[Management of a mass casualty incident according to the recommendations of the Federal Office of Civil Protection and Disaster Assistance and the principles of the Terror and Disaster Surgical Care Concept in a regional trauma center]

Dealing with a mass casualty incident presents many challenges in the clinical and preclinical situation. There are various systems and structures to address this problem. In the present work, the management of the train accident near Garmisch-Partenkirchen on 3 June 2022 is evaluated with the aid of the recommendations of the Federal Office for Civil Protection and Disaster Relief for hospital alarm and deployment planning as well as the recommendations from the terror and disaster surgical care training of the German Academy of Trauma Surgery and the findings are presented from the perspective of a regional trauma center. It also discusses which key factors in the present case have proved to be successful and in which areas there is still a need for improvement.

Readiness and Response: The Practitioner's Role

The threat of foreign animal diseases is steadily increasing. Veterinary practitioners play a vital role in a community's preparedness for, response to, and recovery from a foreign animal disease outbreak. This article examines the steps a practitioner needs to take to become ready to have a role in disaster readiness and response. Resources exist to provide the practitioner with tools needed to transition their normal daily activities to a larger integrated response. The knowledge and skills used by practitioners in disaster management lead to a more effective and efficient response to a foreign animal disease.

Validation of secondary triage algorithms for mass casualty incidents : A simulation-based study-English version

BACKGROUND

In the event of a mass casualty incident (MCI), the situation-related shortage of medical resources does not end when the patients are transported from the scene of the incident. Consequently, an initial triage is required in the receiving hospitals. In the first step, the aim of this study was to create a reference patient vignette set with defined triage categories. This allowed a computer-aided evaluation of the diagnostic quality of triage algorithms for MCI situations in the second step.

METHODS

A total of 250 case vignettes validated in practice were entered into a multistage evaluation process by initially 6 and later 36 triage experts. This algorithm-independent expert evaluation of all vignettes-served as the gold standard for analyzing the diagnostic quality of the following triage algorithms: Manchester triage system (MTS module MCI), emergency severity index (ESI), Berlin triage algorithm (BER), the prehospital algorithms PRIOR and mSTaRT, and two project algorithms from a cooperation between the Federal Office of Civil Protection and Disaster Assistance (BBK) and the Hashemite Kingdom of Jordan-intrahospital Jordanian-German project algorithm (JorD) and prehospital triage algorithm (PETRA). Each patient vignette underwent computerized triage through all specified algorithms to obtain comparative test quality outcomes.

RESULTS

Of the original 250 vignettes, a triage reference database of 210 patient vignettes was validated independently of the algorithms. These formed the gold standard for comparison of the triage algorithms analyzed. Sensitivities for intrahospital detection of patients in triage category T1 ranged from 1.0 (BER, JorD, PRIOR) to 0.57 (MCI module MTS). Specificities ranged from 0.99 (MTS and PETRA) to 0.67 (PRIOR). Considering Youden's index, BER (0.89) and JorD (0.88) had the best overall performance for detecting patients in triage category T1. Overtriage was most likely with PRIOR, and undertriage with the MCI module of MTS. Up to a decision for category T1, the algorithms require the following numbers of steps given as the median and interquartile range (IQR): ESI 1 (1-2), JorD 1 (1-4), PRIOR 3 (2-4), BER 3 (2-6), mSTaRT 3 (3-5), MTS 4 (4-5) and PETRA 6 (6-8). For the T2 and T3 categories the number of steps until a decision and the test quality of the algorithms are positively interrelated.

CONCLUSION

In the present study, transferability of preclinical algorithm-based primary triage results to clinical algorithm-based secondary triage results was demonstrated. The highest diagnostic quality for secondary triage was provided by the Berlin triage algorithm, followed by the Jordanian-German project algorithm for hospitals, which, however, also require the most algorithm steps until a decision.

Navigating the global IV contrast shortage: The experience of a comprehensive cancer center

PURPOSE/OBJECTIVE(S)

In March 2022, a COVID-19 associated lockdown at an intravenous (IV) contrast production facility resulted in global shortages. We report our experience as a comprehensive cancer center navigating the IV contrast shortage.

METHODS

A triage prioritization system was developed to serve as a guideline for ordering clinicians to reduce contrast use. The triage team reviewed all requests and made final determination based on patient history, treatment plan, prior imaging, possible alternative modalities, and competing requests.

RESULTS

Our institution performed a median of 194 CT studies per day. Contrast utilization as a percentage of all CTs ordered was approximately 80% prior to the shortage, nadired at 9% during peak shortage, and has since returned to pre-shortage levels. Over the study period, 132 requests were reviewed. Fifty studies (38%) were approved by the team for contrast administration, 56 (42%) were recommended to be performed without contrast, 15 (11%) for a change in modality, and 11 (8%) were felt suitable for delay. There was overall general concordance between the recommendations of the triage team and studies conducted without significant distributional differences (χ2 = 4.004, two-tailed p = 0.2610).

CONCLUSION

The concept of resilience involves the development of system-based practices that allow for sustained operations during periods of sudden change, or loss of critical supplies. The effort to optimally allocate limited supply of contrast was an extensive effort across the organization including from senior leadership, IT, radiology, nursing, physicians, and APPs. Concepts from heuristics and behavioral science can aid the conservation of a scarce resource. Decisions made by the team appeared to be sound without any known patient harm associated with a lack of contrast.

Disaster management and environmental policy integration in Pakistan - an evaluation with particular reference to the China-Pakistan Economic Corridor Plan

Economic and social development, the state of the environment and a propensity for disasters are closely intertwined. Therefore, environmental policy integration (EPI) across development and disaster management (DM) policies and plans is important. Pakistan as a country is highly vulnerable to climate-induced environmental changes and associated disasters. In this paper, the extent to which its national environment and climate change policy, disaster risk reduction (DRR) policy as well as disaster management (DM) plans are aligned is established, based on a review of government documents and expert opinions. In this context, a particular emphasis is put on China's Belt and Road Initiative (BRI) that led the China-Pakistan Economic Corridor Plan (CPEC; 2017-2030). While environmental assessment (EA) is currently not conducted for any DM policies and plans, DM and EA are well integrated into provincial environmental protection acts, in national as well as most provincial DM plans. It is concluded that a regulatory framework to guide EPI in DM for BRI and CPEC projects is needed.

