Dynamics of Disasters—Key Concepts, Models, Algorithms, and Insights

We consider a model of regions’ ranking in terms of their vulnerability to natural and technological disasters. Regions are different in terms of their resistance to different disasters, by their population, by the distribution of the sources of potential disasters, etc. We consider different models of a data envelopment analysis (DEA) approach taking into account the risks of the implementation of different measures, their cost as well as the heterogeneity of regions. The numerical examples demonstrate the application of the constructed model for the regions of Russian Federation.

In the immediate aftermath of a disaster, relief agencies perform needs assessment operations to investigate the effects of the disaster and understand the needs of the affected communities. Since assessments must be performed quickly, it may not be possible to visit each site in the affected region. In practice, sites to be visited during the assessment period are selected considering the characteristics of the target communities. In this study, we address site selection and routing decisions of the rapid needs assessment teams that aim to evaluate the post-disaster conditions of a diverse set of community groups with different characteristics (e.g., ethnicity, income level, etc.) within a limited period of time. In particular, we study the Selective Assessment Routing Problem (SARP) that determines sites to be visited and the order of site visits for each team while ensuring sufficient coverage of the given set of characteristics. We present a mathematical model and greedy heuristics for the SARP. We perform numerical analysis to evaluate the performance of the greedy heuristics and show that the heuristic version that balances the tradeoff between coverage and travel times provides reasonable solutions for realistic problem instances.

The fixed link between Denmark and Sweden connects two busy cities and a large international airport with many of its travelers and employees. 18,000 vehicles and 160 passenger trains transport each day more than 70,000 people across the combined road and rail Øresund Bridge and through the Øresund Tunnel, approximately 25,000 of them critical to the regional work market. Even though the risk analysis states that the likelihood of a long-term closure (100+ days) is very low Danish and Swedish transport authorities have demanded that the infrastructure operator conducts a survey of the preparedness plans already in place and map possible alternate travel routes for people and freight in case of long-term disruptions. This paper (a) delineates the concept of infrastructure, (b) describes the proceedings of the Work Group for Øresund Preparedness 2014–2016, and (c) discusses the findings presented in its final report to the Danish and Swedish transport authorities while drawing upon experiences from two recent comparable cases of infrastructure disruptions: The Champlain Bridge (2009) and the Forth Road Bridge (2015).

In urban areas, there is a need to evaluate the dynamics and impacts of impending disasters, in order to ensure planning for resilience, emergency actions, and humanitarian support. In this respect, a Bucharest area that suffered a deep trauma in the 1980s was studied. The total or partial demolition of the old urban fabric was ordered by the authoritarian regime to build a new Civic Center. The area was declared as a protected built area in the 1990s. The inventory of the building stock was done in the framework of this project, based on data on building’s age, height, and materials. Additionally, the cultural value was studied with scoring criteria and methods, based on the six scaling stages of the Romanian Law of Monuments. The functional value, as well as the seismic damage and vulnerability, was evaluated for existing building classes. The paper presents examples of multi-hazard scenarios elaborated for this area, considering earthquakes, flooding, and terrorist attacks on public institutions. The spatial databases and online tools were used for mapping the buildings, as well as hazard impacts. According to the results of the above scenarios, shelters/security centers can be created, for earthquake evacuation, in the nearby “Mihai Eminescu” College while, for high-level flooding, the refuge and shelter area should be located in a higher place, near the Academy House or the Romanian Parliament. For other hazards, a center located either at “Mihai Eminescu” College or in Izvor Park is recommended.

Lean thinking (LT) and supply-chain management (SCM) are recognized in the case of United Nations (UN) field operations (FO) as the organizational change towards the “One-UN” culture of cooperation which is one of the biggest challenges for the UN. Based on the above, this paper aims to identify the SCM and LT principles and best practices of the business world for the humanitarian operations world and the expected benefits of the application of the proposed principles and best practices. The study concludes that LT and SCM are two languages with the different accents that, when put together in cross functional design, can deliver better services to the field (SCM accent) and at a lower cost to member states (lean accent), leading to the conclusion that the “ideal SCM” will be the “Lean SCM” for the UN.

