Large-scale Emergency Research Articles

Training for Emergencies - How Germany is Preparing for Large-Scale Emergencies Using the EUROMED 2024 Civil Protection Exercise as an Example

Abstract. Every year, numerous major events with thousands of participants take place around the world, for which extensive safety measures are taken to ensure the well-being and safety of visitors. Nevertheless, incidents that lead to injuries and deaths occur time and again. This year, Germany is looking forward to the UEFA EURO 2024, an international football championship for which the host country, Germany, has made extensive preparations on several levels. One component of this is the Medical Task Force (MTF) of the Federal Office of Civil Protection and Disaster Assistance (BBK), which has trained a possible medical service deployment in the event of a mass casualty incident with a specially coordinated series of exercises. The extent to which current remote sensing data can make a useful contribution and be meaningfully integrated was analyzed during the large-scale EUROMED exercise with the help of an airborne camera system on board a helicopter. The system provided up-to-date aerial images of the exercise site, but was also used to record the traffic situation during the transfer of the vehicle convoys to the exercise site. In addition, state-of-the-art AI-based image analysis methods were tested on site and the results were provided and evaluated. An important operational exercise to bring science and practice together step by step in order to be better prepared for an emergency.

How Quasi-Internal Resources Enhance Firm Performance During Large-Scale Emergencies: The Role of Trade-Off Between CSR and Business Innovations

How Quasi-Internal Resources Enhance Firm Performance During Large-Scale Emergencies: The Role of Trade-Off Between CSR and Business Innovations

Critical care resources in surge response: towards real-time situation awareness

ABSTRACT In large-scale emergencies and disasters, real-time situation awareness around health resources can be lifesaving. Understanding the situation at hand, the pressing needs and the actions needed often using information systems that require manual data entry. Relying on humans to enter data manually during surge response is both error-prone and an impediment to the critical requirement of real-time situation awareness. This paper explores the potential of Ultra-High Frequency Radio Frequency Identification (UHF RFID) technology to automate the process of manual data entry followed in providing situation awareness related to critical care resources during surge response. Using design science, an RFID-based prototype was developed to identify and track intensive care resources in real-time. The system was tested in a simulated hospital ward environment to understand deployment challenges and a dashboard was developed to reflect real-time data from multiple sources simultaneously. When properly deployed, UHF RFID can be a viable approach to automating critical care reporting during surge response. Automating routine processes can enhance data quality and minimise workload on health professionals leading to better patient care and evidence based decision-making.

Masks Down: Diplomacy and Regime Stability in the Post‐Covid‐19 Era

Natural disasters can create peaceful diplomatic interactions between conflicting parties, be they warring states or warring domestic factions. Advocates of “disaster diplomacy” argue that while events such as epidemics, earthquakes, floods, windstorms, and tsunamis result in human tragedies, they also generate opportunities for international cooperation, even between enemies. Conversely, natural disasters can also create rifts between friends and allies. Case studies of individual disasters show that while these events sometimes facilitate diplomatic efforts, they may also emphasize existing differences, creating rifts and exacerbating conflicts. The Covid-19 pandemic represents a unique opportunity to test the disaster diplomacy hypothesis on a rare global health crisis that affected many nations of various regime types and with various relations between them. We argue that pandemics and large-scale emergencies can change the rules of the diplomatic game by exposing states’ genuine interests while disregarding international community norms. As such, the Covid-19 pandemic is tearing off the masks from states’ faces, opening paths to cooperation with unexpected partners while creating rifts between yesterday’s allies. We thus argue that post-Covid-19 diplomacy may be characterized by previously rare tendencies such as “trading with the enemy” on the one hand and abandonment of international agreements on the other. Moreover, on the domestic front, such crises tend to exhibit strong fluctuations in regime type, with a clear shift toward populist parties. Additionally, this article provides two alternative explanations for these phenomena and offers an in-depth analysis of two case studies.

Analysis of the Possibilities of Energy Supply to Consumers in Cold Conditions During Large-Scale Emergency Installations in the Gas Industry

The article presents the main components of the methodological approach to the analysis of the possibilities of supplying final types of energy to consumers in the event of a loss of performance of critical facilities of the Russian gas industry in conditions of cooling in the territories of the federal districts. The principle of choosing a territorial section for analyzing the situation with a simultaneous increase in demand for fuel and energy resources is described. An algorithm for conducting research is given. Research is carried out by modeling the work of the country’s fuel and energy complex during the period of the average day of January in the conditions of a peak-increasing demand for thermal energy in the territories of federal districts. At the same time, the cooling is modeled in turn for each of the federal districts in the zone of operation of the Unified Gas Supply System of Russia. An analysis of the possibilities of meeting the demand for final types of energy in the constituent entities of the Russian Federation is carried out for all key scenarios of cooling and loss of efficiency of critical gas industry facilities. The principles of modeling the situation with the supply of final types of energy to consumers in these situations are shown. The most vulnerable regions under the analyzed conditions are identified. The conclusion is made about the need for a balanced approach to determining the optimal degree of dominance of natural gas in the fuel and energy balance of the regions, taking into account the existing opportunities for its supply in the conditions of large-scale emergencies in the gas industry.

