Since the beginning of COVID-19, individuals who have SARS-CoV-2 infectious have brought a heavy burden on the healthcare system. Unavoidably, along with pandemics, large-scale disasters, which are possibly emerging, may double the current health crisis. For a powerful disaster response plan, the health services should be prepared for the overwhelming number of disaster victims and infected individuals The proposed framework determines the appropriate number and location of temporary healthcare facilities for large-scale disasters while considering the burden of ongoing pandemic diseases. In this study, first, a multi-period, mix-integer mathematical model is developed to find the location and number of disaster emergency units and disaster medical facilities. Second, we develop an epidemic compartmental model to stimulate the negative effects of the disaster on disease spread and a mixed-integer mathematical model to find optimal number and the location of pandemic hospitals and isolation centers. To validate the mathematical models, a case study is conducted for a district of Istanbul, Turkey.

Disasters occur in both developed and developing countries, generating large amounts of disaster waste including construction and demolition (C&D) waste that needs to be appropriately managed. While developed countries are capable of implementing adequate disaster waste management (DWM) strategies to facilitate their recovery processes, developing countries generally struggle to find the resources and expertise needed to develop such strategies. Lebanon is a developing country vexed by several systemic challenges that hindered its abilities to manage disaster waste. In this paper, we focus on the Beirut Port explosion (4 August 2020), which generated more than 800,000 tonnes of disaster wastes. This study first assesses the executed strategies and identifies their enabling factors and implementation challenges. It then proposes a framework for the proper management of disaster waste, which was validated through 18 in-depth interviews with experts and stakeholders involved in disaster management. Interview notes and transcripts were analyzed using an inductive-deductive process that allowed to identify themes using the constant comparative method. The data revealed that the main barriers toward implementing a successful DWM strategy were the absence of appropriate technologies, infrastructure, expertise, legislative framework and financial resources. The study concludes by proposing a DWM roadmap that includes contingency, risk reduction and implementation plans (IPs) that can enhance decision-making and ease the recovery process.

Since the Chernobyl disaster in 1986, decision support systems and modelling tools are utilized in response to nuclear and radiological emergencies. The Java-based Real-time On-line Decision Support System (JRODOS) is a decision support tool that can be utilised in response to an emergency in managing off-site radiological consequences. This article documents the customization and use of JRODOS for Pakistan. The JRODOS was tailored to the local Pakistan conditions, and a case study for a theoretical nuclear power plant accident was used to assess JRODOS's feasibility as a decision support tool. A worse case probabilistic accident scenario was used to identify zones and areas where urgent protective actions, early protective actions and food restriction and other response actions could be required. The areas and distances identified for the implementation of protective and response actions for such a hypothetical accident were found to be in agreement with the International Atomic Energy Agency (IAEA) suggested emergency planning zones and distances. Additionally, the implications of meteorological and source term input parameters on predicting the radiation doses to members of the public were investigated. It was identified that the output of such tools strongly depends on the availability and accuracy of the input parameters such as radioactive release and meteorological data. Limitations and uncertainties associated with these tools need to be considered in deciding on protective and other response actions in response to a nuclear accident. As established by the IAEA, protective and other response actions need to be applied on a graded approach taking into account the protection strategy and uncertainties and limitation in the available information and criteria, based on the conditions at the facility and off the site.

