Understanding Logistics in Disaster Response

Understanding Logistics in Disaster Response
Sample Answer for Understanding Logistics in Disaster Response Included After Question

Understanding Logistics in Disaster Response

Description

Readings:

1.Tatham and Christopher Chapter 1

2.Kovacs and Spens Chapter 2

3. Selected Articles- NPS

Supply Chain Resilience in Disasters

Disaster Logistics and Leadership

In the Preposition NPS article (found in the attchment ), review the value of prepositioning disaster supplies ahead of a disaster. In each case presented, determine if the effort to move supplies made a significant impact on the success or failure of the event.

Select one event and provide an example of an action that would have provided a better outcome for the disaster. Briefly explain how you came to that conclusion.

A Sample Answer For the Assignment: Understanding Logistics in Disaster Response
Title: Understanding Logistics in Disaster Response

NPS-LM-11-188 ^`nrfpfqflk=obpb^o`e= pmlkploba=obmloq=pbofbp= = Strategies for Logistics in Case of a Natural Disaster 28 September 2011 by Dr. Aruna Apte, Assistant Professor, and Dr. Keenan D. Yoho, Assistant Professor Graduate School of Business & Public Policy Naval Postgraduate School Approved for public release, distribution is unlimited. Prepared for: Naval Postgraduate School, Monterey, California 93943 = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= Form Approved OMB No. 0704-0188 Report Documentation Page Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 3. DATES COVERED 2. REPORT TYPE 28 SEP 2011 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Strategies for Logistics in Case of a Natural Disaster 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School,Graduate School of Business & Public Policy,Monterey,CA,93943 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The need to effectively and efficiently provide emergency supplies and services is increasing all over the world. We investigate four policy options? prepositioning supplemental resources, preemptive as well as phased deployment of assets, and a surge of supplies and services?as potential strategies for responding to a disaster. We illustrate the linkage between our four policy options and a disaster classification based upon disaster localization (dispersed or local) and speed of disaster onset (slow or sudden). We summarize our work by introducing a matrix that aligns logistics strategies with disaster types in order to assist policymakers in their resource management decisions. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: a. REPORT b. ABSTRACT c. THIS PAGE unclassified unclassified unclassified 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES Same as Report (SAR) 43 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 The research presented in this report was supported by the Acquisition Chair of the Graduate School of Business & Public Policy at the Naval Postgraduate School. To request Defense Acquisition Research or to become a research sponsor, please contact: NPS Acquisition Research Program Attn: James B. Greene, RADM, USN, (Ret.) Acquisition Chair Graduate School of Business and Public Policy Naval Postgraduate School 555 Dyer Road, Room 332 Monterey, CA 93943-5103 Tel: (831) 656-2092 Fax: (831) 656-2253 E-mail: jbgreene@nps.edu Copies of the Acquisition Sponsored Research Reports may be printed from our website www.acquisitionresearch.net = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= Abstract The need to effectively and efficiently provide emergency supplies and services is increasing all over the world. We investigate four policy options— prepositioning supplemental resources, preemptive as well as phased deployment of assets, and a surge of supplies and services—as potential strategies for responding to a disaster. We illustrate the linkage between our four policy options and a disaster classification based upon disaster localization (dispersed or local) and speed of disaster onset (slow or sudden). We summarize our work by introducing a matrix that aligns logistics strategies with disaster types in order to assist policymakers in their resource management decisions. Keywords: logistics, natural disaster, humanitarian assistance, humanitarian aid, disaster response = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= -i- THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – ii – About the Authors Dr. Aruna Apte has successfully completed various research projects, involving application of mathematical models and optimization techniques that have led to over 20 research articles and one patent. Her research interests are in developing mathematical models for complex, real-world operational problems using optimization tools. She values that her research be applicable. Currently her research is focused in humanitarian and military logistics. She has several publications in journals, such as Interfaces, Naval Research Logistics, Production and Operations