Future projections of worst floods and dam break analysis in Mahanadi River Basin under CMIP6 climate change scenarios

This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station. With coefficient of determination (R2) values of 0.764 and 0.858 for calibration and validation, respectively, the model demonstrates satisfactory performance. Secondly, The HEC-HMS model predicts future flow for the Hirakud Reservoir under three climate change scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) and for three future periods (near future, mid future and far future). Thirdly, by analyzing time-series hydrographs, the study identifies peak flooding events. In addition, the HEC-RAS model is used to assess the effects of dam breaches. Downstream of the Hirakud Dam, the analysis highlights potential inundation areas and depth variations. The study determines the following inundation areas for the worst flood scenarios: 3651.52 km2, 2931.46 km2 and 4207.6 km2 for the near-future, mid-future and far-future periods, respectively. In addition, the utmost flood depths for these scenarios are determined to be 31 m, 29 m and 39 m for the respective future periods. The study area identifies 105 vulnerable villages and several towns. This study emphasizes the importance of contemplating climate change scenarios and implementing proactive measures to mitigate the peak flooding events in the Hirakud reservoir region.

Social vulnerability, impacts and adaptations strategies in the face of natural hazards: insight from riverine islands of Bangladesh

BACKGROUND

Bangladesh is one of the countries at risk of natural disasters due to climate change. In particular, inhabitants of its riverine islands (char) confront ongoing climatic events that heighten their vulnerability. This study aims to assess social vulnerability, impacts, and adaptation strategies to climate change in the riverine island areas of Bangladesh.

METHODS

A mixed-method approach incorporating qualitative and quantitative procedures was used on data collected from 180 households of riverine islands in Gaibandha, Bangladesh. The social vulnerability of riverine island communities was assessed based on their adaptation capacity, sensitivity, and exposure to climatic stressors.

RESULTS

The findings show that char dwellers' vulnerability, impacts, and adaptation capability to climate change vary significantly depending on their proximity to the mainland. Social vulnerability factors such as geographical location, fragile and low-grade housing conditions, illiteracy and displacement, climate-sensitive occupation and low-income level, and so on caused to the in-height vulnerability level of these particular areas. This study also displays that climate change and its associated hazards cause severe life and livelihood concerns for almost all households. In this case, the riverine dwellers employed several adaptation strategies to enhance their way of life to the disaster brought on changing climate. However, low education facilities, deficiency of useful information on climate change, poor infrastructure, and shortage of money are still the supreme hindrance to the sustainability of adaptation.

CONCLUSION

The findings underscore the importance of evaluating the susceptibility of local areas to climate change and emphasize the need for tailored local initiatives and policies to reduce vulnerability and enhance adaptability in communities residing in char households.

Debris flow simulation and modeling of the 2021 flash flood hazard caused by a rock-ice avalanche in the Rishiganga River valley of Uttarakhand

The high mountain ecosystem of the Indian Himalayas has frequently been experiencing primary hazards (like earthquakes, avalanches, and landslides). Often, these events are followed by the triggering of secondary hazards (like landslide dams, debris flows, and flooding), thereby posing massive risks to infrastructure and residents in the region. This study was taken up to understand the dynamics of an extraordinary debris flood disaster in the Rishiganga River valley, Chamoli district of Uttarakhand on 7th February 2021. Rapid mass movements (RAMMS)-debris flow software was employed to recreate the entire sequence of the hazard consisting of a rock-ice slide, mass deposition and erosion along the channel, and subsequent debris flood. Forty-nine scenarios were analyzed for accurate calibration of dry-Coulomb type friction coefficient (µ) and viscous-turbulent friction coefficient (ξ). Consequently, the geomorphologic characteristics of the debris flow were validated using high-resolution satellite image interpretation and field photographs. The volume of detached rock-ice mass was estimated to be 26.42 × 106 m3. At the same time, the RAMMS-derived model outputs for velocity, flow depth, and momentum were found in good agreement with the extent and height of actual debris on the ground. The study highlights an urgent need to identify the glaciers with a high risk of ice avalanches in the Indian Himalayas. The presented modeling approach may be applied in dynamic mountain ecosystems to simulate potential flash floods due to avalanches. Moreover, the information reported in this study can be vital input for improving the district-level disaster management plan.

[Validation of secondary triage algorithms for mass casualty incidents-A simulation-based study-German version]

BACKGROUND

In the event of a mass casualty incident (MCI), the situation-related shortage of medical resources does not end when the patients are transported from the scene of the incident. Consequently, an initial triage is required in the receiving hospitals. In the first step, the aim of this study was to create a reference patient vignette set with defined triage categories. This allowed a computer-aided evaluation of the diagnostic quality of triage algorithms for MCI situations in the second step.

METHODS

A total of 250 case vignettes validated in practice were entered into a multistage evaluation process by initially 6 and later 36 triage experts. This algorithm-independent expert evaluation of all vignettes-served as the gold standard for analyzing the diagnostic quality of the following triage algorithms: Manchester triage system (MTS module MCI), emergency severity index (ESI), Berlin triage algorithm (BER), the prehospital algorithms PRIOR and mSTaRT, and two project algorithms from a cooperation between the Federal Office of Civil Protection and Disaster Assistance (BBK) and the Hashemite Kingdom of Jordan-intrahospital Jordanian-German project algorithm (JorD) and prehospital triage algorithm (PETRA). Each patient vignette underwent computerized triage through all specified algorithms to obtain comparative test quality outcomes.

RESULTS

Of the original 250 vignettes, a triage reference database of 210 patient vignettes was validated independently of the algorithms. These formed the gold standard for comparison of the triage algorithms analyzed. Sensitivities for intrahospital detection of patients in triage category T1 ranged from 1.0 (BER, JorD, PRIOR) to 0.57 (MCI module MTS). Specificities ranged from 0.99 (MTS and PETRA) to 0.67 (PRIOR). Considering Youden's index, BER (0.89) and JorD (0.88) had the best overall performance for detecting patients in triage category T1. Overtriage was most likely with PRIOR, and undertriage with the MCI module of MTS. Up to a decision for category T1, the algorithms require the following numbers of steps given as the median and interquartile range (IQR): ESI 1 (1-2), JorD 1 (1-4), PRIOR 3 (2-4), BER 3 (2-6), mSTaRT 3 (3-5), MTS 4 (4-5) and PETRA 6 (6-8). For the T2 and T3 categories the number of steps until a decision and the test quality of the algorithms are positively interrelated.

CONCLUSION

In the present study, transferability of preclinical algorithm-based primary triage results to clinical algorithm-based secondary triage results was demonstrated. The highest diagnostic quality for secondary triage was provided by the Berlin triage algorithm, followed by the Jordanian-German project algorithm for hospitals, which, however, also require the most algorithm steps until a decision.