Disasters are notorious for extending across multiple jurisdictions, both geographical and functional, and the modern disaster response operational environment is fraught with a multitude of agencies with different mandates and objectives. The complexity and unpredictability of interactions between various actors contribute to the “fog” and “friction” of what constitutes a crisis, similar to the fog and friction of war. Therefore, although situational awareness is an absolute necessity in disaster response, it is impossible to achieve without effective coordination and communication.

Here, we focus on the common operational picture in disaster response, with a view to bridging the gap between its technological and operational components. We use a typical incident planning outline to highlight how software solutions developed at the disaster preparedness phase can reduce the uncertainty during disaster response and streamline the operational planning process. We identify the capabilities and categories of existing applications, and we correlate the capabilities with the stages of the incident planning process to highlight how software supports disaster response coordination. Finally, we discuss the gaps between existing products and modern operational needs and suggest avenues for further research and product development.

Logistics in natural disasters or emergencies involve highly complicated optimization problems with diverse characteristics. The contribution of the present paper is twofold. First, it introduces a multi-period model aiming to minimize the shortages of different relief products in a number of affected areas. The relief products are transported via multiple modes of transportation from dispatch centers to these areas, while adhering to traffic restrictions. A test suite of benchmark problems with diverse characteristics is generated from the proposed model and solved to optimality with CPLEX. The test suite is used for benchmarking a number of established metaheuristics. Necessary modifications are introduced in the algorithms, in order to fit the special requirements of the specific problem type. The algorithms’ performance is assessed in terms of solution accuracy with respect to the optimal solutions. Comparisons among the employed metaheuristics offer valuable insight regarding their ability to tackle humanitarian logistics problems.

Approaches to modeling a tsunami of meteoric origin are discussed. A brief overview of the asteroid and meteorite danger to the Earth is given. Formulas assessing the parameters of the tsunami caused by an asteroid entering the water are derived. The results of the numerical simulation of the effect of the angle of entry of the body into water on the characteristics of the resulting waves in the near field are given. The model based on the Navier–Stokes equations for multiphase flows with a free surface is used in calculations. The dimensions of perturbation are studied and the regularities of changes in the parameters of the source are discovered.

This paper introduces the donation collections problem (DCP), which is a network routing problem motivated by certain challenges that arise during the response phase immediately following large-scale disaster events. In particular, catastrophic events are often characterized by a dramatic surge of unsolicited donations and spontaneous volunteers that pose significant logistical problems for officials, and also inhibit the organized relief efforts of professional responders. The purpose of the DCP is to present a practical alternative for managing post-disaster logistics operations associated with material and volunteer convergence. The DCP is represented mathematically is an integer programming problem. We propose a host of common sense heuristic policies to generate routes for the DCP, and then evaluate the performance of these heuristic methods through computational experimentation. Our results indicate that longer routes are generally preferable to shorter ones based on the DCP objective function, and that network nodes characterized by rapid accumulation of donations should be served during the latter portion of a route’s execution. Our findings also show that routing strategies that would be potentially appealing to inexperienced volunteers produce extraordinarily undesirable results. The collections routing literature almost entirely focuses on the development of optimal or near optimal solution algorithms. However, the humanitarian contexts that motivate the DCP warrant examination of simple heuristic policies that can be easily implemented in practice. This approach seems to represent a unique line of inquiry in the domain of collection routing.

Network criticality indicators, such as the Unified Network Performance Measure, provide powerful tools for interested entities who aim to assess those parts of the network, the closure of which would mostly affect its overall performance. The progress in complex networks analysis on the premises of graph theory allowed for advances on the identification of network characteristics and alternative sets of indicators for the evaluation of network performance. The aim of the present paper is to lay out the contribution of network analytics (in the form of complexity and criticality indicators) in disaster management, with the road network of the Peloponnese region, Greece, acting as a case study. Findings show that adopting interdisciplinary advances can provide useful insights to entities, responsible for the mitigation, preparedness, response, and reconstruction phases of disaster management and support them in the complex decision-making process.