Management. She has recently published a monograph on Humanitarian Logistics. Aruna has over twenty years of experience teaching operations management, operations research, and mathematics courses at the undergraduate and graduate levels. She has advised emergency planners in preparing for disaster response. She is the founding and current president for a new college (focus group) in Humanitarian Operations and Crisis Management under the flagship academic professional society in her intellectual area of study, Production and Operations Management Society. Dr. Keenan Yoho’s primary research activities are in the area of analyzing alternatives under conditions of uncertainty and resource scarcity. Keenan’s primary research activities lie in the analysis of alternatives for capital purchases under conditions of resource scarcity, supply chain management, risk analysis, humanitarian assistance and disaster response, and resource management in environments that exhibit high degrees of uncertainty. Dr. Aruna Apte Graduate School of Business and Public Policy Naval Postgraduate School Monterey, CA 93943-5000 Tel: 831-656-7583 Fax: (831) 656-3407 E-mail:auapte@nps.edu Dr. Keenan D. Yoho Graduate School of Business and Public Policy Naval Postgraduate School Monterey, CA 93943-5000 Tel: 831-656-2029 Fax: (831) 656-3407 E-mail: kdyoho@nps.edu = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – iii – THIS PAGE INTENTIONALLY LEFT BLANK = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = – iv – NPS-LM-11-188 ^`nrfpfqflk=obpb^o`e= pmlkploba=obmloq=pbofbp= = Strategies for Logistics in Case of a Natural Disaster 28 September 2011 by Dr. Aruna Apte, Assistant Professor, and Dr. Keenan D. Yoho, Assistant Professor Graduate School of Business & Public Policy Naval Postgraduate School Disclaimer: The views represented in this report are those of the author and do not reflect the official policy position of the Navy, the Department of Defense, or the Federal Government. = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = -v- THIS PAGE INTENTIONALLY LEFT BLANK = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = – vi – Table of Contents I. Introduction …………………………………………………………………………………. 1 II. Literature Review …………………………………………………………………………. 5 III. Disaster Life Cycles ……………………………………………………………………… 7 IV. Disaster Classification ………………………………………………………………….. 9 V. VI. A. Indian Ocean “Boxing Day” Tsunami of 2004 ………………………….. 10 B. Haiti 2010 Earthquake …………………………………………………………. 11 C. Hurricane Katrina ……………………………………………………………….. 12 D. Influenza “Swine Flu” Epidemic of 2009 …………………………………. 12 Discussion …………………………………………………………………………………. 15 A. Prepositioning …………………………………………………………………….. 15 B. Proactive Deployment …………………………………………………………. 17 C. Phased Deployment ……………………………………………………………. 18 D. Surge Capacity …………………………………………………………………… 20 Conclusion…………………………………………………………………………………. 21 List of References ………………………………………………………………………………… 25 = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – vii – THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – viii – I. Introduction In 2009 there were 335 natural disasters reported worldwide that killed 10,655 persons, affected more than 119 million others, and caused over $41.3 billion in economic damages (Vos, Rodriguez, Below, & Guha-Sapir. 2009). The number of natural disasters reported between 1900 and 2010 has increased significantly and, with it, the number of requests for aid and humanitarian assistance (see Figure 1). While the trend in the number of disasters reported shows an increase, it is not clear that there has been a commensurate response in terms of preparedness. The United States Agency for International Development (USAID) reports that of all funds used to support disaster operations, 90% are spent for response, whereas 10% are spent on preparedness activities and investments and risk reduction (A. Giegerich, personal communication, September 21, 2010). The United Nations estimates that every dollar spent to prepare for a disaster saves seven dollars in disaster response (United Nations Human Development Program, 2007). 550 500 450 400 350 300 250 200 150 100 50 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Figure 1. Number of Disasters Reported from 1900–2010 (EM–DAT, 2011) = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -1- Although the objective of all the organizations and agencies involved in humanitarian assistance is to reduce human suffering and casualties, the duration and severity of the human toll during a natural disaster is largely dependent upon the speed and scope of the response, which is often a function of the level of preparedness that has been established prior to the disaster event. While there are no internationally agreed upon metrics by which to judge or measure the effectiveness of a response to a disaster, scholars working in the humanitarian and disaster response research area have found that improvement is desirable (Apte, 2009; Van Wassenhove, 2006). An effective and efficient