Who will survive in the next disaster? The associations between sociodemographics and typhoon protective behaviors

Personal protective behaviors play an important role in disaster management because they can effectively reduce the damage caused by disasters. Understanding the relations and differences in protective behaviors among different social classes can help improve disaster management. This study conducted a street questionnaire survey in a typhoon-prone district of Hong Kong. Data were analyzed to understand how sociodemographic parameters (i.e., gender, age, education level, and income) influenced typhoon protective behaviors (i.e., preparedness before typhoons and emergency response during typhoons) of Hong Kong residents. The results showed that the level of preparedness of the respondents was low before the arrival of the typhoon. When the typhoon is approaching Hong Kong, they become more responsive. The higher the intensity of the typhoon, the higher the level of the adoption of emergency response. Among four sociodemographic parameters, only age and income significantly affected protective behaviors. This study supplements the existing literature on typhoon disaster management and provides a reference for improving typhoon disaster management in Hong Kong and other coastal cities affected by typhoons.

A three-stage stochastic optimization model integrating 5G technology and UAVs for disaster management

In this paper, we develop a three-stage stochastic network-based optimization model for the provision of 5G services with Unmanned Aerial Vehicles (UAVs) in the disaster management phases of: preparedness, response and recover/reconstruction. Users or devices on the ground request services of a fleet of controller UAVs in flight and the requested services are executed by a fleet of UAVs organized as a Flying Ad-Hoc Network and interconnected via 5G technology. A disaster scenario can create difficulties for the provision of services by service providers. For this reason, in the first stage, service providers make predictions about possible scenarios in the second stage. Therefore, the first stage represents the preparedness phase, the second stage represents the response phase, followed by the recovery/reconstruction phase, represented by the third stage. In each of the three stages, service providers seek to maximize the amount of services to be performed, assigning each service a priority. They also aim to, simultaneously, minimize the total management costs of requests, the transmission and execution costs of services, the costs to increase the resources of the pre-existing network and, if need be, to reduce them in the recovery/reconstruction phase. For the proposed multi-stage stochastic optimization model, we provide variational formulations for which we investigate the existence and uniqueness of the solution. Finally, a detailed numerical example is solved in order underline some of the key aspects of the model. This paper adds to the literature on the rigorous mathematical modeling of advanced technologies for disaster management.

A systematic review of natural language processing applications for hydrometeorological hazards assessment

Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.

Flood susceptibility mapping by best-worst and logistic regression methods in Mersin, Turkey

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.

United States Federal Emergency Management Agency regional clustering by disaster exposure: a new paradigm for disaster response

The Federal Emergency Management Agency (FEMA) divides the United States (US) into ten standard regions to promote local partnerships and priorities. These divisions, while longstanding, do not adequately address known hazard risk as reflected in past federal disaster declarations. From FEMA's inception in 1979 until 2020, the OpenFEMA dataset reports 4127 natural disaster incidents declared by 53 distinct state-level jurisdictions, listed by disaster location, type, and year. An unsupervised spectral clustering (SC) algorithm was applied to group these jurisdictions into regions based on affinity scores assigned to each pair of jurisdictions accounting for both geographic proximity and historical disaster exposures. Reassigning jurisdictions to ten regions using the proposed SC algorithm resulted in an adjusted Rand index (ARI) of 0.43 when compared with the existing FEMA regional structure, indicating little similarity between the current FEMA regions and the clustering results. Reassigning instead into six regions substantially improved cluster quality with a maximized silhouette score of 0.42, compared to a score of 0.34 for ten regions. In clustering US jurisdictions not only by geographic proximity but also by the myriad hazards faced in relation to one another, this study demonstrates a novel method for FEMA regional allocation and design that may ultimately improve FEMA disaster specialization and response.

[Medical civil protection in Germany-a Greek tragedy?]

In terms of civil protection, there is nothing against self-critical reflection of events, such as the coronavirus disease 2019 pandemic, and learning lessons from them. In the past, however, this has often not led to anticipating future situations, their consequences, and better preparation. To some extent, this seems like a Greek tragedy: conscious disregard-except that this must not be fate. In addition to pandemics, important scenarios for which civil protection in Germany is inadequately prepared, according to the author's assessment, include attacks and accidents involving CBRN (chemical, biological, radiological, nuclear) hazardous substances, mass casualties in the event of a terror attack with hospitals or rescue services as the target of the attacks, as well as prolonged power or drinking water failures, and disruptions in information and communication structures following cyberattacks. There is an urgent need to identify existing problems and demand functional concepts for hospitals, rescue services, and rapid response teams. Responsible authorities, in turn, must develop, finance, and implement concepts.

"Recovering, not recovered" Hospital disaster resilience: a case-study from the 2015 earthquake in Nepal

Background

Disasters are an increasing threat to human health, but we know little about their impact on health services, particularly in low and middle-income settings. ‘Resilient hospitals’ have been increasingly recognized as a cornerstone of disaster management. While various frameworks of hospital resilience exist, they emerged from pre-disaster considerations, and do not incorporate evidence from post-disaster settings.

Objective

This dissertation investigated the impact of a large-scale sudden onset disaster in a tertiary hospital in Nepal, and explored its resilience mechanisms.

Methodology

This consists of an in-depth case-study combining quantitative data from routinely generated hospital records and qualitative data from semi-structured interviews with hospital staff. We used both advanced statistical methods and mixed inductive and deductive coding to analyze the data.

Results

Most of the admitted earthquake victims required surgical interventions and long hospitalizations, considerably straining the hospital. For six weeks, the average number of daily admissions decreased. During this period, the share of injury-related admissions was particularly high, and such admissions were particularly long compared to the baseline. Admissions due to other conditions relatively decreased and were shorter. We found that the hospital’s resilience was highly dependent on emerging adaptations, in addition to the pre-existing disaster plan. Individual resilience of staff also played a major role, and was influenced by senses of safety, meaningfulness, and belonging.

Conclusion

Hospitals should prepare resources and plan for their known disaster risks, but should also allow for a certain flexibility to innovative adaptions to emerging, unforeseen challenges. Challenges faced by hospital workers should not be undermined, and addressing them will increase hospital resilience.