A Bayesian network-based probabilistic framework for seismic vulnerability assessment of road networks

After an earthquake, roadside debris from collapsed buildings, structural damage to bridges, and large-scale emergency evacuations are major obstructions to the connectivity of road networks (RNs). However, few studies have comprehensively examined the impact of all these obstructions on the seismic vulnerability of RNs while considering uncertainties. In this paper, a novel probabilistic framework is proposed for seismic vulnerability assessment of RNs based on Bayesian networks (BNs). Herein, the vulnerability of RNs is defined as a combination of the failure probability of a road link and its conditional consequences in terms of network dis-connectivity. The framework can be used to assess the effects of debris distributions, the functionality losses of bridges, and evacuation flows on the failure probabilities of road links and the seismic vulnerability of RNs. Uncertainties in the post-earthquake damage states of buildings and bridges, building collapse types, building-to-road distances, and travel costs are explicitly considered and propagated in the framework using Monte Carlo (MC) simulations. The MC results are used to establish prior probabilities, and based on bidirectional inference with BNs, it is possible to assess the seismic vulnerability of RNs and identify critical roads. The proposed methodology is verified through a real-world RN in China.

Multiwell-based G0-PCC assay for radiation biodosimetry

In major radiological events, rapid assays to detect ionizing radiation exposure are crucial for effective medical interventions. The purpose of these assays is twofold: to categorize affected individuals into groups for initial treatments, and to provide definitive dose estimates for continued care and epidemiology. However, existing high-throughput cytogenetic biodosimetry assays take about 3 days to yield results, which delays critical interventions. We have developed a multiwell-based variant of the chemical-induced G0-phase Premature Chromosome Condensation Assay that delivers same-day results. Our findings revealed that using a concentration of phosphatase inhibitor lower than recommended significantly increases the yield of cells with highly condensed chromosomes. These chromosomes exhibited increased fragmentation in a dose-dependent manner, enabling to quantify radiation damage using a custom Deep Learning algorithm. This algorithm demonstrated reasonable performance in categorizing doses into distinct treatment groups (84% and 80% accuracy for three and four iso-treatment dose bins, respectively) and showed reliability in determining the actual doses received (correlation coefficient of 0.879). This method is amendable to full automation and has the potential to address the need for same-day, high-throughput cytogenetic test for both dose categorization and dose reconstruction in large-scale radiation emergencies.

Decision fatigue experience of front-line nurses in the context of public health emergency: an interpretative phenomenological analysis

BackgroundDecision fatigue is a new concept in the field of psychology and refers to a state of fatigue alongside impaired cognitive processing and emotional regulation ability. Previous studies have confirmed that nurses are prone to decision fatigue, and nurses who experience decision fatigue may implement nursing measures that are inconsistent with clinical evidence, thus affecting patients’ benefits. COVID-19, as a large-scale global public health emergency, increased the workload and burden of nurses and aggravated decision fatigue. However, the factors leading to decision fatigue among nurses have not yet been identified.MethodsThis study is guided by interpretative phenomenology. During the epidemic period of COVID-19: From November 2022 to February 2023, a one-to-one, semi-structured in-depth interview was conducted among nurses with decision fatigue experience who were participating in front-line work in Jilin Province using homogenous sampling. The interview recordings and related data were transcribed into text within 24 h, and data analysis was assisted by NVivo 12.0 software.ResultsAfter a total of 14 front-line nurses were analyzed in this study, The thematic level reaches saturation, the findings present a persuasive and coherent narrative, and the study is terminated, and finally extracted and formed three core themes: “Cognition, influence and attitude of decision fatigue”, “Approaching factors of decision fatigue” and “Avoidant factors of decision fatigue”.ConclusionThis study confirmed that decision fatigue was widespread in the work of front-line nurses, affecting the physical and psychological health of nurses, the quality of nursing work, the degree of benefit of patients and the clinical outcome. However, nursing staff do not know enough about decision fatigue, so the popularization and research of decision fatigue should be strengthened. Improve the attention of medical institutions, nursing managers and nursing staff.Some suggestions are put forward for the intervention of decision fatigue through personnel, task, tool and technology, organization and environment.

