Emergency Management Assistance Research Articles

Two-stage evolutionary game model on complex networks for emergency logistics based on blockchain platform

Enhancing the efficiency of emergency logistics is essential to reinforce the response capacity to emergencies. It requires collaborative efforts from both the emergency management departments and logistics enterprises. However, the capability for information docking between these organizations substantially influences the efficiency of emergency materials transportation. Blockchain, as an emerging information technology, has the potential to substantially enhance the efficiency and security of information docking through its unique characteristics. Additionally, logistics enterprises share information and resources based on the blockchain platform, aiming to ensure fast, efficient, and reliable collaborative transportation of emergency materials to impacted regions. Therefore, we propose a two-stage evolutionary game analysis of emergency materials collaborative transportation based on this platform. First, we develop a first-stage evolutionary game model to analyze the challenges associated with building a blockchain platform among emergency management departments and logistics enterprises. Subsequently, a second-stage evolutionary game model is devised to bolster the willingness of logistics enterprises based on this platform for collaborative transportation of emergency materials. Third, to validate the theoretical findings of the two-stage game model, two scale-free networks are employed for numerical simulations. As those networks evolve, the edges among nodes and the strategy choices of nodes in those networks likewise change. Numerical simulations are performed based on those networks to scrutinize the sensitivity of factors influencing strategic choice among game stakeholders. Ultimately, certain management insights gleaned from the findings can assist emergency management departments and logistics enterprises in making more informed decisions in the future.

Sparse sensing to dense assessment: Incorporating spatial autocorrelation for assessing flood impacts.

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.

The Evaluation of the Departmental Resources and Positions Within the Inter-Departmental Collaborative Networks: A Case Study on the Joint Prevention and Control Mechanism in China.

This study examines the inter-departmental emergency response dynamics within China's Joint Prevention and Control Mechanism (JPCM). Departments' positions in the network are fundamental to understanding the overall structure and operation of the collaborative emergency response. Moreover, understanding the influence of departmental resources on departmental positions promotes efficient inter-departmental collaboration. The study uses regression analysis to empirically investigate the departmental resources to departments' participation in the JPCM collaboration. The independent variable adopts the departments' positions by statistically presenting the departments' centrality using social network analysis. The dependent variables employ departmental resources, including departmental duties, staffing levels, and approved annual budgets based on data from the government website. The result of social network analysis shows that in JPCM inter-departmental collaboration, the Ministry of Transport, Health Commission, Ministry of Public Security, Ministry of Emergency Management, Ministry of Culture and Tourism, Ministry of Education, and Development and Reform Commission are mainly involved. The regression analysis result indicates that the department's engagement in collaborative actions is determined and influenced by the department's statutory duties. The more statutory duties department has, the more it is in a critical position and role for JPCM. The study can assist emergency management practitioners and academic departments in utilizing evidence-based approaches to justify the collaboration and involvement of participating departments. The analysis of the collaborative networks considering JPCM in China by the participation and organization logic is of fundamental significance for arguing for complementing COVID emergency management and inter-departmental emergency collaboration studies.

Predicting social media rumours in the context of public health emergencies

The spread of rumours on social media in the context of public health emergencies often distorts perceptions of public events and obstructs crisis management. Microblog entries about 28 rumour cases are collected on Sina Weibo during the COVID-19 outbreak. The Modality–Agency–Interactivity–Navigability model is used to identify the key factors of rumour prediction. To investigate the relationship among information modality, information content, information source and rumour identification, the binary logistic regression model is established based on the features of users and microblog entries. In addition, we propose a multi-feature rumour prediction model based on the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models. The proposed rumour prediction model has the best performance compared with other models. The feature importance is then calculated by the SHapley Additive exPlanations (SHAP), which can also explain the XGBoost results. It is shown that the likelihood that microblog entries are rumours decreases as the values of variables such as user influence and the positive sentiment of comments rise. Microblog entries posted on Thursdays or at noon are more probably to be rumours than those posted at other time. The proposed model can assist emergency management departments in establishing a feasible rumour prediction mechanism to guide public opinion against rumours.