ICT in disaster management context: a descriptive and critical review

Disasters cause catastrophic events that lead to fatalities, damage, and social disturbance. Hydrological and meteorological disasters have an enormous impact worldwide. The impact of IT (Information Technology) in managing these disasters has been neglected. This study is intended to reveal the worldwide research status of hydro-meteorological disasters and various ITs in hazard management through a descriptive and critical review of existing literature. The bibliographic data is collected from Scopus and PATSTAT from 2010 to 2019. This study provides a basic framework for data acquisition, literature selection, and analysis of published documents. A descriptive review of selected literature is conducted to reveal the growth of publications w.r.t. year-wise reported hazards, citation analysis of published documents, patent analysis, geographical status of different hazards research, most influential journals, institutions, and documents. Further, critical review is conducted to analyze the environmental issues, recent developments in ICT-based disaster management, resilience concerns, key research areas, and challenges to implement ICT in disaster management. The present analysis depicts the importance of information technology in disaster management and offers guidance for future disaster management work supported by IT.

A state-of-the-art examination of disaster management in Sierra Leone: the implementation drawbacks, research gaps, advances, and prospects

Unfolding events have shown that Sierra Leone is enduring various disasters at a worrying rate. While progress is being made in disaster management, activities that degrade the fragile ecosystem, exacerbated by climate change, poverty, and bad governance, remain growing concerns. Amid these concerns, there is inadequate information to take appropriate actions. The authors wish to provide a solution by examining 35 publications from various scholarly and grey literature and raw data sources, following a critical review process designed to expose the implementation drawbacks, research gaps, advances, and prospects in disaster management in Sierra Leone. The study results show that lapses in data management, fewer professionals, and inefficiencies in communication are the most pressing considerations for improving disaster management in the country. Inadequate funds to implement management plans remain pervasive. Following these findings, we recommend that all first responders be routinely exposed to international tabletop exercises and simulated disaster response training to help them build their capacities and learn from other countries. Students in the relevant disciplines should be encouraged to participate in these exercises to facilitate early learning. Also, management initiatives should consider gender equity, the situations of the disabled, and other vulnerable groups at all levels of planning and implementation of activities. Future studies should assess the influence of social media on disaster management research and practice in the country.

An integrated System Dynamics model for Closed Loop Supply Chains under disaster effects: The case of COVID-19

For a Closed Loop Supply Chain (CLSC), disaster is a risk source of unknown-unknowns, which may result in production disruptions with significant consequences on -but not limited to-profitability. For this reason, we provide a System Dynamics (SD)-based analysis for disaster events on the operation of CLSCs in order to study the system response (production/collection/disassembly/remanufacturing/recycling rates, inventories, cost, profit). This response is examined through the dynamics at a manufacturer, parts producer, collector, and disassembly center level, by providing control mechanisms for resilient CLSCs under disaster effects. In this dynamic analysis, COVID-19 is treated as a disaster event. Five different business scenario settings are presented for the manufacturer, which are considered as alternative mitigation policies in responding to product demand. The extensive simulation results provide insights for policy-makers, which depend on the reduction in manufacturer's production, reduction in product demand and duration of recovery period which are considered as causal effects due to the COVID-19 outbreak. For all combinations, holding base stocks during the pre-disaster period is proposed as the best mitigation policy in terms of manufacturer's inventory. In terms of economic impact, holding base stocks or coordination with third party are revealed as the best choice depending on the combination, while remote inventory policy adoption as the worst choice.

Hydrometeorological disasters during COVID-19: Insights from topic modeling of global aid reports

Since the beginning of the COVID-19 pandemic, the world has experienced numerous hydrometeorological disasters along with it. The pandemic has made disaster relief work more challenging for humanitarian organizations and governments. This study aims to provide an overview of the topics/issues of concern in the countries while responding to hydrometeorological extreme events (e.g., floods and cyclones) during the pandemic. Latent Dirichlet Allocation (LDA), a computational topic modeling technique, is employed to reduce the numerous (i.e., 1771) humanitarian reports/news to key terms and meaningful topics for 24 countries. Several insights are derived from the LDA results. It is identified that countries have suffered multiple crises (such as locust attacks, epidemics and conflicts) during the pandemic. Maintaining social distancing while disaster evacuation and circumventing the lockdown for relief work have been difficult. Children are an important topic for most countries; however, other vulnerable groups such as women and the disabled also need to be focused upon. Hygiene is not a highly weighted topic, which is of concern during a pandemic that mandates good sanitation to control it effectively. However, health is of great importance for almost all countries. The novelty of the paper lies in its interdisciplinary approach (usage of a computational technique in disaster management studies) and the timely examination of disaster management experiences during the ongoing pandemic. The insights presented in the study may be helpful for researchers and policy-makers to initiate further bottom-up work to address the challenges in responding to hydrometeorological disasters during a pandemic.

Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process

While the utility of social media has been widely recognized in the current literature, minimal effort has been made to further the analysis of their roles on disruptive events, such as the COVID-19 pandemic. To address this gap, this work comprehensively identifies the 16 prevalent social media roles in disaster preparedness during the COVID-19 pandemic. Furthermore, an integrated fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and analytic network process (ANP), hereby termed the FDANP methodology, is used to perform the causal analysis of social media roles and to systemically measure the priority of these roles in emergency preparedness. Among the identified roles, those considered top priority are social media roles concerned with the facilitation of public health policy development, prevention of misinformation, and management of public behavior and response. These results were found to be robust, as evidenced by the sensitivity analysis. The implications of these findings were also detailed in this work in the context of a developing country.

Multi-source domain adaptation of social media data for disaster management

Labeled data scarcity at the time of an ongoing disaster has encouraged the researchers to use the labeled data from some previous disaster for training and transferring the knowledge to the current disaster task using Domain Adaptation (DA). However, often labeled data from more than one previous disaster may be available. As all deep learning models are data-hungry and perform better if fed with more annotated data, it is advisable to use data from multiple sources for training a Deep Convolutional Neural Network (DCNN). One of the easiest ways is to simply combine the data from multiple sources and use it for training. However, this arrangement is not that straightforward. The models trained on the combined data from various sources do not perform well on the target, mainly due to distribution discrepancies between multiple sources. This has motivated us to explore the challenging area of multi-source domain adaptation for disaster management. The aim is to learn the domain invariant features and representations across the domains and transfer more related knowledge to solve the target task with improved accuracy than single-source or combined-source domain adaptation. This study proposes a Multi-Source Domain Adaptation framework for Disaster Management (MSDA-DM) to classify disaster images posted on social media based on unsupervised DA with adversarial training. The empirical results obtained confirm that the proposed model MSDA-DM performs better than single-source DA by up to 10.83% and combined-source DA by up to 5.06% in terms of F1-score for different sets of source and target disaster domains. We also compare our model with current state-of-the-art models. The main challenge of multi-source DA is the choice of the relevant sources taken for training since, unlike single-source DA that handles only source-target distribution drift, the multi-source DA network has to address both source-target and source-source distribution drifts.

