Enhance Disaster Response Research Articles

Integrating physiotherapists in disaster management: A key to comprehensive recovery

Abstract Disasters, both natural and man-made, pose significant challenges, causing substantial loss of life, property damage, and environmental degradation. Physiotherapists play a crucial role in disaster management, spanning prevention, preparedness, response, and recovery phases. They provide acute care, and long-term rehabilitation, addressing physical and psychosocial recovery aspects. Case studies from the Kashmir, Nepal, and Haiti earthquakes illustrate the vital role of physiotherapists in triage, wound care, mobilization, and community reintegration. Despite barriers such as resource limitations and professional challenges, physiotherapists enhance disaster response through specialized skills and multidisciplinary collaboration. To improve disaster management, recommendations include policy support, training, capacity building, and international cooperation. Investing in physiotherapy in disaster management is essential for building resilient, inclusive communities capable of facing future challenges.

Enhancing Disaster Response and Resilience Through Near-Time GIS for Flood Monitoring and Analysis in Niger River Basin, Nigeria

Enhancing Disaster Response and Resilience Through Near-Time GIS for Flood Monitoring and Analysis in Niger River Basin, Nigeria

Deploying a user-friendly GIS mapping tool in post-earthquake Turkey and Syria

Background The February 2023 earthquakes in Turkey and Syria exposed significant gaps in healthcare information systems, which severely hampered communication, coordination, and the overall public health response. These disruptions were primarily due to the lack of real-time, accessible data on healthcare facilities. Methods In response, an interactive Geographic Information System (GIS) tool, coupled with an integrated WhatsApp chatbot, was developed under the platform Afetsaglikharitasi.org. This system enabled real-time mapping and updates on the status of healthcare services, even in areas with limited internet connectivity, facilitating more efficient resource coordination during the disaster response. Results By March 30, 2023, the disaster health map included data from 537 health centers and had reached over 800,000 individuals via social media platforms such as Instagram, Facebook, and Twitter. The platform attracted over 3,500 unique users, with most accessing it via mobile devices to locate healthcare services. Additionally, shortcomings in the Turkey National Disaster Response Plan (Türkiye Afet Müdahale Planı – TAMP) were identified, particularly the lack of integration of digital health technologies, which hindered resource allocation during the earthquake response and underscored the need for their inclusion in disaster management strategies. Conclusions This study demonstrates the effectiveness of digital health tools in enhancing disaster response and underscores the importance of integrating user-centered, deployable technologies to improve healthcare delivery in emergency settings.

Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

AbstractTo improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.

Risk assessment of urban infrastructure vulnerability to meteorological disasters: A case study of Dongguan, China

Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7–8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

경찰공무원의 직무몰입이 재난대응역량에 미치는 영향에서 회복탄력성의 매개효과

Modern society faces many forms of disasters, not just emerging viral infectious diseases like COVID-19. These disasters not only cause huge losses in human and material resources, but also cause social disruption. Effective disaster response is an essential part of protecting society and its people, and the role of police officers is pivotal in this process. Police officers are directly involved in maintaining order, saving lives, and recovering from disasters during the initial response phase, so their response capabilities have a direct impact on the effectiveness of disaster management. In this context, it can be argued that police officer engagement plays a key role in enhancing disaster response capacity. To further understand the relationship between job commitment and disaster response capacity, it is also necessary to consider the internal factors of individuals. In this study, we propose resilience as such an internal factor and explore its mediating effect to enhance the effectiveness of the police role in disaster situations. The results of the analysis showed that job commitment (=.66, p<.001) was positively significant as a result of the significance test of the effect of the independent variable on the mediating variable. On the other hand, the results of the significance test of the effects of independent variables and parameters on the dependent variable showed that job commitment (=.29, p<.001) had a significant effect on disaster response capability, and the parameter resilience also had a significant effect on disaster response capability (=.33, p<.001). The results suggest that police officers' job engagement directly enhances their disaster response capabilities and that resilience partially influences this relationship. This suggests that police officers with higher job commitment have the ability to respond more effectively in disaster situations. These findings suggest that programs that promote resilience in police organizations can enhance job commitment, which in turn can improve the disaster response capabilities of police officers. Therefore, police organizations should consider implementing a variety of training and support programs to enhance resilience in addition to strategies to increase job engagement.