Towards comprehensive regional resilience evaluation, resistance, recovery, and creativity: From the perspective of the 2008 Wenchuan Earthquake

Regional resilience is the ability to resist, recover, and redevelop (creativity) after a disaster. However, few comprehensive pre-and post-disaster analyses based on resilience indicators have been conducted. This research used ordination and clustering analyses to develop a comprehensive framework that included a multi-indicator horizontal analysis and an uni-indicator longitudinal recovery trend analysis. Novel resilience evaluation criteria and a resilience score that accounted for both short-term resistance and long-term recovery and creativity are also proposed. The framework was then applied to data from 55 counties in Sichuan Province, China, before and after the 2008 Wenchuan Earthquake (2005–2016). It was found that: (i) after the Wenchuan Earthquake, the economic and public facility-related indicators first underwent a one to three-year recovery process after which there was a period of steady redevelopment; (ii) the more economically developed regions had higher resilience; and (iii) compared with the average for the more developed regions, the underdeveloped regions appeared to have better catastrophic earthquake resistance and recovery capabilities. This research provides a comprehensive quantitative regional resilience evaluation, which could assist emergency managers and decision-makers in improving disaster resilience.

Implementation Science for Managers and Healthcare Organizations Responding to Emergencies

The purpose of the article is to illustrate how implementation science concepts and methods can be applied by researchers and implementers to understand and assist emergency management in a large primary and community healthcare organization. The article refers to a single-case implementation action evaluation of an emergency management system in a healthcare organization. It describes the methods used in this study and findings to explain how a joint healthcare and university research team were able to use the science and methods both to help implementation and contribute to science. We report two sets of findings. First, findings about implementation of emergency management to illustrate how the investigation adapted implementation science and concepts to achieve the objectives evaluation. We discovered that implementation science provides useful concepts to understand contextual factors and adds to knowledge about organizational change and emergency management in the uncertain and evolving situation we encountered. The second set of findings are the strengths and limitations of both implementation science and the action evaluation methods we used to achieve the dual objectives of practical help with implementation and to contribute to science. The article uses the first implementation action evaluation study of the response of large public primary and community healthcare organization to a pandemic to illustrate how implementation science can be applied. This type of study was able to improve implementation of the response as well as contribute to scientific knowledge about emergency healthcare management and organization.

Knowledge-Based Decision Support System for Emergency Management: The Pandemic Framework

Emergency management systems (EMS) assist emergency managers to resolve emergencies on hand, through analyzing the emergency characteristics and consolidating data from different departments that are involved in resolving the emergency. Many countries have adopted various forms of EMS that are specialized in resolving one type of emergency, and studies demonstrate their effectiveness in producing better decisions. However, the COVID-19 pandemic uncovered the lack of a comprehensive framework that could deal with different emergencies. It also revealed the inability of the current systems to communicate with each other to retrieve the needed data. The aim of this study is to show the current state of EMS in emergency departments by constructing a framework for a knowledge-based decision support system for emergency management focusing on resolving pandemics. Qualitative approach was adopted in this research, where the authors reviewed emergency management in general and pandemics in specific. Existing EMS systems were investigated, and knowledgebased decision support systems were explored. Approaches for integration, communication, and collaboration were also studied. As a result of this study, a comprehensive framework, i.e., a knowledgebased decision support system for emergency departments, focusing on resolving pandemics was introduced. The framework was validated by a domain expert who provided insights and suggestions for future research. While the primary research focus is to assist emergency managers in resolving the COVID-19 pandemic, the proposed framework is unique by adopting different approaches and techniques that enable the system to deal with various emergencies not limited to the current pandemic.