Exploring the raison d'etre behind metric selection in network analysis: a systematic review

Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban system. Connecting entities in this way is vital in understanding the complexity of the modern world, and how to navigate these complexities during an event. However, the field of network analysis has grown rapidly since the 1970s to produce a vast array of available metrics that describe different graph properties. This diversity allows network analysis to be applied across myriad research domains and contexts, however widespread applications have produced polysemic metrics. Challenges arise in identifying which method of network analysis to adopt, which metrics to choose, and how many are suitable. This paper undertakes a structured review of literature to provide clarity on raison d'etre behind metric selection and suggests a way forward for applied network analysis. It is essential that future studies explicitly report the rationale behind metric choice and describe how the mathematics relates to target concepts and themes. An exploratory metric analysis is an important step in identifying the most important metrics and understanding redundant ones. Finally, where applicable, one should select an optimal number of metrics that describe the network both locally and globally, so as to understand the interactions and structure as holistically as possible.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00476-w.

A causality analysis of risks to perishable product supply chain networks during the COVID-19 outbreak era: An extended DEMATEL method under Pythagorean fuzzy environment

In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts' judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.

Resilience in emergency management: Learning from COVID-19 in oil and gas platforms

Emergency management, both in civilian and military context, is regarded as a complex socio-technical system, whose dynamic nature and complexity require a holistic approach. Over time, scholars developed diverse strategies and methods to capture such complexity and effectively design emergency plans for more or less severe disasters scenarios. Nonetheless, planning is not always an omni-comprehensive task, pushing organizations to stretch their adaptive capacities in dynamic and challenging settings. This manuscript explores such adaptive capacity as put in place by a leading Norwegian organization in providing emergency management solutions, facing unexpected challenges (at the time of the event): handling of Covid-19 infection episodes on offshore oil platforms. The study, conducted through the Functional Resonance Analysis Method (FRAM) highlights the relevance of organizational learning which allows to handle emergencies by adapting plans to the specific context and by renewing new emergency management procedures derived from lessons learned. The study focuses on three different Covid-19 infection management cases to understand the nuances of actions and emerging adaptations that led to the development of a revised emergency plan, seen again through the lens of FRAM. While the methodological approach refers to Covid-19 infection management, we believe it can be extended into larger crisis management, providing a use case for the applicability of FRAM into emergency management scenarios.

The cost of social vulnerability: an integrative conceptual framework and model for assessing financial risks in natural disaster management

Groups that are unable to prepare for disasters, or to recover from damage on their own, have a high dependency on government services, which inevitably leads to more government spending. Given this, governments can better project the entire cost of disasters and, in turn, effectively manage their finances, by proactively identifying high-vulnerable populations in anticipating financial costs of disasters. However, little attention has been paid to social vulnerability in assessing financial risks in the natural hazards or public finance studies. Thus, this article fills this gap by bringing the concept of social vulnerability from three different fields of study to propose a conceptual framework and corresponding applicable model for estimating disaster costs to inform governmental financial management: the sociological literature on disaster management, economics literature on risk management, and environmental literature of disasters. We review 134 articles on vulnerability from 1990 to 2021, assessing the different conceptualizations of social vulnerability, and the factors affecting vulnerable populations, in each literature. This study contributes to the natural hazards literature on financial and emergency management by integrating the existing literature on social vulnerability into a conceptual framework for measuring social vulnerability and relating it to efforts to assess the financial impact of disasters. Furthermore, based on this conceptual framework, we develop an applicable model for estimating the financial costs of disasters that researchers or governments may apply to assess and develop effective strategies for managing the financial risks associated with disasters. Specifically, the model, which we call the cost of social vulnerability to disasters model (CSVDM), suggests specific indicators from the literature to measure the costs of social vulnerability to more accurately predict the financial impact of disasters.

Understanding the role of self-organizations in disaster relief during COVID-19: A case study from the perspective of co-production

This article examines the role of self-organizations, the spontaneous emergence of order in natural and physical systems in disaster relief during the outbreak of COVID-19 in Wuhan China. By investigating a selection of involved self-organizations, it adopts a co-production perspective by focusing on two cases during the process of delivering emergency responses with the government and citizens to combat the health crisis. Observations and case analysis have found that both government and citizen channel play a role in delivering disaster responses. In particular, self-organizations emerged in the process of delivering relief services are observed as a critical actor of change in terms of mobilizing resources and collaborating citizens to achieve personal safety and community resilience. First, government channels are primarily manifested in residential communities in obeying emergency orders and tasks. Second, citizen channels are practiced through the co-production of disaster responses mainly delivered by residents, nonprofit organizations and enterprises. The finding suggests that self-organizations in the co-production of disaster responses with the government and residents in Wuhan has been effective that eventually brought the health crisis under control. Policy implications generated from this health crisis may provide insightful lessons for government policy making and disaster management.

Application of the "water flow decision pattern" to Asian disaster response

This research aimed to provide a new decision pattern toward the ultimate goal of improving Asian disaster management. The "water flow decision pattern," which is likened to the natural flow of water, was proposed to facilitate smooth decision-making by decision makers. Text document analysis with emphasis on a qualitative technique was used as the major methodology. Five failure cases were analyzed: the sinking of the ferry Sewol in Korea, the drought in India, the SARS outbreak in China, the nuclear leakage in Fukushima, and the typhoon Haiyan in the Philippines. The key finding was that the water flow decision pattern comprehensively combines five decision factors, namely, weight, availability, timeliness, emplacement, and roundabout. Hence, Asian nations may consider its application as a theoretical frame in the future, after appropriate training and exercise are carried out.

