained and tested on videos taken from different altitudes. Moreover, the CNN model pre-trained on the VOC dataset and re-trained on balanced drone video yields the best result in significantly shorter training time. [ABSTRACT FROM AUTHOR]

Presents a telephone survey examination of food-related coping strategies in Floridian households after Hurricane Andrew. Average respondent; Food-purchasing problems after Hurricane Andres; Changes in food preparation behavior; Changes in kitchen cleanup; Newly acquired knowledge about meeting food-related needs in a hurricane situation.