Geophysical and Social Influences on Evacuation Decision-Making: The Case of Hurricane Irma

Understanding the factors that influence evacuation decision-making among local residents is of critical importance to those involved in monitoring and managing weather-related hazards. This study examined both geophysical and social variables that we believe influenced individual decision-making on whether to stay home, seek out a public shelter, or leave the area entirely during Hurricane Irma. A 23-item survey was administered to a convenience sample of adults (n = 234) who resided within a coastal Florida county that received an evacuation warning during Hurricane Irma in 2017. Results suggested sources of information relied on through media, government, family, and social networks contributed to differences in evacuation behavior. Moreover, potential exposure to weather-related conditions, such as flooding and strong winds, along with the likelihood to use available social resources, also influenced decisions to stay or leave the threatened area. Finally, prior evacuation behavior was significantly associated with the decision to evacuate during Hurricane Irma. The decision to evacuate for Hurricane Irma was shown to impact decision-making to evacuate for major hurricanes in the future. If these findings are applied to future storms, a broader conclusion can be made that residents in vulnerable areas may be more likely to evacuate for major hurricanes than they were in the past. Improved understanding of evacuation decision-making can assist emergency managers in preparation and planning to reduce casualties resulting from a hurricane or other weather-related hazard.

Application of 2D numerical simulation for rating curve development and inundation area mapping: a case study of monsoon dominated Dwarkeswar river

ABSTRACT While the implementation of real-time flood prediction and warning systems based on real-time continuous rainfall and streamflow data collection has been implemented in a number of river basins in a number of countries such systems require a reliable network of telemetered rainfall and streamflow gauges within the river basin. Typical of a developing country, the southern part of West Bengal has very few gauging stations with, most stations recording only gauge height data. There is a significant lack of local-level information on rating curves and inundation mapping which can be used for flood management purposes and to identify under what conditions areas of the floodplain need to be evacuated to protect human life. Under these circumstances, stand-alone flood modelling can be undertaken to generate rating curves at key locations and inundation maps which can be used during floods to assist emergency managers implement management actions and to decide when to evacuate communities at risk from rising floodwaters. In this study, the monsoon dominated Dwarkeswar River near Arambag Town, Hooghly District, West Bengal was selected for the prediction of a rating curve and inundation area. The August 2016 flood was simulated using a HEC-RAS (Version 5.0.7) rain-on-grid model. The simulated rating curve and inundation area were validated against the river stage data and flooded area observed in the same year. It was concluded that the model was able to simulate the rating curve and inundation area with an acceptable level of accuracy and that this demonstrated the feasibility of applying this approach to other river basins with a limited number of gauges in order to improve flood management and evacuation practices.

Still struggling: intersectionality, vulnerability, and long-term recovery after the Tuscaloosa, Alabama USA Tornado

ABSTRACT This research explores the impact of the Tuscaloosa tornado on the long-term financial and emotional well-being of survivors 4 to 5 years after the event. Using social stress theory and an intersectional analytic approach, this research utilizes in-depth interviews to assess the recovery experiences of survivors. Findings reveal that the tornado resulted in qualitatively different emotional and financial impacts for marginalized Tuscaloosa, Alabama tornado survivors compared to their better situated counterparts, and created unique challenges for Latina survivors. Findings from this analysis can assist emergency managers and service providers in planning for long-term recovery for citizens from multiple diverse backgrounds.

A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments

Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real‐time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC‐SMA) and an adaptive neuro‐fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24‐hr lead times show strong performance for SAC‐SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Emergency preparedness collaboration on Twitter

Agency collaboration is an important function in the management of disasters and catastrophes. For effective emergency management, the need for intergovernmental collaboration grows as the scale of the disaster increases. Several researchers have examined the use of social media by emergency management (and other governmental agencies) during large-scale disasters; however, few have examined the use of social media for intergovernmental collaboration. This study explores the use of social media platforms as a means to establish and maintain intergovernmental collaboration for emergency management-related agencies. More salient is the focus on social media during the preparedness and planning stages of emergency management. Using qualitative observational and coding analysis, this study identifies the types of connections made by topic, level of governance, and established affiliation in the local emergency operations plan (LEOP). The findings show that more than 50 percent of the connections made were established on Twitter and not present in the current LEOP. Furthermore, the most popular topic to initiate online connections was related to public education information. The findings from this study can assist emergency management practitioners in developing social media strategies, which incorporate methods to connect with other agencies on Twitter.

Radiological Preparedness in the Land of Lincoln.