Assessing Tsunami Impacts and Enhancing Disaster Response in Tirtayasa Banten through High-Resolution Satellite Imagery

In December 2018, the coastal town of Tirtayasa, Banten, suffered severe damage from a tsunami, leaving the area highly vulnerable to future disasters. This research assesses the tsunami’s impact, thoroughly evaluating damage to infrastructure and vegetation using high-resolution satellite imagery. By comparing pre- and post-tsunami images, we quantitatively measure resilience and devastation, documenting significant landscape changes to better understand the extent of damage and identify resilient areas. These insights are critical for developing effective disaster response plans. The study employs advanced geospatial analytic techniques, demonstrating how satellite imagery enhances disaster preparedness and management by enabling prompt and accurate assessments, which are essential for both emergency response and long-term recovery planning. Integrating satellite-based remote monitoring into standard disaster management practices offers substantial advantages, improving the preparedness and response capabilities of vulnerable areas. This research highlights the importance of advanced change detection techniques to improve the accuracy of impact assessments and foster the development of targeted measures to mitigate the effects of future natural disasters.

Key performance indicators and benchmarks in MCI prehospital response using technological tools: a qualitative study assessing the perception of practitioners and tool developers.

The aim of this study is to investigate the opinions and perspectives of The Novel Integrated Toolkit for Enhanced Prehospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) end-users and tool developers regarding Key Performance Indicators (KPIs) and benchmarks that assess the prehospital response to Mass Casualty Incidents (MCIs) enhanced by the NIT-MR. A qualitative study employing focus group discussions was conducted to collect opinions and perspectives of end-users and tool developers regarding KPIs and benchmarks in MCI response using the NIT-MR. The criteria considered for the selection and distribution of participants within the groups was the nature of their involvement within the NIGHTINGALE project and their familiarity with the tools to be discussed. Thirty-one participants from different countries were included. Four themes emerged during data analysis which are: definition/explanation is the personal understanding of participants of the term KPI, process of KPI development and relationship with User Requirements is the decision process for assigning KPIs to user requirements, benchmarking is the mental process of associating a benchmark to a KPI or for developing a benchmark, and technical/medical gap is the gap of understanding between each sides' fields. This study emphasized the need for a structured approach to using KPIs and bridging the gap between technological and medical worlds, taking the NIGHTINGALE project, funded by the European Union, which aims to develop a technological toolkit for first responders in mass casualty incidents as an example. These insights are crucial for enhancing disaster response.

Optimising disaster response: opportunities and challenges with Uncrewed Aircraft System (UAS) technology in response to the 2020 Labour Day wildfires in Oregon, USA

Background The expanding use of Uncrewed Aircraft System (UAS) technology in disaster response shows its immense potential to enhance emergency management. However, there is limited documentation on the challenges and data management procedures related to UAS operation. Aims This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Methods This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Key results UAS technology plays a significant role in search and rescue, reconnaissance, mapping, and damage assessment, alongside notable challenges such as extreme flying conditions, data processing difficulties, and airspace authorisation complexities. Conclusions The study concludes with the need for updated infrastructure standards, streamlined policies, and better coordination between technological advancements and political processes, emphasising the necessity for reform to enhance disaster response capabilities. Implications The findings of this study inform future guidelines for the effective and safe use of UAS in disaster situations, advocating for a bridge between state-of-the-art UAS research and its practical application in emergency response.

Nursing leadership in disaster preparedness and response lessons learned and future directions

Nursing leadership is integral to effective disaster preparedness and response, demanding agility, strategic planning, and robust resource management. Disasters, both natural and man-made, present unique challenges that require nursing leaders to be adept in rapid decision-making and efficient coordination. The unpredictability of disasters necessitates comprehensive and regularly updated preparedness plans, ensuring nursing teams are well-versed in their roles through continuous training and simulations. Interprofessional collaboration and strong community partnerships are essential, fostering a unified response and efficient resource distribution. Communication remains a cornerstone of disaster management, with clear, timely information dissemination being crucial for coordinating efforts and maintaining public trust. Resource scarcity during disasters, such as shortages of medications and personal protective equipment (PPE), poses significant challenges. Effective resource management strategies are essential to maintain patient care standards and protect healthcare workers. The psychological toll on healthcare providers is another critical issue, with nursing leaders needing to implement support systems that address mental health and promote resilience. Integrating advanced technologies like telehealth and data analytics can enhance disaster response, providing remote care options and enabling predictive modeling for better resource allocation. Innovative approaches in nursing leadership include the use of telehealth for remote consultations and monitoring, data analytics for predictive modeling, and interprofessional education to improve teamwork and coordination. Strengthening community partnerships enhances overall disaster response capacity, ensuring that community-specific needs are addressed. Mental health support for healthcare providers, both immediate and long-term, is vital for maintaining a resilient workforce. Policy advocacy by nursing leaders is crucial to developing robust disaster preparedness frameworks and securing adequate funding. In summary, nursing leadership is pivotal in disaster preparedness and response, with a focus on strategic planning, interprofessional collaboration, effective communication, resource management, mental health support, and the integration of advanced technologies. Emphasizing these areas will enhance the resilience and responsiveness of healthcare systems, ultimately improving outcomes for patients and healthcare providers during disasters.