In this paper, we develop a competitive freight service provision network model for disaster relief. A humanitarian relief organization is interested in determining its most cost-effective deliveries of needed supplies in a crisis setting. Multiple freight service providers are engaged in competition to acquire the business of carrying the supplies in the amounts desired to the destinations. We describe the objective functions faced by the various decision-makers and their underlying constraints, and present the optimality conditions. We then define the freight service provision network equilibrium for disaster relief and formulate it as a variational inequality problem. We provide qualitative results for the equilibrium product shipment pattern in terms of existence and uniqueness. For completeness, we also construct a new cooperative system-optimization model and discuss the price of anarchy relating the two models, along with additional theoretical results. In addition, we propose algorithmic schemes that take advantage of the underlying network structure of the problem. We present a case study on the shipment of personal protective equipment supplies in the context of the Ebola humanitarian healthcare crisis in west Africa. The computational results in this paper yield insights on the equilibrium shipment and price patterns in the freight service provision sector for humanitarian operations in terms of enhanced or reduced competition, as well as increases in demand.

In this paper, we develop a mean-variance disaster relief supply chain network model with stochastic link costs and time targets for delivery of the relief supplies at the demand points, under demand uncertainty. The humanitarian organization seeks to minimize its expected total operational costs and the total risk in operations with an individual weight assigned to its valuation of the risk, as well as the minimization of expected costs of shortages and surpluses and tardiness penalties associated with the target time goals at the demand points. The risk is captured through the variance of the total operational costs, which is relevant to the reporting of the proper use of funds to stakeholders, including donors. The time goal targets associated with the demand points enable prioritization as to the timely delivery of relief supplies. The framework handles both the pre-positioning of relief supplies, whether local or nonlocal, as well as the procurement (local or nonlocal), transport, and distribution of supplies post-disaster. The time element is captured through link time completion functions as the relief supplies progress along paths in the supply chain network. Each path consists of a series of directed links, from the origin node, which represents the humanitarian organization, to the destination nodes, which are the demand points for the relief supplies. We propose an algorithm, which yields closed form expressions for the variables at each iteration, and demonstrate the efficacy of the framework through a series of illustrative numerical examples, in which trade-offs between local versus nonlocal procurement, post- and pre-disaster, are investigated. The numerical examples include a case study on hurricanes hitting Mexico.

Current community preparedness campaigns and interventions for natural hazards are not as effective as they aim to be. Research consistently shows that levels of preparedness for natural hazards are low across cultures, despite increased efforts in public hazard education and outreach. Individuals living in areas at risk of natural disasters are not prepared, despite reporting being aware of such risks. This lack of preparedness increases their probabilities of suffering the cascade of ill consequences that follow a disaster. Most of the natural disaster preparedness campaigns rely mainly on the delivery of information, despite studies consistently showing that simply providing the public with information about risk and safety skills is not sufficient to affect preparedness behaviors. Moreover, many of these campaigns lack evaluation and so their success cannot be proven. Research in the fields of social representations, emergency preparedness, and risk communication indicates that a combination of cognitive, emotional, and cultural factors, as well as messaging style, shapes preparedness behaviors. This paper presents the findings of an online search conducted to identify major earthquake and fire preparedness campaigns. The content, design, and theoretical background of these campaigns are analyzed and the results of this evaluation are discussed. This review serves as a guideline for future interventions and campaigns. It aims to contribute to the field of natural hazard preparedness by extracting the components of existing campaigns that have successfully increased preparedness.

Despite popular expectations of political transition and economic development associated with the 2010 Arab Spring movement, the Arab region sees unrelenting conflict in many of its parts (for example, Libya, Syria, and Yemen). Against the backdrop of regional challenges, the present paper will discuss the impact of the Syria Crisis on its neighboring country, the Republic of Lebanon.

The Syria Crisis just entered its sixth year and the destruction of historical cities such as Aleppo and Palmyra reach unprecedented levels as international actors (e.g., Russia) enter the conflict. Streams of refugees leave the camps in neighboring Jordan, Turkey, and Lebanon to embark on the very risky journey across the Mediterranean Sea to Europe. Without any durable political solution anticipated to emerge anytime soon, the conflict takes uglier turns every day, for example: (1) increasing disrespect for the protection of civilians (e.g., Geneva Convention) results in a rising direct and indirect civilian death toll also affecting the few crucial health workers that still remain in country; (2) continuing geographical expansion of Islamic State militants and unsavory shifting coalitions at the national and regional level—be they political or military; and so on.