The paper will provide a brief overview of past preparedness efforts in Illinois with special emphasis on the Chicago urban area for an improvised nuclear device (IND) event, current status, and near- and long-term plans to address this daunting challenge. The planning assumption involved the detonation of a 10 kT IND in downtown Chicago at noon on a workday. Major efforts began in 2010 involving over 300 federal, state, and local public and private entities. The planning efforts included three summits and 16 workshops that produced essential response planning documents. Many capability gaps were identified during the planning process, and all essential major tasks were identified. Task sequencing, interdependencies of the task sequencing, time-phased task sequencing, and resource constrained time-phased task sequencing were reviewed in detail by the various working groups, and a draft integrated response plan and associated appendices were prepared. A Federal Emergency Management Agency (FEMA) Region V integrated IND Senior Leaders Tabletop Exercise involving six major tasks of the draft plan was conducted on 5 December 2013. Although all six tasks were performed "with some challenges," much more preparedness work is still needed. Future preparedness efforts regarding INDs and radiological dispersal devices (RDDs) will be described. One of the major capability deficiencies noted during the planning process is the lack of adequate technical resources necessary to provide assistance and support for response, recovery, and population monitoring. This paper will highlight two of the ongoing national efforts to address this deficiency. Specifically, these programs are: (1) Radiological Operations Support Specialist (ROSS) initiative, which is a collaboration of the U.S. Department of Homeland Security's FEMA, U.S. Department of Energy's National Nuclear Security Administration (NNSA) and the Conference of Radiation Control Program Directors, Inc. (CRCPD); and (2) the Radiation Response Volunteer Corps Program of the CRCPD and the Centers for Disease Control and Prevention. Ultimately, these efforts will aid in providing the requisite resources in a manner consistent with the existing Emergency Management Assistance Compact currently in place for all hazards.

Seismic Risk Assessment of New Zealand Unreinforced Masonry Churches using Statistical Procedures

ABSTRACTThe 2010–2011 Canterbury earthquake sequence provided extensive evidence of the significant seismic vulnerability of New Zealand unreinforced masonry (URM) churches. Given the high seismicity of the country, the exposure of human lives and the societal significance of ecclesiastic buildings, for both historical and religious reasons, the reduction in seismic vulnerability of this building type is of primary importance. By analyzing the seismic performance of a sample of 80 affected buildings, regression models correlating mean damage levels against ground-motion parameters were developed for observed collapse mechanisms, accounting for vulnerability modifiers whose influence was estimated via statistical procedures. Considering the homogeneity of New Zealand URM churches, the vulnerability models developed for the Canterbury region were extended to the whole country inventory, and a synthetic index was proposed to summarise damage related to several mechanisms. Territorial scale assessment of the seismic vulnerability of churches can assist emergency management efforts and facilitate the identification of priorities for more in-depth analysis of individual buildings. After proper calibration, the proposed approach can be applied to other countries with similar building heritage.

Households\u2019 Risk Perception and Behavioral Responses to Natech Accidents

This study analyzes data on households’ risk perception and protective behavior following a natural disaster triggered technological accident (Natech accident) that occurred at an industrial park in Sendai during the Great East Japan Earthquake and Tsunami, on 11 March 2011. The results indicate that some households carried out multiple evacuations and that households’ risk perceptions changed throughout the Natech accident evacuation process. Risk perception differed according to household location and demographic characteristics. We also found differences in the protective measures adopted for households in different locations. Specifically, those living closer to the Natech accident tended to evacuate immediately, whereas those living further away tended to shelter in place. Wind direction is discussed as a factor that influences households’ risk perception and evacuation response to a Natech accident. The findings of this study advance knowledge of household behavior in response to a Natech accident and can assist emergency managers in developing strategies for better management of evacuation processes.

Emergency preparedness of veterans and nonveterans.