Background: The probability of sexual violence in areas affected by natural disasters may increase. An increase in the rate of unwanted pregnancies and subsequent intentional abortions, physical and mental injuries, and death can be the consequences of sexual violence. One of the characteristics of natural disasters is their unpredictability and ambiguity. Since decisions must be made quickly during the response, there must be adequate planning in advance, and preparedness in the community will reduce surprises in the responding organizations. This study aimed to identify the factors affecting the preparedness against sexual violence in natural disasters. Methods: In this study, an electronic search was performed in the MEDLINE (PubMed), Scopus, Web of Science, and ProQuest databases. Other search resources, such as Science.gov, Scienceopen.cond Meta-search engines, organizational websites, including UNICEF, UNFPA, and IFRC, as well as key journals and the International Disaster and Risk Conference were hand-searched from 1/1/1990 to 8/29/2021. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was used to select the studies, and the findings were finally analyzed by thematic analysis method using MAXQDA10 software. Results: A total of 40 papers out of a total of 2978 studies were considered in this analysis. Macropolicies, empowerment, contextualization, management and organization, command community-based, and responsive planning were the five primary categories that were found. Conclusion: Preparedness measures are highly important to respond promptly and effectively to sexual violence and should be integrated into disaster preparedness programs. The findings of this study can be used by disaster response managers and policymakers in developing and improving preparedness programs.

Aggressive overlapping of stochastic activities during phases of vaccine development has been critical to making effective vaccines for COVID-19 available to the public, at "pandemic" speed. In cyclical projects wherein activities can be overlapped, downstream tasks may need rework on account of having commenced prior to receiving requisite information that is only available upon completion of upstream task(s). We provide a framework to understand the interplay between stochastic overlap duration and rework due to overlap, and its impact on minimizing expected completion time for a cyclical project. We motivate the problem using the new paradigm for planning vaccine development projects. It best exemplifies features and scenarios in our model that were not considered and are also not apparent in the examples for cyclical development projects in the literature focused on engineered and manufactured products. We find that planning overlapping in scenarios that may be deemed ineffective with an assumption of deterministic tasks, can actually be beneficial when analyzed using stochastic task duration. We determine optimal planned start times for stochastic tasks as a function of a parameter that proxies for the extent of net gain/loss from overlap to minimize expected completion time for the project. We show that in situations with a net gain from overlap it is optimal to start the downstream task concurrently unless the downstream task does not stochastically dominate the upstream task and the net gain from overlap is not low enough. However, in situations with a net loss from overlap it is always optimal to have some degree of overlap in a stochastic task environment. We find that project rescheduling flexibility is always beneficial in a scenario with net loss from overlap and only beneficial in a scenario with net gain from overlap when the downstream task does not stochastically dominate the upstream task and the net gain from overlap is high enough. Our results on overlapping in 1-to-1, 1-to-n, and n-to-1 stochastic task configurations guide the development of an effective heuristic. Our heuristic offers good solution quality and is scalable to large networks as its computational complexity is linear in the number of tasks.

The increasing damage caused by disasters is a major challenge for disaster management authorities, especially in instances where simultaneous disasters affect different geographical areas. The uncertainty and chaotic conditions caused by these situations combined with the inherent complexity of collaboration between multiple stakeholders complicates delivering support for disaster victims. Decisions related to facility location, procurement, stock prepositioning and relief distribution are essential to ensure the provision of relief for these victims. There is a need to provide analytical models that can support integrated decision-making in settings with uncertainty caused by simultaneous disasters. However, there are no formulations tackling these decisions combining multiple suppliers, multiple agencies, and simultaneous disasters. This article introduces a novel bi-objective two-stage stochastic formulation for disaster preparedness and immediate response considering the interaction of multiple stakeholders in uncertain environments caused by the occurrence of simultaneous disasters. At the first stage, decisions related to the selection of suppliers, critical facilities, agencies involved, and pre-disaster procurement are defined. Resource allocation, relief distribution and procurement of extra resources after the events are decided at the second stage. The model was tested on data from the situation caused by simultaneous hurricanes and storms in Mexico during September of 2013. The case is contrasted with instances planning for disasters independently. The results show how planning for multiple disasters can help understand the real boundaries of the disaster response system, the benefits of integrated decision-making, the impact of deploying only the agencies required, and the criticality of considering human resources in disaster planning.