Lebanon—being an immediate neighbor of Syria—and its population have felt the brunt of the Syria Crisis. Hosting more than 1 million refugees, the economic burden is assumed to have accumulated to 7.5 billion USD. Contributing to these “costs of conflict” are the economic, social, and environmental impacts of the Syria Crisis. Setting the stage for the discussion, the present paper provides a brief history of the special relationship between the two countries in the Levant. It highlights the close political, economic, and social ties over centuries, often neither peaceful nor constructive. With an understanding for the relationship between Lebanon and Syria, the paper details the economic, social (health, education), and environmental (agriculture, food security) costs for Lebanon that emanate from the continuing Syria Crisis.

The resilience of Lebanon and its people is remarkable, though without support the impacts of the Syria Crisis could prove insurmountable. In February 2016, the London Conference brought together stakeholders that committed to substantial humanitarian and development support for Lebanon. They emphasised the importance of greater integration and cooperation among public, private, and civil society institutions to improve economic (e.g., labor market access, subsidy reform, competition, and market access), social (e.g., safety nets), and environmental (e.g., upgrade irrigation practices) policies in Lebanon with a view towards enhancing resilience and returning to a peaceful, sustainable, and inclusive development growth path.

In this paper we study the cost of absenteeism and presenteeism (going to work while sick) during a pandemic in a local economy with several geographically distinct locations, and with work force populations consisting of individuals who live and work in the same city, and individuals who live and work in different locations (daily commuters).We run simulations to study the effects of the fear factor and of the severity of disease on the number of missed work days in the region, which we translate into loss of productivity costs. We find that higher values of the fear parameter lead to high absenteeism and lower infection levels. However, we also show that for severe pandemics (such as the number of secondary infections is higher) there are scenarios where there exists a unique value of the fear parameter which leads to minimum economic costs for the regional economy. This indicates that “staying at home” policies during an epidemic could be implemented for the work force, without reaching a state of emergency.

Detection of tornadoes that provides warning times sufficient for evasive action prior to a tornado strike has been a well-established objective of weather forecasters. With modern technology, progress has been made on increasing the average lead time of such warnings, which translates into a number of lives saved. Recently, machine learning (e.g., kernel methods) has been added to the collection of techniques brought to bear on severe weather prediction. In this chapter, we seek to extend this innovation by introducing and applying two types of kernel-based methods, support vector machines and minimax probability machines to detect tornadoes, using attributes from radar derived velocity data. These two approaches utilize kernel methods to address nonlinearity of the data in the input space. The approaches are based on maximizing the margin between two different classes: tornado and no tornado. The use of the Weather Surveillance Radar 1988 Doppler, with continuous data streaming every 6 min, presents a source for a dynamic data driven application system. The results are compared to those produced by neural networks (NN). Findings indicate that these kernel approaches are significantly more accurate than NN for the tornado detection problem.

In this chapter, we consider the problem of efficiently evacuating all people in an urban area from danger zones to safe zones. This problem, which has attracted major scientific interest and has been well-studied in literature, is indeed large-scale, and as such difficult to solve. In this work, we propose a solution method based on an islanding scheme. This decomposition approach takes into consideration the betweenness of a set of nodes in the transportation network, and aims to obtain clusters from those nodes that can be easily solved: the idea is to divide the flow more evenly towards multiple paths to safety, leading to a more robust evacuation process. We portray our results on several synthetic and real-life transportation networks. More importantly, we use a very large-scale network representation of the city of Jacksonville, Florida, in the USA to show that our approaches solve the problem, a feat that proved impossible for commercial solvers. We conclude this study with our observations and plans for future work.

The following chapter is a description of the role of the humanitarian logistician as viewed through the eyes of a medical professional while working on the same team in Liberia during the Ebola crisis in 2014. Rather than being a technical explanation of logistical tasks, this chapter describes the heroic work the logisticians performed and how their work was critical in enabling the medical team to function, save lives, and survive.