Strategies for Healthcare Disaster Management in the Context of Technology Innovation: the Case of Bulgaria

In Bulgaria, integrating technology and innovation is crucial for advancing sustainable healthcare disaster management, enhancing disaster response and recovery, and minimizing long-term environmental and social impacts. The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. These innovations, when integrated effectively, can significantly strengthen Bulgaria's preparedness and response capabilities, improving efficiency and reducing the environmental footprint of disaster interventions. Collaboration with technology experts, research institutions, and international organizations is essential for successful implementation. The research aims to assess how these technologies can enhance disaster management efficacy, adopting a methodical approach using Systematic Literature Reviews (SLRs). Systematic Literature Reviews (SLRs) utilize precise criteria for research selection and evaluation to reduce bias, systematically compiling and analyzing data from various studies to offer evidence-based insights. They are crucial for uncovering research gaps, indicating areas needing further exploration. Findings highlight the transformative potential of technology integration in streamlining communication, data management, and operational efficiency. The study underscores the urgent need for an integrated approach and a culture of innovation within the healthcare sector, emphasizing ongoing assessment and adaptation of technology-based strategies. While acknowledging limitations and calling for further research, the study provides valuable insights into Bulgaria's healthcare disaster management strategies and sets the stage for future investigations to refine and expand upon these strategies. It advocates for a strategic framework guiding the efficient integration of technology into disaster management, aiming for resilient and patient-centric healthcare systems.

Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing

Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing

A multimodal transfer learning framework for the classification of disaster-related social media images

Rapidly classifying disaster-related social media (SM) images during a catastrophe event is critical for enhancing disaster response efforts. However, the biggest challenge lies in acquiring labeled data for an ongoing (target) disaster to train supervised learning-based models, given that the labeling process is both time-consuming and costly. In this study, we address this challenge by proposing a new multimodal transfer learning framework for the real-time classification of SM images of the target disaster. The proposed framework is based on Contrastive Language-Image Pretraining (CLIP) model, jointly pretrained on a dataset of image-text pairs via contrastive learning. We propose two distinct methods to design our classification framework (1) Zero-Shot CLIP: it learns visual representations from images paired with natural language descriptions of classes. By utilizing the vision and language capabilities of CLIP, we extract meaningful features from unlabeled target disaster images and map them to semantically related textual class descriptions, enabling image classification without training on disaster-specific data. (2) Linear-Probe CLIP: it further enhances the performance and involves training a linear classifier on top of the pretrained CLIP model’s features, specifically tailored to the disaster image classification task. By optimizing the linear-probe classifier, we improve the model’s ability to discriminate between different classes and achieve higher performance without the need for labeled data of the target disaster. Both methods are evaluated on a benchmark X (formerly Twitter) dataset comprising images of seven real-world disaster events. The experimental outcomes showcase the efficacy of the proposed methods, with Linear-Probe CLIP achieving a remarkable 7% improvement in average F1-score relative to the state-of-the-art methods.