This study examined statistical differences in levels of disaster preparedness between former members of the US Armed Forces (veterans) and civilians (nonveterans). It was hypothesized that veterans would exhibit a higher degree of disaster preparedness as compared to their nonveteran counterparts as a consequence of their training and life experience. Furthermore, if this were proven to be valid, the finding would identify this cohort as an ideal target audience for emergency and disaster preparedness education efforts. A four-page survey consisting principally of closed-ended questions about emergency preparedness was written to measure these differences. Most of the questions required respondents to rank their answers according to a five-step Likert Scale. The survey could be completed either in hard copy or online from September 2014 to January 2015. Ultimately, 113 surveys were returned for evaluation. Of those respondents, 62 were veterans and 51 were nonveterans. The responses were analyzed using one-way analysis of variance tests for statistical significance using the 95 percent confidence standard for each tested value. The results support that veterans are more prepared for domestic emergencies than nonveterans. In addition, veterans were more willing to provide leadership and direction to others in an effort to assist emergency managers in responding to domestic disasters. It is for these reasons that emergency managers should consider targeting veterans for disaster preparedness training to help ensure effective and efficient responses to emergencies.

Understanding Household Mobilization Time During Natech Accident Evacuation

Natural disasters can cause major technological accidents (Natech accidents) resulting in fires, explosions or hazardous materials releases. The severe consequence may risk human lives and property. Understanding household evacuation behavior could help emergency managers develop strategies that protect the populace better against Natech risk. Our goal in this study is to capture in household mobilization time from the moment the Natech accident occurs and the moment evacuation occurs. Actual evacuation may or may not occur depending on the judgment of individuals about perceived risk or on evacuation orders by local authorities. Data we collected was from a random household survey following a Natech accident at a Sendai oil refinery during the 2011 Great East Japan Earthquake and Tsunami. Results show that respondents evacuated faster after receiving a Natech evacuation order than based on their own judgments. Risk perception and respondents’ demographic characteristics played roles in when evacuation mobilization began. The findings advanced our knowledge of household evacuation timing behavior in response to a Natech accident and could assist emergency managers in developing better strategies for managing the evacuation process.

A multi-scale urban atmospheric dispersion model for emergency management

To assist emergency management planning and prevention in case of hazardous chemical release into the atmosphere, especially in densely built-up regions with large populations, a multi-scale urban atmospheric dispersion model was established. Three numerical dispersion experiments, at horizontal resolutions of 10 m, 50 m and 3000 m, were performed to estimate the adverse effects of toxic chemical release in densely built-up areas. The multi-scale atmospheric dispersion model is composed of the Weather Forecasting and Research (WRF) model, the Open Source Field Operation and Manipulation software package, and a Lagrangian dispersion model. Quantification of the adverse health effects of these chemical release events are given by referring to the U.S. Environmental Protection Agency’s Acute Exposure Guideline Levels. The wind fields of the urban-scale case, with 3 km horizontal resolution, were simulated by the Beijing Rapid Update Cycle system, which were utilized by the WRF model. The sub-domain-scale cases took advantage of the computational fluid dynamics method to explicitly consider the effects of buildings. It was found that the multi-scale atmospheric dispersion model is capable of simulating the flow pattern and concentration distribution on different scales, ranging from several meters to kilometers, and can therefore be used to improve the planning of prevention and response programs.

New Jersey's EMS response to Superstorm Sandy: a case study of the emergency management assistance compact.

In the United States, understanding the Emergency Management Assistance Compact (EMAC) is critical to responding to a natural disaster or manmade event. Recently, the State of New Jersey responded to Superstorm Sandy and implemented the EMAC system by requesting ambulances to aid in the Emergency Medical Services response. New Jersey's response to Superstorm Sandy was unprecedented in that this storm affected the entire state and EMS community. New Jersey's EMS community and infrastructure were impacted greatly, despite years of planning and preparation for such an event. Once received, out-of-state EMS resources were integrated into New Jersey's emergency management and EMS systems. In this report, each phase of the EMAC in New Jersey is explored, from how the response was coordinated to how it ultimately was executed. The state coordinated its response on multiple levels and, as such, tested the practical applicability of the EMAC process and employed best practices and solutions to issues that arose. These best practices and solutions may prove invaluable for any state or territory that may activate the EMAC system for emergency medical service resources.