RescuePY: Simulation-Based Rescue Response Impact Assessment

Mobility in metropolitan regions is changing. The distribution of space in cities, the design of transport modes, and the organization of mobility are being re-thought. However, no matter the changes and innovations on the way to a more sustainable future, essential constants must be upheld: In the event of minor, regionally limited emergencies, medical assistance must reach those in need quickly. When dealing with large-scale emergencies, the ability to evacuate the area promptly must be ensured. The impact analysis of mobility innovations on emergency services within urban areas so far has been based purely on empirical observations using existing data. Currently, it is only possible to analyze what-if considerations in a limited way. Nevertheless, due to the increasingly rapid changes in mobility, a comprehensive and interlinked analysis will be necessary. This is the key contribution of rescuePY: rescuePY is a simulation suite based on the mesoscopic and microscopic simulation environment hybridPY. It allows holistic and microscopic transport modeling of rescue infrastructure to quantify the impact of the mobility transition towards higher sustainability on the performance of rescue services. The main features of this software are: Rescue system assessment for strategic, long-term planning Mobility-influence studies for operative, mid-term planning Activity-based urban evacuation modeling The capabilities of rescuePY are demonstrated by two applications: a simulation- based, mesoscopic system analysis of emergency services in Munich compared to real-world data and microscopic modeling of emergency vehicles (EMVs) in different road architectures. Ongoing developments aim to improve the evaluation methodology for the aggregated impact analysis of mobility innovations on rescue response services.

Emergencies and exceptional health situations: The Paris fire department at the heart of the action

The Paris fire department is at the heart of the management of exceptional health situations and large-scale emergencies. Their operational strategies are benchmarks, both in France and internationally. Their expertise in the context of the Olympic Games makes them a major player.

Multiwell-based G0-PCC assay for radiation biodosimetry.

In cytogenetic biodosimetry, assessing radiation exposure typically requires over 48 hours for cells to reach mitosis, significantly delaying the administration of crucial radiation countermeasures needed within the first 24 hours post-exposure. To improve medical response times, we incorporated the G0-Premature Chromosome Condensation (G0-PCC) technique with the Rapid Automated Biodosimetry Tool-II (RABiT-II), creating a faster alternative for large-scale radiation emergencies. Our findings revealed that using a lower concentration of Calyculin A (Cal A) than recommended effectively increased the yield of highly-condensed G0-PCC cells (hPCC). However, integrating recombinant CDK1/Cyclin B kinase, vital for chromosome condensation, proved challenging due to the properties of these proteins affecting interactions with cellular membranes. Interestingly, Cal A alone was capable of inducing chromosome compaction in some G0 cells even in the absence of mitotic kinases, although these chromosomes displayed atypical morphologies. This suggests that Cal A mechanism for compacting G0 chromatin may differ from condensation driven by mitotic kinases. Additionally, we observed a correlation between radiation dose and extent of hPCC chromosome fragmentation, which allowed us to automate radiation damage quantification using a Convolutional Neural Network (CNN). Our method can address the need for a same-day cytogenetic biodosimetry test in radiation emergency situations.

Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)

To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.

Terrorist attacks in Türkiye: An analysis of counter-terrorism medicine

Aim: The objective of this research was to perform a counter-terrorism medicine analysis of terrorist attacks in Türkiye. Methods: This study is a retrospective cross-sectional research. The study population consisted of terrorist incidents that occurred in Türkiye, as provided by the START Global Terrorism Database for the period from 1970 to 2020. Various characteristics of terror attacks in Türkiye were analyzed and presented in tabular form. Results: Over the past 51 years, there have been a total of 4,485 terrorist incidents, with 91.2% reaching their intended targets as orchestrated by terrorist organizations in Türkiye. Of these incidents, 2,767 had detrimental economic impacts. Additionally, 17,570 individuals experienced adverse medical effects. Among the 81 provinces in Turkey, Istanbul witnessed the highest number of terrorist incidents and suffered the most significant medical consequences. Conclusion: Türkiye has been significantly impacted by terrorist incidents over the past fifty years especially within the last decade, particularly in metropolitan provinces like Istanbul. These events have negatively affected both the health and economic well-being of many individuals. In response, while the country has implemented various security measures and preparedness strategies to mitigate such incidents, there is also a critical need to improve the readiness of healthcare services to effectively handle the potential consequences of terrorist attacks. This includes advancing anti-terror medicine and tactical medicine capabilities, as well as strengthening mass casualty management systems. Such improvements will ensure a more effective and coordinated response to large-scale emergencies, ultimately saving lives and reducing the long-term impacts on society.

Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models

Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. To this end, we analyze social media data from two disaster events in two different countries to identify reported impacts to CIFs as well as their impact severity and operational status. We employ state-of-the-art open-source LLMs to perform computational tasks including retrieval, classification, and inference, all in a zero-shot setting. Through extensive experimentation, we report the results of these tasks using standard evaluation metrics and reveal insights into the strengths and weaknesses of LLMs. We note that although LLMs perform well in classification tasks, they encounter challenges with inference tasks, especially when the context/prompt is complex and lengthy. Additionally, we outline various potential directions for future exploration that can be beneficial during the initial adoption phase of LLMs for disaster response tasks.

Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

ОРГАНИЗАЦИЯ И ПРОВЕДЕНИЕ ПОИСКОВО-СПАСАТЕЛЬНЫХ РАБОТ С ИСПОЛЬЗОВАНИЕМ СЕТЕЦЕНТРИЧЕСКОЙ СИСТЕМЫ УПРАВЛЕНИЯ ГРУППОЙ БЕСПИЛОТНЫХ ВОЗДУШНЫХ СУДОВ

The number of natural and man-made emergencies on the territory of the Russian Federation continues to remain at a fairly high level. All emergencies are accompanied by the impact on the environment of dangerous damaging factors that can lead to harm to human health and life, fires and explosions with partial or complete destruction of various objects, pollution of the terrain and atmosphere by emissions of harmful substances. All this requires the implementation of emergency measures for the organization and conduct of search and rescue and other urgent work to find and protect people in dangerous areas and lesions. The purpose of the work is to substantiate and develop new tools and methods to improve the efficiency and quality of the organization and conduct of search and rescue operations in areas of large-scale emergencies by units of EMERCOM of Russia. The methodological basis of the work consists of analysis, synthesis, statistical, system-structural methods, and forecasting method. Approaches to improving the efficiency and quality of organizing and conducting search and rescue operations in large-scale emergency zones based on the use of a distributed network-centric control system for a group of unmanned aircraft, which allows all devices of the group to promptly exchange information messages both among themselves and with the central control node.

Acoustic data detection in large-scale emergency vehicle sirens and road noise dataset

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. SACNN leverages a combination of EfficientNet and One Dimensional Convolutional Neural Networks (1D_CNN) to improve the precision and efficiency of detection. The dataset comprises 3-second WAV-format audio files categorized into Emergency Vehicles (Ambulance and Firetruck sirens) and Traffic (plain traffic sounds), with each category having 200 sound files and their corresponding 200 spectrogram images per audio file. Experimental results demonstrate SACNN's superior performance across various metrics, including accuracy, precision, recall, and F1 score, while also achieving notable advancements in computational efficiency. The research addresses the need for more effective solutions in the domain of emergency vehicle siren and road noise detection by introducing SACNN as a cutting-edge solution. By integrating EfficientNet and 1D_CNN, SACNN surpasses existing models in accuracy and computational efficiency. Comparative analysis with EfficientNet and 1D_CNN using a large-scale dataset validates SACNN's superior performance, with an average accuracy, precision, recall, and F1 score of 1.00. Additionally, SACNN exhibits the highest computational efficiency, averaging 0.20883 s per sample. These findings highlight SACNN's efficacy in detecting and classifying emergency vehicle sirens and road noise in large-scale datasets, with implications for traffic management, public safety, and noise pollution monitoring. SACNN offers the potential for constructing real-time acoustic data detection systems.

Distributed Hierarchical Deep Reinforcement Learning for Large-Scale Grid Emergency Control

Distributed Hierarchical Deep Reinforcement Learning for Large-Scale Grid Emergency Control

Human emergency behaviour and psychological stress characteristic mining based on large-scale emergencies

AbstractHuman emergency behaviour and psychological stress response in emergencies are important scientific issues in basic emergency management research. The analysis of the dynamic characteristics of large-scale human behaviour based on electronic footprint data provides a new method for quantitative research on this problem. Previous studies usually assumed that human behaviors were randomly distributed in time, but few studies have studied the psychological stress response of human groups under the influence of emergencies and carried out prediction methods through social media data. Based on the data from five emergencies and daily events in the Qzone, this paper explores the statistical characteristics of human communication behaviors such as time, space and social interaction. The research results reveal the psychological evolution of human groups when they encounter public security emergencies by analysing the causes of individual psychological stress responses in the group. We find that the time interval between people’s posting behaviour and interactive comment behaviour in mobile QQ space before and after an emergency can be approximately described by a power-law distribution. The time interval distribution of Posting and reply is an obvious heavy-tailed distribution. These behavioural characteristics are consistent with people’s psychological stress characteristics. Individual psychological stress responses gradually evolve into social-psychological responses with changes in behavioural characteristics. The greater the social-psychological stress response is, the more panic the public will be, which will cause the outbreak of group irrational behaviour. The research results are theoretically helpful in understanding the impact of emergencies on human communication behaviour patterns and reveal the psychological stress process of mass panic in large-scale emergencies.