Resource exchange patterns between Voluntary Organizations Active in Disaster (VOADs): A multilevel network assessment to improve disaster response capacity

Voluntary Organizations Active in Disaster (VOADs) depend on interorganizational networks to rapidly deliver services to communities during times of crisis. These networks allow VOAD members to share resources before, during, and after disasters. A growing body of empirical research indicates that routine, pre-disaster interactions and exchanges between organizations predict coordination and collaboration during disasters. Rarely, however, do organizations systematically assess such routine exchanges. This study seeks to fill this gap by presenting a cyclical five-step framework in which organizations use assessments of multilevel routine exchanges to improve disaster response networks. During a pilot project with Nebraska VOAD members and their partners, we created a network survey to collect multilevel resource exchange data. The survey was administered to a total of 51 organizations in the Nebraska VOAD survey roster. We received complete surveys from 43 Nebraska VOAD organizations (84 % response rate). We use these survey results to illustrate how organizations can use our network assessment process of identifying, intervening, and improving to enhance disaster response capacity. State-level VOADs can use this approach to understand: 1) how their exchange networks function, 2) which organizations are exchange ‘pillars’ across multiple networks, and 3) which organizations could be better integrated into exchange networks. By evaluating resource exchange networks before and between disaster events, VOADs can adapt their networks to better assist communities during disasters.

Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping

The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system that ingests data from multiple non-traditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through geographic regression analysis using official ground-truth data, showcasing its strong performance and explanatory power. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.

Automated aerial assessment for seamless adaptive adhoc restoration in partially collapsed network

Disasters often result in widespread infrastructure failures, leading to network blackouts that impede critical information dissemination and hamper search-and-rescue operations. This paper presents a novel methodology for expedited assessment and restoration of network connectivity in disaster-affected regions. While existing approaches have made significant contributions, they still face either one or a combination of challenges such as time-consuming procedures, limited coverage, integration complexities, and inaccessibility in certain areas. To address these challenges, we propose two innovative contributions: (1) a novel automated aerial assessment technique for remote identification of network blackouts, offering enhanced accessibility, reduced response times, and heightened reliability; (2) a pioneering automated approach utilizing personal mobile devices (PMDs) to establish alternate multi-hop ad hoc routes from affected regions, enabling auxiliary network formation and seamless integration with surviving networks. The proposed route setup, activated upon detection of network blackouts, facilitates self-managed network maintenance, which adapts to gradual restoration processes, thereby optimizing efficiency, connectivity span, stability, and energy conservation. Both methodologies are rigorously evaluated through simulations and test-bed experiments, demonstrating substantial advancements over existing methodologies. This research contributes to the field by significantly enhancing disaster response capabilities through improved network resilience and rapid connectivity restoration strategies.

The Effect of Disasters on Altruistic Behavior and its Mechanisms

Disasters, characterized by sudden and catastrophic events causing extensive damage to communities, come in various forms, including natural and human-made catastrophes. Disasters can change people's behavior. The present study explores the relationship between disasters and altruistic behavior and its mechanism. It delves into how disasters evoke altruistic responses in individuals, examining mechanisms including heightened empathy, the influence of the public atmosphere, group selection tendencies, and the impact of positive prior experiences. The study draws upon research studies that illuminate the connection between disasters and increased altruism, shedding light on the psychological and social factors that drive people to selflessly assist others during times of crisis. Understanding the relationship between disasters and altruistic behavior is critical to enhancing disaster response and fostering a sense of solidarity within communities. The results of this study promote people's understanding of how disasters affect people's altruistic behavior, and deepen people's understanding of people's altruistic behavior in disaster situations.

Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery

ABSTRACT Landslides are major natural disasters in mountainous areas, often caused by earthquakes and heavy rainfalls. Traditional manual delineation methods for identifying landslide features using optical imagery are inefficient, highlighting the need for automated detection techniques. Deep Convolutional Neural Networks (CNNs) have emerged as advanced solutions in computer vision for this purpose. Despite the reliance on pre-event and post-event imagery or various data sources like digital elevation models (DEMs), the success of deep learning models largely depends on the quality and availability of training data. This poses a challenge for their immediate application after a landslide. This study explores the transferability of a CNN model trained on data from the 2016 Kumamoto Earthquakes for detecting landslides in different events, specifically the 2018 Hokkaido earthquake and the 2017 Asakura Rainfall in Japan. These cases were chosen for their geographical similarities. The proposed deep transfer learning model, based on a DeepLabV3 + architecture built on a pre-trained ResNet50, automatically identifies landslide features without needing specific training data or model adjustments for each event. It achieved high accuracy in both cases, demonstrating CNNs’ potential for broad application in landslide detection and enhancing disaster response efforts.

Public emotional atmosphere during disasters: understanding emotions in short video comments on the Zhengzhou flood

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the “Zhengzhou flood.” First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion’s importance in short video comments, which can enhance disaster response efforts in the future.