Brazilian disaster datasets and real-world instances for optimization and machine learning

We present comprehensive datasets of Brazilian disasters from January 2003 to February 2021 as well as real-world optimization instances built up from these data. The data were gathered through a series of open available reports obtained from different government and institutional sources. Afterwards, data consolidation and summarization were carried out using Excel and Python. The datasets include 9 types of disaster, such as flash floods, landslides and droughts, and the corresponding number of affected people during an 18-year or a 218-month observation period for 5,402 Brazilian municipalities, totaling more than 65,000 observations. Data on relevant geographical, demographic and socioeconomic aspects of the affected municipalities are also provided. These encompass geographic coordinates, regions, population, income, development indicators, amongst other information. From a statistical point of view, the data on disasters can address a number of applications using both supervised and unsupervised machine learning techniques such as, for time series analysis or other dynamic models using socioeconomic data as explanatory variables, i.e. data on the size of the poor population, income, education and general development. The geographic dataset can be useful for aggregating analyses concerning the various forms of territorial organization and allows for the visualization of data in maps. All the aforementioned data can be also used to devise realistic optimization instances related to diverse humanitarian logistics and/or disaster management problems, such as facility location, location-allocation, vehicle routing, and so forth. In particular, we describe two real-world instances for the location-allocation problem studied in [1]. For that purpose, we partially use the given datasets and included other information such as costs and distances relevant to the optimization model. Although using real-world cases to test optimization approaches is a common and encouraged practice in Operations Research, comprehensive datasets and practical optimization instances, as presented in this article, are rarely described and/or available in the academic literature.

Customer perceptions of COVID-19 countermeasures on passenger ships during the pandemic

The COVID-19 pandemic devastated substantial portions of the tourism industry; the cruise industry particularly suffered from negative publicity as the virus spread rapidly on cruise ships. The pandemic is a disaster that the industry has been forced to adapt to. This study illustrates, through a mixed-methods research design, what factors cruiseferry operators considered in their responses to the pandemic, whether the implemented countermeasures increased their customers' sense of security, and what countermeasures customers would agree to follow before boarding a ship. The study thereby provides insights into which countermeasures are likely to decrease customers' perceived health risks and which they are ready to accept or not on cruises during pandemics.

Disruptive technologies as a solution for disaster risk management: A review

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Flight Crash During COVID-19: Lessons Learnt

PURPOSE

The survival rate of a Flight crash is quite low and this makes the Calicut incident unique. Management of flight crash victims is itself a herculean task and during COVID-19 pandemic it poses another challenge to the treating team since all the patients are under quarantine and the treatment protocols are not well defined.

METHODS

On 7/8/2020 at around 7.41 pm, Air India Express aircraft from Dubai to Calicut, while landing on the runway lost its control and skidded off the runway and broke into three parts. We report a detailed analysis of orthopaedic injury patterns and difficulties encountered in the management of these patients with full personal protective equipment (PPE) kit during COVID-19 pandemic.

RESULTS

47 patients from the crash site were brought to our hospital and 38 of them were admitted under the orthopaedic department. 74 doctors and 76 trained nurses along with 58 supporting staff were involved in the management of the mass casualty during that night. Most of the patients suffered injuries to lower extremity and spine which included 11 femur, 13 tibial and 12 spine fractures. Average union time was around 3 months for fractures. Delayed union of fracture femur, avascular necrosis of talus and nonunion of 5th metatarsal base fracture were the reported complications. Surgical site infection was observed in two patients.

CONCLUSION

Trauma management team should be prepared to manage difficulties encountered during identification, communication, and treatment of the disaster victims during this COVID-19 era.

Emergency response, intervention, and societal recovery in Greece and Turkey after the 30th October 2020, MW = 7.0, Samos (Aegean Sea) earthquake

On 30 October 2020, an M_w = 7.0 earthquake struck the Eastern Aegean Sea with considerable impact on Samos Island in Greece and the area of Izmir in Turkey. It was the most lethal seismic event in 2020 worldwide, and the largest and most destructive in the Aegean Sea since the 1955 earthquake that also affected both countries. The Civil Protection authorities in Greece and Turkey were effectively mobilized responding to the earthquake emergency. The main response actions comprised initial announcements of the earthquake and first assessment of the impact, provision of civil protection guidelines through emergency communication services, search and rescue operations,medical care, set up of emergency shelters and provisions of essential supplies, psychological support, as well as education, training activities and financial support to the affected population. From the comparison of the Civil Protection framework and the implemented response actions, it is seen that actions at both sides of the eastern Aegean Sea, followed a single-hazard approach in disaster management with similar response activities coordinated by a main Civil Protection agency, which was in close cooperation with the respective authorities at a national, regional and local level. Based on the presented information, it is concluded that the post-earthquake response and emergency management were satisfactory in both countries, with valuable lessons learnt ahead of the next major earthquake. To this end, many aspects can be further addressed to enhance community resilience and introduce a multi-hazard approach in (natural and man-made) disaster management.

Revisiting social vulnerability analysis in Indonesia data

This paper presents the dataset about the social vulnerability in Indonesia. This dataset contains several dimensions which rely on previous studies. The data was compiled mainly from the 2017 National Socioeconomic Survey (SUSENAS) done by BPS-Statistics Indonesia. We utilize the weight to obtain the estimation based on multistage sampling. We also received additional information on population, the number, and population growth from the BPS-Statistics Indonesia's 2017 Population projection. Furthermore, we provide the distance matrix as the supplementary information and the number of populations to do the Fuzzy Geographically Weighted Clustering (FGWC). This data can be utilized to do further analysis of social vulnerability to promote disaster management. The data can be accessed further at https://raw.githubusercontent.com/bmlmcmc/naspaclust/main/data/sovi_data.csv.

Disaster management in India: are we fully equipped?

Disasters occur with almost unpredictable probability, even though some ideas about the regions of incidence and likely impact on likelihood are available in the scientific literature. In this lecture, I have taken a full view of six disasters that include hydro-geological, meteorological, climate based like floods and droughts as well as the biological holocaust of Covid-19 pandemic. The approach followed in this lecture is to analyse the occurrences, incidence, history and devastation caused by the disaster. The impact and policies to alleviate the effects are also discussed. The culture of disaster reliance is discussed at the end.

The Science of Disaster Medicine: From Response to Risk Reduction

The scientific advancement of the disaster medicine concept started approximately five decades ago. Different disciplines, such as public health, emergency health services, emergency medicine, and military medicine, work within the disaster medicine framework. Disaster medicine aimed to ensure that health services and facilities are operational both in the pre- and post-disaster periods to prevent and reduce the negative health circumstances of the society facing disaster risks. It is a discipline with slow scientific progress due to unclearly systematized multidisciplinary structure and sub-study areas. However, important targets regarding the field of disaster medicine were indicated in the Sendai Framework for Disaster Risk Reduction 2015-2030 published by the United Nations. Among the global goals of disaster medicine, are to reduce the number of deaths and injuries, reduce the number of affected people, strengthen critical facility infrastructure, and ensure functional sustainably of these facilities during disasters. To achieve these goals, disaster medicine is expected to rapidly develop both institutionally and academically. Disaster medicine is a global, mass, administrative, and doctrinal discipline that means beyond clinical studies. Particularly, the development and dissemination of disaster medicine education were emphasized for the first time with the Sendai Framework for Disaster Risk Reduction, which was determined globally in 2015. The disaster medicine discipline is seen to reach a very strong point by 2030.

