Search Results (40)

In mass-casualty incidents, emergency responders require access to accurate and timely information to support informed decision-making and ensure the efficient allocation of resources. This article presents a dataset derived from a case study conducted in Mexico City (CDMX) based on the earthquake of 19 September 2017. The dataset presents hypothetical scenarios involving multiple demand points and large numbers of victims, making it suitable for analysis using optimization techniques. It integrates voluntary collaborative geographic information, open government data sources, and historical records, and details the data collection, cleaning, and preprocessing stages. The accompanying Python 3 source code enables users to update the original data for consistent analysis and processing. Researchers can adapt this dataset to other cities with similar risk characteristics, such as Santiago (Chile), Los Angeles (USA), or Tokyo (Japan), and extend it to other types of catastrophic events, including floods, landslides, or epidemics, to support emergency response and resource allocation planning. Full article

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value. Full article

This paper introduces a novel framework that integrates reinforcement learning with declarative modeling and mathematical optimization for dynamic resource allocation during mass casualty incidents. Our approach leverages Mesa as an agent-based modeling library to develop a flexible and scalable simulation environment as a decision support system for emergency response. This paper addresses the challenge of efficiently allocating casualties to hospitals by combining mixed-integer linear and constraint programming while enabling a central decision-making component to adapt allocation strategies based on experience. The two-layer architecture ensures that casualty-to-hospital assignments satisfy geographical and medical constraints while optimizing resource usage. The reinforcement learning component receives feedback through agent-based simulation outcomes, using survival rates as the reward signal to guide future allocation decisions. Our experimental evaluation, using simulated emergency scenarios, shows a significant improvement in survival rates compared to traditional optimization approaches. The results indicate that the hybrid approach successfully combines the robustness of declarative modeling and the adaptability required for smart decision making in complex and dynamic emergency scenarios. Full article

Surge capacity is the ability to manage sudden patient influxes beyond routine levels and can be evaluated using the 4S Framework: staff, stuff, system, and space. While low-resource settings like Rwanda face frequent mass casualty incidents (MCIs), most surge capacity research comes from high-resource settings and lacks generalisability. This study assessed Rwanda’s hospital surge capacity using a cross-sectional survey of emergency and surgical departments in all referral hospitals. Descriptive statistics, t-tests, Fisher’s exact test, ANOVA, and linear mixed-model regression were used to analyze responses. Of the 39 invited participants, 32 (82%) responded. On average, respondents believed that they could manage 13 MCI patients (95% CI: 10–16) while maintaining routine care, with significant differences between tertiary and secondary hospitals (11 vs. 22; p = 0.016). The intra-class correlation was poor for most variables except for CT availability and ICU beds. Surge capacity perception did not vary significantly by professional category, though less senior staff reported higher capacity. Significantly higher capacity was reported by those with continuous access to imaging (p < 0.01). Despite limited resources, Rwandan hospitals appear able to manage small to moderate MCIs. For larger incidents, patient distribution across facilities is recommended, with critical cases prioritized for tertiary hospitals. Full article

In the face of growing demand for emergency treatment in mass casualty incidents involving acute hemorrhagic shock, disaster sites often suffer from limited search and rescue manpower and inadequate medical detection capabilities. With the rapid development of robot technology, the deployment of robots provides greater flexibility and reliability in disaster emergency response and search and rescue work, which can effectively address the shortage of search and rescue forces and medical resources at disaster sites. This paper introduces a snake-like robot designed for the rapid triage of casualties with hemorrhagic shock. Through a structural design combining active wheels and orthogonal joints, the robot integrates the advantages of high-speed mobility of wheeled robots with the high flexibility of jointed robots so as to adapt to the complex environments typical of search and rescue scenarios. Meanwhile, the end of the robot is equipped with a visible light camera, an infrared camera and a voice interaction system, which realizes the rapid triage of casualties with hemorrhagic shock by collecting visible light, infrared and voice dialog data of the casualties. Through Webots software simulation and outdoor site simulation experiments, seven indicators of the designed snake-like search and rescue robot are verified, including walking speed, minimum passable hole size, climbing angle, obstacle-surmounting height, passable step size, ditch-crossing width and turning radius, as well as the effectiveness of collecting visible light images, infrared images and voice dialog data of the casualties. Full article

