Hurricanes, as one of the most devastating natural disasters, significantly impact the public\'s health, causing both physical injuries and long-lasting mental health issues. Although substantial research has focused on hurricane-related injuries, this study aims to synthesize findings from recent literature, specifically evaluating the 10 most recent hurricanes, to identify research gaps and inform future studies. This scoping review, conducted in accordance with PRISMA-Scr guidelines, assessed studies from PubMed, CINAHL, Cochrane databases, and Medline as of February 2024. Eligibility criteria focused on studies examining physical and mental health impacts, COVID-19 effects, and emergency medical services (EMS) interventions related to Hurricanes Ian, Nicholas, Ida, Zeta, Delta, Sally, Laura, Isaias, Hanna, and Dorian. Twenty articles met the inclusion criteria. The studies were categorized into four themes: physical injuries and fatalities, mental health impacts, hurricane-COVID-19 interplay, and EMS interventions. Findings revealed varied mechanisms of injuries and deaths, significant mental health challenges, compounded crises due to COVID-19, and diverse EMS strategies, including AI utilization and strategic planning for medical care delivery. Addressing the social determinants of health and evaluating hurricane readiness initiatives were two gaps in the literature identified. Future research should focus on the mental health impacts and concurrent crisis challenges to develop comprehensive disaster management practices that enhance community resilience against future hurricanes and public health crises.

Decision Support Systems (DSS) have emerged as important tools for enhancing community resilience due to their ability to provide timely and efficient solutions to disaster-related problems while reflecting the perspectives of different stakeholders and utilizing multiple data sources. This paper provides a comprehensive summary of DSS applications to community resilience, emphasizing how the different modeling techniques are used in different disaster phases. We found that optimization techniques are the most frequently used methods for building DSS. Furthermore, we found that DSS tend to focus more on the preparedness and response phases of disaster management, rather than the recovery and mitigation phases. Moreover, the study highlights the main challenges in developing and implementing DSS for resilience, such as data availability, the uncertainty of the disaster context, and the need for cross-disciplinary collaboration. Based on the reviewed papers, we provide some guidelines to practitioners to select the most suitable decision-support tools for the needs of their community. The study aims to help decision-makers and researchers build effective decision support systems for enhancing community resilience, considering the current challenges.

Pakistan bears an incredible burden of vaccine-preventable diseases, and efforts to improve widespread immunization have been hindered by inadequate public health management following disasters and other health emergencies. Through a brief review of Pakistan\'s health system, an understanding of routine immunization challenges is sought based on the organizational changes made to the planning and delivery of immunization activities. Further, recent immunization trends of measles, polio, and tuberculosis are examined in correspondence to health emergencies prompted by climate change and the COVID-19 pandemic. The national public health response to each disease is discussed, and insight is given to how the devolution of Pakistan\'s health system may have influenced the severity of each emergency. Focus is given to the potential immunization challenges and how they may impact future initiatives for the control of vaccine-preventable diseases. Although incidence rates suggest increased cases of certain vaccine-preventable diseases and disruptions of immunization activities following recent disasters, further studies may need to be conducted to establish a stronger understanding of the immunization trends noted within this review.

The disaster-resilient village programme, which aims to develop settlements with the autonomy to adapt and deal with disasters aims to promote disaster awareness. Several questions arise regarding the contribution of the programme to the development of the adaptive capacity of village communities, including: what types of adaptive capacity emerge as a result of the programme\'s implementation, and what factors promote and inhibit the development of adaptive capacity? This study employs a comprehensive literature review. The initial step is to do a keyword search using Publish or Perish 8.2.3944.8118. This study includes a range of search phrases, including the phrase \'disaster-resilience\' and keywords: community resiliency, disaster preparedness and disaster policy. The type of adaptive capacity that emerges from the implementation of the disaster response village programme incorporates the topic of flexibility predominantly, showing that this programme raises a diversity of adaption practices and possibilities in the community. This approach is influenced by the disaster-prone geographical characteristics of Indonesia. Practical gaps exist in the form of evaluating values in the development of similar disaster programmes, while theoretical gaps exist in the form of conceptual identification of cultural characteristics that may occur as a result of efforts to build adaptive ability through the programme. This article examines societal values that are affected by programmes.
UNASSIGNED: This manuscript aims to add to the variety of disaster programme design initiatives requiring community resilience and sustainability. This sociocultural and disaster-related field is pertinent to the scope of this publication.

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.

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big \"sensed\" data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management.

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.

In the context of a holistic and comprehensive disaster response effort to the COVID-19 pandemic, many countries across the globe mobilized their military forces in order to cope with sudden and exponential surges of critically ill patients with COVID-19 in stretched healthcare systems.
The purpose of this work is to identify, map, and render world-wide key concepts of civil-military cooperation (CIMIC) in disaster management during the COVID-19 crisis visible.
Literature was systematically searched in three databases (PubMed, Web of Science, Cochrane Library) on 26 January 2022, and analyzed with qualitative, mixed narrative-phenomenological methods in compliance with PRISM-ScR and SRQR.
Forty-five publications were included in the analysis; pertinent authors were from 22 countries covering five continents. We identified three key thematic clusters in the published literature: Cluster (1) Medico-scientific contributions with the participation of military medical personnel or institutions: members of the military acted as subject matter experts, clinical and experimental (co-) investigators as well as co-founders for enabling COVID-19 relevant research. Areas covered were relevant to the COVID-19 patient\'s clinical journey from prevention, exposure, diagnostics, and treatment and included pertinent fields such as digital health and telemedicine, global and public health, critical care, emergency and disaster medicine, radiology, neurology, as well as other medical specialties, i.e., respiratory care, pulmonology, burn medicine, and transfusion medicine, in addition to environmental and occupational sciences as well as materials science. Cluster (2) CIMIC field experiences or analyses included areas such as political framework, strategy, structure, nature of civil-military interaction, and concrete mission reports in selected countries. Themes covered a broad spectrum of pandemic disaster management subjects such as capacity and surge capacity building, medical and pharmaceutical logistics, patient care under austere circumstances, SARS-CoV-2 testing support, intelligent and innovative information management, vaccination support, and disaster communication. Cluster (3) The military as a role model for crisis management.
Civil-military cooperation made a significant contribution to the level of resilience in crisis management on a global scale, positively impacting a broad spectrum of core abilities during the COVID-19 pandemic.

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

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.