Nurses' knowledge, skills and preparedness for disaster management of a Megapolis: Implications for nursing disaster education

As frontline health caregivers, nurses must be adequately prepared to respond effectively to disasters. Therefore, it is necessary to assess nurses' disaster management capacity to know their preparedness levels, especially for a densely populated Megapolis like Dhaka, which is at high risk of disasters. Thus, the study aimed to examine Dhaka city nurses' knowledge, skills, preparedness (KSP) for disasters and explore the factors influencing the KSP levels. A self-explanatory descriptive survey questionnaire was developed adopting the Disaster Preparedness Evaluation Tool (DPET) in Bangla language for quantitative data collection. Following multistage sampling, 410 nurses' data were collected from eight hospitals in Dhaka city. The nurses in Dhaka city had moderate levels of knowledge, skills, and preparedness for disaster management, reflecting that they need more development to deal with disasters effectively. Moreover, multiple linear regression analyses indicate that nurses' disaster knowledge, skills, and preparedness scores were significantly positively associated with training on disaster management (p < 0.001) and disaster response experience (p < 0.001). In addition, one-half of the respondents demanded more drills and exercises on disaster management in their workplaces to enhance their disaster skills and preparedness. Furthermore, disaster education should be incorporated into the nursing curriculum, which would play a seminal role in preparing them for future disaster management.

A process-driven and need-oriented framework for review of technological contributions to disaster management

An escalation in the frequency and intensity of natural disasters is observed over the last decade, forcing the community to develop innovative technological solutions to reduce disaster impact. The multidisciplinary nature of disaster management suggests the collaboration between different disciplines for an efficient outcome; however, any such collaborative framework is found lacking in the literature. A common taxonomy and interpretation of disaster management related constraints are critical to develop efficient technological solutions. This article proposes a process-driven and need-oriented framework to facilitate the review of technology based contributions in disaster management. The proposed framework aims to bring technological contributions and disaster management activities in a single frame to better classify and analyse the literature. A systematic review of benchmark disruptive technology based contributions to disaster management has been performed using the proposed framework. Furthermore, a set of basic requirements and constraints at each phase of a disaster management process have been proposed and cited literature has been analysed to highlight corresponding trends. Finally, the scope of computer vision in disaster management is explored and potential activities where computer vision can be used in the future are highlighted.

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.

Operationalizing a Pandemic-Ready, Telemedicine-Enabled Drive-Through and Walk-In Coronavirus Disease Garage Care System as an Alternative Care Area: A Novel Approach in Pandemic Management

OBJECTIVE

Emergency departments face unforeseen surges in patients classified as low acuity during pandemics such as the coronavirus disease pandemic. Streamlining patient flow using telemedicine in an alternative care area can reduce crowding and promote physical distancing between patients and clinicians, thus limiting personal protective equipment use. This quality improvement project describes critical elements and processes in the operationalization of a telemedicine-enabled drive-through and walk-in garage care system to improve ED throughput and conserve personal protective equipment during 3 coronavirus disease surges in 2020.

METHODS

Standardized workflows were established for the operationalization of the telemedicine-enabled drive-through and walk-in garage care system for patients presenting with respiratory illness as quality improvement during disaster. Statistical control charts present interrupted time series data on the ED length of stay and personal protective equipment use in the week before and after deployment in March, July, and November 2020.

RESULTS

Physical space, technology infrastructure, equipment, and staff workflows were critical to the operationalization of the telemedicine-enabled drive-through and walk-in garage care system. On average, the ED length of stay decreased 17%, from 4.24 hours during the week before opening to 3.54 hours during the telemedicine-enabled drive-through and walk-in garage care system operation. There was an estimated 25% to 41% reduction in personal protective equipment use during this time.

CONCLUSION

Lessons learned from this telemedicine-enabled alternative care area implementation can be used for disaster preparedness and management in the ED setting to reduce crowding, improve throughput, and conserve personal protective equipment during a pandemic.

Comparison of trauma scores and total prehospital time in the prediction of clinical course in a plane crash: Does timing matter?

OBJECTIVE

To investigate how the total prehospital time (TPT), Abbreviated Injury Scale (AIS), Injury Severity Score (ISS), and Trauma Score-Injury Severity Score (TRISS) affect the outcome of plane crash victims from anatomical, physiological and psychological perspectives. The accuracy or strength of these scores and TPT in predicting hospitalization and surgery, sequelae development and psychiatric complications [permanent temporary disability (PoTDs)] and PTSD can allow medical professionals to direct and prioritize management efforts of the victims of mass casualties in general.

METHODS

The study was designed as a single-center retrospective study. By examining the records of victims of a plane crash transferred to the ED, AIS, ISS, TRISS and TPT were calculated on admission. The clinical severity of the patients was determined by a joint decision of five clinicians. The performances of the trauma scores on hospitalization, surgery, PTSD and PoTDs were compared. The study data were analyzed via the Mann-Whitney U test and descriptive statistical methods. Pearson's chi-square test was used for the comparison of qualitative data, and ROC analyses were employed to determine cutoff levels.

RESULTS

The AIS, ISS, and TRISS scores of the victims with an indication for hospitalization, calculated on admission to the ED, were significantly higher than those of the other victims (p = 0.001). In addition, TPT, AIS, ISS, and TRISS scores were significantly higher in hospitalized patients than in outpatients (p < 0.05). The cutoff levels for AIS and ISS were ≥ 1.50 and ≥ 4.50, respectively, while they were ≥ 123.5 min for TPT with regard to hospitalization decisions. The AIS, ISS, and TRISS scores calculated on admission for the patients who underwent surgery were significantly higher than those who did not (p = 0.001). Cutoff levels for AIS and ISS were ≥ 2.50 and ≥ 11.50, respectively, while they were ≥ 135.5 min for TPT with respect to the decision to operate on the victims.

CONCLUSIONS

It is expected that everyone who practices medicine be equipped to handle multiple casualties. As the number of people involved in mass casualties increases, diagnostic tools, workups such as laboratory and radiological studies, and prognostic markers such as trauma scores should be simpler and more user-friendly.