Introduction: Marathon runs conducted in tropical environments can result in high injury rates. This study was conducted to provide information about the burden of injuries in such environments, to aid planning for similar mass events, enhance medical support, and improve participant safety. Methods: This was a retrospective review of casualty data from the Singapore Marathon races from 2013 to 2016. Patient Presentation Rate (PPR) and Transport to Hospital Rate (THR) were calculated and correlated with heat index, derived from weather information. Injury types were also reviewed. The negative binomial regression was performed to investigate impact of heat index on casualty rates. The medical response plan is briefly described. Results: During the four-year period covered, heat index increased from 29° to 35°. There were more casualties amongst the participants from the full marathon than other race categories. The THR was 0.3 to 0.68 per 1000 participants. Two participants had cardiac arrest. Negative binomial regression showed significant impact of heat index on casualty rate. Incidence rate ratio was 1.22 for severe casualties, which indicated that every 1 unit increase in heat index resulted in 22% rise in severe casualty numbers. Compared with 10 km racers, half marathon racers experienced 1.58 times greater likelihood of all injuries and full marathon racers, a 3.87 times greater risk. Conclusions: Adverse weather conditions with high-heat index can increase injury rates during strenuous physical activities such as the marathon. Applying careful measures to minimise the impact of heat and high humidity may help minimise such injuries. Full article

Chemical, biological, radiological, and nuclear (CBRN) incidents pose significant challenges requiring swift, coordinated responses to safeguard public health. This is especially the case in densely populated urban areas, where the public is not only at risk but can also be of assistance. Public cooperation is critical to the success of mass decontamination efforts, yet prior research has primarily focused on technical and procedural aspects, neglecting the psychological and social factors driving casualty behaviour. This paper addresses this gap through a narrative literature review, chosen for its flexibility in synthesising fragmented and interdisciplinary research across psychology, sociology, and emergency management. The review identified two primary pathways influencing casualty decision making: rational and affective. Rational pathways rely on deliberate decisions supported by clear communication and trust in responders’ competence, while affective pathways are shaped by emotional responses like fear and anxiety, exacerbated by uncertainty. Trust emerged as a critical factor, with effective —i.e., transparent, empathetic, and culturally sensitive— communication being proven to enhance public cooperation. Cultural and societal norms further shape individual and group responses during emergencies. This paper demonstrates the value of narrative reviews in addressing a complex, multifaceted topic such as casualty behaviour, enabling the integration of diverse insights. By emphasising behavioural, psychological, and social dimensions, the results of this paper offer actionable strategies for emergency responders to enhance public cooperation and improve outcomes during CBRN incidents. Full article

Mass shootings result in significant casualties. Due to the complexity of buildings, capturing crowd dynamics during mass shooting incidents is particularly challenging. Therefore, it is necessary to study crowd dynamics and the key mechanisms of mass shooting incidents and explore optimal building design solutions to mitigate the damage caused by terrorist attacks and enhance urban safety. In this study, we focused on the Bataclan Shooting (13 November 2015) as the target case. We used an agent-based model (ABM) to model both the attacking force (shooting) and counterforce (anti-terrorism response). According to the real situation, the dynamic behavior of three types of agents (civilians, police, and shooters) during the shooting accident was modeled to explore the key mechanism of individual behavior. Taking civilian casualties, police deaths, and shooter deaths as the real target values, we obtained combinations for optimal solutions fitting the target values. Under the optimal solutions, we verified the effectiveness and robustness of the model. We also used artificial neural networks (ANNs) to detect the predictive stability of the ABM model’s parameters. In addition, we studied the counterfactual situation to explore the impact of police anti-terrorism strategies and building exits on public safety evacuation. The results show that for the real cases, the optimal anti-terrorism size was four police and the optimal response time was 40 ticks. For double-layer buildings, it was necessary to set exits on each floor, and the uniform distribution of exits was conducive to evacuation under emergencies. These findings can improve police patrol routes and the location of police stations and promote the creation of public safety structures, enhancing the urban emergency response capacity and the level of public safety governance. Full article