Physical distancing and face masks remain frontline prevention strategies due to suboptimal vaccine uptake and the highly infectious COVID-19 variants. Communities of color are disproportionately impacted by a chronic disease burden that places them at higher risk of severe COVID-19 disease. Therefore, they can greatly benefit from face mask use and physical distancing, especially if the individual(s) have not received the vaccine. We applied the Health Belief Model to explore barriers and motivators influencing physical distancing and face mask use among high-risk, Black American subgroups during the early COVID-19 pandemic stages. We conducted 62 semi-structured interviews among four Black American subgroups: young adults, individuals with underlying medical conditions, essential workers, and parents. Thematic analysis, guided by the Health Belief Model, yielded six themes: (1) Knowledge on Face Mask Use and Physical Distancing, (2) Perceived Susceptibility and Severity Varies by Subgroup, (3) Experience with and Perceived Self-Efficacy to Engage in Preventive Behavior, (4) Perceived Benefits to engaging in preventive behaviors, (5) Perceived Barriers to engage in preventive behaviors, and (6) Cues to action to increase participation. Each subgroup's unique experience informed multilevel, tailored approaches that can be used by health promotion practitioners to improve face mask use and physical distancing among uniquely vulnerable Black American subgroups in the current and future pandemic.

Component criticality analysis of infrastructure systems has traditionally focused on physical networks rather than infrastructure services. As an example, a key objective of transport infrastructure is to ensure mobility and resilient access to public services, including for the population, service providers, and associated supply chains. We introduce a new user-centric measure for estimating infrastructure criticality and urban accessibility to critical public services - particularly healthcare facilities without loss of generality - and the effects of disaster-induced infrastructure disruptions. Accessibility measures include individuals' choices of all services in each sector. The approach is scalable and modular while preserving detailed features necessary for local planning decisions. It relies on open data to simulate various disaster scenarios, including floods, seismic, and compound shocks. We present results for Lima, Peru, and Manila, Philippines, to illustrate how the approach identifies the most affected areas by shocks, underserved populations, and changes in accessibility and critical infrastructure components. We capture the changes in people's choices of health service providers under each scenario. For Lima, we show that the floods of 2020 caused an increase in average access times to all health services from 33 minutes to 48 minutes. We identify specific critical road segments for ensuring access under each scenario. For Manila, we locate the 22% of the population who lost complete access to all higher health services due to flooding of over 15 cm. The approach is used to identify and prioritize targeted measures to strengthen the resilience of critical public services and their supporting infrastructure systems, while putting the population at the center of decision-making.

In times of crisis, such as public health emergency, military conflict or natural disaster, health systems face immense pressures. Large-scale crises continue to appear at irregular intervals and healthcare facilities should be prepared to react quickly and flexibly to the increased need for treatment and care. This paper aims to outline key concepts related to healthcare architecture during a mass-scale crisis, discuss challenges, and suggest solutions. Although the field lacks robust research evidence, lessons learned from past and ongoing events clearly indicate that advance planning is essential for effective crisis response. The crisis response has three guiding principles -adaptability, flexibility, and creativity. The interventions can focus on adapting existing healthcare facilities, building new units, and repurposing civil facilities for hospitals. A wide range of building typologies can be utilised, including modular containers, tents, lightweight structures, and repurposing civil buildings and public spaces. These interventions must be quick, economic, durable, and easily deployable. Temporary facilities must ensure an effective spatial platform for patients and staff, establish links with existing services, and consider post-crisis life of interventions. While these principles can be considered for any type of healthcare facility and any type of crisis, end-results will depend on baseline infrastructure and available resources.

Cities are vulnerable to a range of disasters that can occur simultaneously due to their complexity. Therefore, an effective disaster response plan is needed to reduce the disaster vulnerability of cities. In particular, evacuation route management is important for reducing the losses from a disaster. Efficient disaster response can be realized by searching for suitable evacuation routes and effective road network management. In this paper, we propose a disaster response framework based on a multilayered road network structure and evacuation routes based on our road network. The suggested road structure consists of three layers for the effective management of the network. An A* algorithm-based search for multiple evacuation routes under different conditions in response to an individual disaster on the configured road map provides a safe route for evacuees. As a result, the damage caused by disasters in urban areas can be ameliorated.

Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.

Shelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods.

Sewage sludge (SS) is a frequent and challenging issue for countries with big populations, due to its massive output, significant hazard potential, and challenging resource utilization. Pyrolysis can simultaneously realize the reduction, harmlessness and recycling of SS. Co-pyrolysis offers a wide range of potential in terms of increasing product quality and immobilizing heavy metals (HMs), thanks to its capacity to use additives to address the mismatch between SS characteristics and pyrolysis. High-value utilization potential of SS biochar is the key to evaluating the advancement of treatment technology. A further requirement for using biochar resources is the immobilization and bioavailability reduction of HMs. Due to the catalytic and synergistic effects in the co-pyrolysis process, co-pyrolysis SS biochar exhibits enhanced functionality and has been applied in soil improvement, pollutant adsorption and catalytic reactions. This review focuses on the research progress of different additives in improving the functionality of biochar and influencing the behavior of HMs. The key limitation and challenges in SS co-pyrolysis are then discussed. Future research prospects are detailed from seven perspectives, including pyrolysis process optimization, co-pyrolysis additive selection, catalytic mechanism research of process and product, biochar performance improvement and application field expansion, cooperative immobilization of HMs, and life cycle assessment. This review will offer recommendations and direction for future research paths, while also assist pertinent researchers in swiftly understanding the current state of SS pyrolysis research field.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

This study aimed to identify factors influencing disaster preparedness capability, measure and compare the relative importance of evaluation indicators of preparedness capability in a rainstorm disaster, and analyze the impact of these factors on disaster preparedness so as to improve disaster preparedness capability. The evaluation model was proposed by constructing the target level (the first level) as an indicator system; this was divided into four indicators (the second level): planning, organization, equipment, and education and exercise, and 14 tertiary evaluation indicators (the third level). The validity of the evaluation index system was demonstrated, and the weight of each level was calculated using the Analytic Hierarchical Process and expert survey methods, taking the example of the Zhengzhou "7.20" rainstorm to conduct an empirical analysis of the proposed model. The weak points of disaster preparedness capability were identified. The empirical analysis revealed that organization scored the highest, followed by planning, equipment, and education and exercise, indicating the lack of disaster management equipment and resources, disaster management training, and exercise and public emergency safety education. These results will help in future decision-making, as they provide a clear understanding of what needs to be done to improve disaster preparedness capability.

In-situ stresses are significantly important for underground mining geotechnical design and coal seam gas management in underground coal mines. Aiming at regions with geological structures, this paper proposes an in-situ stress inversion method combining Rhino precise modeling and transverse isotropy theory, and tests it near the Guodishan fault. The results show that the application of Rhino precise modeling and transversely isotropic model in in-situ stress inversion shows advantages in improving the accuracy of in-situ stress calculation. In addition, based on the inversion analysis of in-situ stress near the Guodishan fault, we believe that the fault structure has a significant impact on the distribution of in-situ stress. Within the elevation range of - 400 m to - 800 m, the horizontal stress and vertical stress of the footwall are greater than those of the hanging wall. Moreover, the K ratio near the fault is generally less than 1, but the K ratio in the footwall is usually greater than that in the hanging wall, indicating that the tectonic stress has a stronger influence on the footwall. It is worth noting that the stress gradient near the fault is higher, which may lead to higher disaster risk.

Dermatology residency programs must be prepared to address the unpredictable but seemingly inevitable impacts of natural (eg, hurricanes) and manmade (eg, threats of violence) disasters as well as widespread infectious disease (eg, the COVID-19 pandemic). However, there is a paucity of literature regarding how residency programs should prepare for and respond to these types of disasters. From the equipment trainees utilize in clinic to the didactic education dermatology residents receive, preserving the means of clinical care delivery and mastery of core competencies in the face of unique and disastrous circumstances poses a great challenge to dermatology residency programs. Addressing disaster preparedness early may help to mitigate the short- and long-term impacts of such events, allowing for a more sustainable residency program.