Children in a disaster: health protection and intervention

Children are disproportionately affected by disasters. They have greater physiological, psychological and sociological vulnerabilities, often exacerbated by the fact that their unique needs can be overlooked during relief efforts. This article provides an overview of disasters, including how they are categorised, and the factors that need to be considered by military and civilian healthcare teams that respond to them. Information is drawn from a variety of previous disasters, with the effects considered across a range of different populations and communities. The lessons learnt from previous disasters need to inform the ongoing discussions around how to best train and supply both individual healthcare workers and the wider teams that will be expected to respond to future disasters. The importance of role-specific training incorporating caring for children, consideration of paediatric casualties during planning exercises and teaching scenarios, and the requirement for paediatric equipment and medications cannot be overemphasised. While provision of paediatric care may not be the primary role of an individual healthcare worker or their broader team, it still remains their ethical and often legal duty to plan for and deliver care for children when responding to a disaster. This is a paper commissioned as part of the Humanitarian and Disaster Relief Operations special issue of BMJ Military Health.

IMPACT OF DISASTERS ON DISASTER MEDICINE EDUCATION

Introduction: Disaster medicine is a novel but rapidly evolving medical specialty. It aims for evidence based practices as they are essential for contemporary medicine. Every calamity provides input for development. Researchers in the field study these events for the purpose of amending theory and practice to reflect new challenges. The better the understanding of the shortfalls reported is, the greater will the worth for disaster medical response to the upcoming events be. Purpose: The objective of the study is to demonstrate the connection between disasters and commencement and evolution in disaster medicine education and to highlight the significance of lessons learned for practice improvement. Materials and methods: By means of the descriptive method, lessons learned from disaster medical support to some of the most significant catastrophic events in recent years are presented. Comparative and deductive analyses are performed in order to assess the influence of disasters on the evolution of disaster medical support education and training. Results: Analysis of the most consequential disasters proves that the affected countries have implemented disaster medical support planning, organization, and management changes. These changes in policy and practice lead to amendments and advances in disaster medical tuition. Conclusion: As a conclusion, disaster medicine education reliance on the best practices approved throughout the disaster relief operations is noted. Every gained experience and lesson learned have to be implemented into the lectures and seminars, thus transforming real life achievements into knowledge and wisdom.

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

Performance Evaluation of Geological Disaster Relief Operations in China using SBM-DEA Methodology

Geological disasters have caused enormous damage to human beings and the economy in China. Chinese government pay great effort on geological disaster relief. Usually, the efficiency of the disaster rescue is the first priority to be considered. Takeing the historical analysis of China's geological disaster rescue as the main line ,in this paper, we developed a slacks-based measure data envelopment analysis (SBM-DEA) model to evaluate the performance of 18 geological disasters relief during 2015–2019 in China, which are used to examine the performance of the geological disasters rescue activities. The results show that though the capabilities of geo-disaster relief is continuous improvement from 2015 to 2019, China’s geological disasters rescue system is still at the primary stage. Especially, the efficiency of landslide rescue operation is pretty low. We analysis the factors influencing the efficiency and provide several suggestions for capacity improvement of geo-disasters rescue.

A Hybrid Approach of VIKOR and Bi-Objective Decision Model for Emergency Shelter Location–Allocation to Respond to Earthquakes

Earthquakes have catastrophic effects on the affected population, especially in undeveloped countries or regions. Minimizing the impact and consequences of earthquakes involves many decisions and disaster relief operations that should be optimized. A critical disaster management problem is to construct shelters with reasonable capacity in the right locations, allocate evacuees, and provide relief materials to them within a reasonable period. This study proposes a bi-objective hierarchical model with two stages, namely, the temporary shelter stage and the short-term shelter stage. The proposed objectives at different stages are to minimize the evacuation time, maximize the suitability based on qualitative factors, and minimize the number of sites while considering the demand, capacity, utilization, and budget constraints. The performance evaluation of the emergency shelter was carried out by fuzzy-VIKOR, and the most ideal location of the shelter was determined through multiple standards. Emergency management organizations can benefit from the collective expertise of multiple decision-makers because the proposed method uses their knowledge to automate the location and allocation process of shelters. In the case of Chengdu, Sichuan Province, China, the results of using this hybrid approach provide the government with a range of options. This method can realize the trade-off between efficiency and cost in the emergency shelter location and material distribution, and realize reliable solutions in disaster emergencies.

Bioethics in humanitarian disaster relief operations: a military perspective

The ethical dilemmas faced every day by military personnel working within the NHS will potentially be very different to ones that will be faced in the wake of a humanitarian disaster. Allied to this the potentially differing objectives from military personnel when compared with other healthcare workers in these scenarios and a conflict of ethics could arise.Within this paper, the fundamentals of this conflict will be explored and how working within the military framework can affect clinical decisions. This is a paper commissioned as a part of the humanitarian and disaster relief operations special issue of BMJ Military Health.

The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations

PurposeThe study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT).Design/methodology/approachWe collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM).FindingsOur findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools.Research limitations/implicationsOur study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency.Originality/valueOur study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators.

An Extended Model for Disaster Relief Operations Used on the Hagibis Typhoon Case in Japan

This paper presents a generalization of a previously defined lexicographical dynamic flow model based on multi-objective optimization for solving the multi-commodity aid distribution problem in the aftermath of a catastrophe. The model considers distribution of the two major commodities of food and medicine, and seven different objectives, and the model can easily be changed to include more commodities in addition to other and different priorities between the objectives. The first level in the model is to maximize the amount of aid distributed under the given constraints. Keeping the optimal result from the first level, the second level can be solved considering objectives such as the cost of the operation, the time of the operation, the equity of distribution for each type of humanitarian aid, the priority of the designated nodes, the minimum arc reliability, and the global reliability of the route. The model is tested on a recent case study based on the Hagibis typhoon disaster in Japan in 2019. The paper presents a solution for the distribution problem and provides a driving schedule for vehicles for delivering the commodities from depots to the regional centers in need for humanitarian aid.