Using drones to obtain vital signs during mass-casualty incidents can be extremely helpful for first responders. Thanks to technological advancements, vital parameters can now be remotely assessed rapidly and robustly. This motivates the development of an automated unmanned aerial system (UAS) for patient triage, combining methods for the automated detection of respiratory-related movements and automatic classification of body movements and body poses with an already published algorithm for drone-based heart rate estimation. A novel UAS-based triage algorithm using UAS-assessed vital parameters is proposed alongside a robust UAS-based respiratory rate assessment and pose classification algorithm. A pilot concept study involving 15 subjects and 30 vital sign measurements under outdoor conditions shows that with our approach, an overall triage classification accuracy of 89% and an $F_1$ score of 0.94 can be achieved, demonstrating its basic feasibility. Full article

Once a fire breaks out in an electric bus, it can easily lead to mass casualties and severe injuries, resulting in significant property damage and social impact. The high-temperature smoke and toxic gases in an electric bus fire are key factors that cause a large number of casualties, both of which are closely related to ventilation conditions. In view of this, this study utilized the Fire Dynamics Simulator (FDS 6) software to establish a three-dimensional experimental model of an electric bus. Numerical simulations of the fire combustion process in the electric bus under different ventilation conditions were conducted. Multiple fire scenes were established based on varying ventilation areas, different wind speeds, and diverse window opening positions. This study specifically analyzed the temperature and CO concentration variations under different fire scenes. By comparing the simulation results under different ventilation conditions, it can be concluded that when an electric bus catches fire, opening 100% of the windows, the wind speed is 8 m/s, and opening the rear window of the electric bus first can minimize the fire risk. Through the numerical simulation of electric bus fires under various conditions, this study analyzed the impact of different ventilation conditions on electric bus fires, providing a theoretical basis for firefighting and rescue efforts as well as personnel evacuation in electric bus fire incidents, with the ultimate goal of maximizing public safety. Full article

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. Full article

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented. Full article

This pre–post interventional study explores the effectiveness of simulation-based training in enhancing disaster nursing skills among nursing students at Taif University, Saudi Arabia. The training, which uses a realistic train accident simulation and involves a response team of healthcare professionals, aims to improve knowledge and performance in crisis management and triage during mass casualty incidents. The study’s necessity stems from the critical role nurses play in disaster response, requiring a comprehensive understanding of challenges, collaboration among stakeholders, and improved capabilities. A random sample of 101 nursing students voluntarily participated in the study, with the necessary approvals obtained. We measured their emergency management skills and knowledge using a detailed questionnaire (27 items) and conducted pretest and posttest evaluations. Data analysis was performed using SPSS. The results indicate the training’s effectiveness, as a significant portion of participants achieved high performance levels in the posttest, contrasting with a higher percentage of fail-level grades in the pretest. These findings underscore the potential to improve disaster management protocols and nursing professionals’ preparedness in Saudi Arabia. The study emphasizes the importance of comprehensive education in disaster nursing in enhancing emergency response and patient outcomes. Full article

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity—in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances. Full article

Mass-casualty incidents and disaster scenarios pose significant challenges for medical facilities, necessitating robust preparedness measures. This study aimed to evaluate the preparedness of a specific medical facility in Poland, using the hospital safety index (HSI). A comprehensive analysis of structural, functional, and organizational factors was conducted, assessing facility infrastructure, technical facilities, safety standards, work organization, cooperation with external facilities, human resource management, crisis planning, and communication strategies. The facility exhibited strengths in infrastructural requirements and inter-facility cooperation. Areas of improvement included adherence to safety procedures, crisis communication, and the frequency of evacuation drills. Furthermore, recommendations were provided for enhancing nurse reserves, adopting lean management, promoting a safety culture, and refining business continuity plans. The findings should be interpreted with caution, due to the single-facility focus, potential HSI protocol subjectivity, and the possible Hawthorne effect. This study underscores the importance of continuous research and improvement in crisis management strategies and disaster-victim care, emphasizing the pivotal role of the HSI as an evaluative tool. Full article