humanitarian response depends “on the ability of logisticians to procure, transport and receive supplies at the site of a humanitarian relief effort” (Thomas, 2003). In this research we focus on the response to a disaster area in the form of distributing supplies, and strategies that will enhance the effectiveness of such a response. For the purpose of this research, we accept the Center for Research on the Epidemiology of Disasters’ (CRED) definition of disaster, which is “a situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance.” The unpredictability of the timing of a disaster, as well as the scope of its human and material destruction, raises several serious questions for emergency planners and first responders. For example, how can a state of supply preparedness be established and maintained? How should adequate prepositioned disaster relief inventory be established and sustained over time, to include the rotation of perishable stocks? How can information regarding the location, quantity, and condition of prepositioned inventory be shared, and what effect would this information sharing have on the total investment of prepositioned stocks? Is prepositioning the best strategy for all types of disasters? How reliable are the potential supply lines if it is determined that supplies should be virtually stockpiled (that is, a detailed list or database of supplies by type and quantity is created and maintained, as well as reliable sources that can provide the supplies quickly)? = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -2- Should the supplies be sourced locally or from outside the disaster zone? Answers to these questions depend on the expected onset speed of the disaster, the volume and weight of supplies to be moved, the expected magnitude of humanitarian relief required, and the expected likelihood of a disaster in the area. As part of our investigation we explore four policy options: (1) prepositioning supplemental resources in or near the incident location; (2) proactive deployment of assets in advance of a request; (3) phased deployment of assets and supplies, analogous to the “just in time” inventory control philosophy practiced by many commercial manufacturers; and (4) “surge” transportation of manpower and equipment from locations outside the disaster area. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -3- THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -4- II. Literature Review One of the major issues in a response supply chain in case of a natural disaster is to coordinate the operations and relief inventories over a large number of stages, locations, and organizations. This has to be done while providing the emergency supplies and services to the affected population under extreme conditions. Decisions regarding the types of provisions that should be prepositioned, as well as their location, should be made well before a disaster strikes in order to provide quick response. To some extent, without such a high level of uncertainty and an adverse environment, it is similar to the core question in supply chain management of coordinating activities and inventories over a spectrum of stages of the supply chain and facility locations of the inventory (Schoenmeyr & Graves, 2009). In the private sector, it has been found that if each individual stage in a serialsystem of the supply chain operates with a designated base stock policy with service guarantees, then the optimal safety stock strategy is to maintain inventory at certain key locations, which results in separating the stages of the supply chain; this type of policy allows each stage to operate independently by minimizing the need for communication and coordination amongst players (Simpson, 1958; Graves & Willems, 2002). Models available in supply chain management literature are predominantly with unlimited capacity for storage. In cases where there is unlimited capacity, the amount of safety stock needed is less than the level needed with capacity constraint (Schoenmeyr & Graves, 2009). The determination of the optimal placement of safety stock in a supply chain has been addressed by Simpson (1958) and Schoenmeyr and Graves (2008), where there are evolving or predetermined forecasts, and by Graves and Willems (2002), where there is uncertain, as well as non-stationary, demand. This concept can explain the response supply chain where there exists uncertainty for the quantity required, as well as what is required (Apte, 2009; Ergun, Karakus, Keskinocak, = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -5- Swann, & Villareal, 2009). Rawls and Turnquist (2010) developed a model for determining the location and quantity of supplies that should be prepositioned when there is uncertainty with respect to whether a disaster will occur and where it will occur, and built upon this work by adding service quality constraints (Rawls & Turnquist, 2011) to ensure the probability of meeting demand and the average shipment distance is within a specified parameter. In addition