Insights and decision support for home health care services in times of disasters

Home health care (HHC) services are of vital importance for the health care system of many countries. Further increases in their demand must be expected and with it grows the need to sustain these services in times of disasters. Existing risk assessment tools and guides support HHC service providers to secure their services. However, they do not provide insights on interdependencies of complex systems like HHC. Causal-Loop-Diagrams (CLDs) are generated to visualize the impacts of epidemics, blackouts, heatwaves, and floods on the HHC system. CLDs help to understand the system design as well as cascading effects. Additionally, they simplify the process of identifying points of action in order to mitigate the impacts of disasters. In a case study, the course of the COVID-19 pandemic and its effects on HHC in Austria in spring 2020 are shown. A decision support system (DSS) to support the daily scheduling of HHC nurses is presented and applied to numerically analyze the impacts of the COVID-19 pandemic, using real-world data from a HHC service provider in Vienna. The DSS is based on a Tabu Search metaheuristic that specifically aims to deal with the peculiarities of urban regions. Various transport modes are considered, including time-dependent public transport.

Crisis management research (1985-2020) in the hospitality and tourism industry: A review and research agenda

The global tourism industry has already suffered an enormous loss due to COVID-19 (Coronavirus Disease 2019) in 2020. Crisis management, including disaster management and risk management, has been becoming a hot topic for organisations in the hospitality and tourism industry. This study aims to investigate relevant research domains in the hospitality and tourism industry context. To understand how crisis management practices have been adopted in the industry, the authors reviewed 512 articles including 79 papers on COVID-19, spanning 36 years, between 1985 and 2020. The findings showed that the research focus of crisis management, crisis impact and recovery, as well as risk management, risk perception and disaster management dominated mainstream crisis management research. Look back the past decade (2010 to present), health-related crisis (including COVID-19), social media, political disturbances and terrorism themes are the biggest trends. This paper proposed a new conceptual framework for future research agenda of crisis management in the hospitality and tourism industry. Besides, ten possible further research areas were also suggested in a TCM (theory-context-method) model: the theories of crisis prevention and preparedness, risk communication, crisis management education and training, risk assessment, and crisis events in the contexts of COVID-19, data privacy in hospitality and tourism, political-related crisis events, digital media, and alternative analytical methods and approaches. In addition, specific research questions in these future research areas were also presented in this paper.

Portability of semantic and spatial-temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

Anesthesiology in Times of Physical Disasters-Earthquakes and Typhoons

Nepal and Hong Kong both are susceptible to natural disasters due to their geographic locations. Nepal suffers from frequent earthquakes, and Hong Kong regularly experiences typhoons of varying severity. Natural disasters may present acutely or with some advance warning. In either case, it is critical that disaster response plans are well established in advance of any incident. This article discusses the anesthetic and critical care implications of such natural disasters, using Nepal and Hong Kong as case studies.

Traditional foodstuffs and household food security in a time of crisis

Traditional foodstuffs play an important role in household food security. No research has, however, attempted to examine traditional foodstuffs in light of disasters and crises. Such research can provide a useful outlook on how traditional foodstuffs can aid households in a situation of disrupted food supply. This outlook becomes relevant in view of future disastrous events that can undermine household food security, especially in poor disadvantaged communities. This study examined the role of traditional foodstuffs during a major crisis. The study adopted an ethnographic perspective and the method of semi-structured household interviews to explore how traditional foodstuffs were used by communities in the city of Mosul, Iraq, under the ISIS administration and during the liberation war (2016-2017). The study showcased the critical role of traditional foodstuffs in survival of local households. It highlighted the importance of cross-generational knowledge of traditional foodstuffs in community preparedness for disasters and crises. The study proposed to integrate traditional foodstuffs into governmental strategies on household food security in Iraq, and beyond. It suggested including traditional foodstuffs in the humanitarian food supply chains in the regions prone to disasters and crises. Future research should examine the prerequisites for such inclusion, especially from the viewpoint of societal and political acceptance of traditional foodstuffs and methods of their production.

Applications of virtual and augmented reality in infectious disease epidemics with a focus on the COVID-19 outbreak

The pandemics of major infectious diseases often cause public health, economic, and social problems. Virtual reality (VR) and augmented reality (AR), as two novel technologies, have been used in many fields for emergency management of disasters. The objective of this paper was to review VR and AR applications in the emergency management of infectious outbreaks with an emphasis on the COVID-19 outbreak. A search was conducted in MEDLINE (PubMed), Embase, IEEE, Cochrane Library, Google Scholar, and related websites for papers published up to May 2, 2020. The VR technology has been used for preventing or responding to infections by simulating human behaviors, infection transmission, and pathogen structure as a means for improving skills management and safety protection. Telehealth, telecommunication, and drug discovery have been among the other applications of VR during this pandemic. Moreover, AR has also been used in various industries, including healthcare, marketing, universities, and schools. Providing high-resolution audio and video communication, facilitating remote collaboration, and allowing the visualization of invisible concepts are some of the advantages of using this technology. However, VR has been used more frequently than AR in the emergency management of previous infectious diseases with a greater focus on education and training. The potential applications of these technologies for COVID-19 can be categorized into four groups, i.e., 1) entertainment, 2) clinical context, 3) business and industry, and 4) education and training. The results of this study indicate that VR and AR have the potential to be used for emergency management of infectious diseases. Further research into employing these technologies will have a substantial impact on mitigating the destructive effects of infectious diseases. Making use of all the potential applications of these technologies should be considered for the emergency management of the current pandemic and mitigating its negative impacts.

Scientific risk performance analysis and development of disaster management framework: A case study of developing Asian countries

Disaster is a state of serious disruptions in the functionality of any society or county. Disasters pose serious economic or environmental impacts that surpass the capacity of the affected country or society to compete with the use of their assets. Recently, Pakistan significantly prone to health disasters due to COVID-19 among developing South Asian countries. The long-term impact of health disasters and other natural hazards put additional pressure mostly on the government's economic policy. It forces the government to follow a constructive approach like a disaster relief-based approach rather than a conventional mitigation management formation to reduce the impact of disaster risk. This study elaborates on the main issues associated with disaster preparedness as well as recovery of the economy and businesses of the country. For Scientific risk performance analysis, open-source data from the National Institute of Disaster Management (NDMA) has been utilized to study the current situation of COVID-19 in Pakistan. Results show Pakistan has been facing a highly vulnerable situation as more than three hundred and fifty thousand confirmed cases have been reported. Poor health and technical management facilities have been exposed against COVID-19 as Pakistan has a low heath budget because of its declining GDP growth rate in the world. This research will help in disaster preparedness and the development of a disaster risk management framework while designing strategies to deal with such pandemics in the future.