to the prepositioning of relief inventories, a disaster response may require the formulation of policies that require the expansion of warehouses, medical facilities, and temporary shelters, while infrastructure preparation may include the provision of airstrips and ramp space at existing airfields (Salmeron & Apte, 2010). Koavacs and Spens (2009) weighed the difference between traditional commercial logistics and humanitarian logistics. With humanitarian logistics, it is imperative to go beyond the profitability of commercial logistics. Within the domain of humanitarian logistics, suppliers have different motivations for participating, and customers do not generate voluntary demand. It is clear that in most cases a “repeat purchase” is not a possibility. Thus, supply networks must take into account the lack of true demand. Demand is dictated by the relief agencies that are the primary actors within this framework. Therefore, it is the responsibility of the agency to “push” the supplies to the disaster location in the immediate response phase, which is different from the commercial philosophy of pull-based demand. Humanitarian logistics focuses on getting the greatest volume of supplies to the points where they are needed, and there may be lessons learned in the commercial sector that could be used to improve the planning and execution of strategies that could be implemented during a disaster response. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -6- III. Disaster Life Cycles The life cycle of a disaster from the perspective of Humanitarian Assistance and Disaster Relief (HADR) is divided into three stages (as illustrated in Figure 2): being prepared in the pre-disaster stage, response as the disaster strikes, and recovery in post-disaster (Apte 2009; Van Wassenhove, 2006). RESPONSE PREPAREDNESS RECOVERY Asset Prepositioning Infrastructure Preparation Pre-Disaster Ramp Up Ramp Down Sustainment Disaster Event Post-Disaster Figure 2. Life Cycle of Disasters (Apte, 2009) Disaster preparedness is the first step in mitigating the adverse impacts of any unforeseen catastrophic event. Preparedness on an individual level is defined by the creation of an escape and survival plan, as well as the procurement and storage of supplies that will enable an individual to act on the plan. Preparedness at an organizational or institutional level translates to the planning and preestablishment of adequate capacity and resources that will enable efficient relief operations. Prepositioning of war reserve and contingency stocks, such as that practiced by each of the U.S. Armed Services, has proven an effective means of increasing the speed of response to a conflict (Abell et al., 2000; Button, Gordon, Hoffmann, Riposo, & Wilson, 2010; Hura & Robinson, 1991). The private commercial sector, too, has been involved in prepositioning strategic safety stocks in supply chains with evolving forecasts (Schoenmeyr & Graves, 2008), capacity = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -7- constraints (Schoenmeyr & Graves, 2009), and non-stationary demands (Graves & Willems, 2002, 2008). Disaster response is a function of the preparation that took place prior to the disaster event, as well as the coordination of available supplies and distribution capacity. The first part of the response consists of gaining situational awareness of events and conditions on the ground in the disaster area through the collection of available information, and then using this information and awareness to generate an operational picture that will inform the nature, scale, and timing of the response. The result of this collection of information and establishment of situational awareness is a needs assessment or requirement for assistance. The response itself is largely comprised of the tactical activities that must take place to move needed supplies to those parts of the disaster area that have the most critical demand, given the available resources at hand. Disaster recovery consists of stabilizing the disaster area and improving the living and economic conditions of those affected by the catastrophic event. The recovery phase means different things to different organizations. For the military, the recovery phase likely signals the beginning of drawn-down or redeployment operations, whereby military personnel and equipment are withdrawn and responsibility turned over to civil authorities. For non-governmental and non-military aid organizations, the recovery phase may consist of establishing semi-permanent camps, aid stations, or warehouses to shelter displaced persons; delivering critical services that cannot be provided by other civil authorities; and coordinating the storage and distribution of supplies that are otherwise unavailable or in short supply to the local population. Studying the life cycle of recent disasters offers insight into both short-term and long-term consequences. It also provides us with numerous lessons to form effective strategies for mitigating future disasters. However, in order to formulate such strategies, we need to understand disasters in terms of their speed and scope. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -8- IV. Disaster Classification Disasters are often classified based on the speed of onset and the source or cause of the disaster (Ergun, Heier, & Swann, 2008; Van Wassenhove, 2006). However, in our research we focus on four disaster scenarios that are combinations of the geographic dispersion of the disaster (dispersed or localized) and its speed of onset (slow or sudden), as discussed by Apte (2009) and described in Figure 3. We differentiate local from dispersed disasters in terms of the number of civil administrative districts impacted, such as cities, counties, townships, parishes, prefectures, provinces, or states. As the number of civil administrative districts increases, so does the geographic area impacted, resulting in an increase in the complexity associated with responding to the disaster. It is the coordination of effort across multiple districts, coupled with the size of the relief requirement, which frustrates the effectiveness of humanitarian assistance and disaster response operations. Slow-onset disasters are defined as those that allow potentially affected populations time to react in order to mitigate the impact of the disaster, whereas sudden-onset disasters allow little to no time to react to the disaster event. The disaster classification suggests that the level of difficulty in the logistics execution is less onerous in the case of localized, slow-onset disasters (depicted in quadrant III of Figure 3) because there may be adequate lead-time and local resources to prepare for the response. We next discuss four specific disaster cases that exhibit different onset and localization characteristics, as illustrated in Figure 3, and serve as exemplars of strategies that are appropriate to specific disaster types, as described in the discussion section. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -9- Dispersed Localized IV 2009 H1N1 Flu Pandemic I 2004 Indian Ocean Tsunami III 2005 Hurricane Katrina II 2010 Haiti Earthquake Slow Onset Time Sudden Onset Figure 3. Classification of Disasters (Apte, 2009) A. Indian Ocean “Boxing Day” Tsunami of 2004 Dispersed and sudden-onset disasters (depicted in quadrant I of Figure 3) tend to be the most catastrophic in humanitarian terms because they lack warning in advance of their onset, and they impact large geographic areas that often cross multiple civil administrative areas, making coordination critical and difficult. The Indian Ocean Tsunami of 2004 was the result of a 9.1 magnitude earthquake and was responsible for more than 227,000 deaths, more than 500,000 injured, over 2 million missing, and more than 1.5 million displaced persons across more than 12 countries (Greenfield & Ingram, 2011). The destruction was primarily limited to the coastal regions (Samek, Skole, & Chomentowski, 2004), but was dispersed across so many countries that relief efforts were frustrated by the lack of complete reports of the damage to those countries affected and of the specific types of aid and assistance needed most. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 10 – Most of the people affected by the tsunami did not actually know it was coming. Because the earthquake occurred far offshore, those affected on land were not aware of it and had no way of knowing the tsunami was coming. The earthquake occurred so far away that it was not felt. The key problem was notification. Agencies that did sense the earthquake were not able to effectively notify those areas that might be potentially affected and even if they did the agencies did not have adequate means of disseminating the potential threat of a tsunami to all those that might be affected. It should be noted at this juncture that there was no tsunami warning system in the Indian Ocean. Some areas, particularly the Banda Aceh region of Sumatra in Indonesia, lacked a basic, functioning transportation infrastructure, which imposed severe capacity constraints on the flow of inbound supplies. B. Haiti 2010 Earthquake A sudden-onset disaster, even if localized (depicted in quadrant II of Figure 3), creates operational difficulties that are greater than circumstances where the onset is slow, but less than if the catastrophe were both rapid in its onset and geographically dispersed. Sudden-onset disasters deny authorities and the public time to prepare for the consequences of the disaster event and, therefore, tend to exact a much higher human cost. The earthquake that struck Haiti on January 12, 2010, measured 7.0 in magnitude on the Richter scale, resulting in more than 200,000 dead (United Nations, 2010). Poorly designed and constructed buildings, bridges, and other infrastructure resulted in significant losses, the creation of large debris fields and obstructions to transportation, and a need for large-scale rescue efforts of those trapped alive underneath concrete and steel wreckage. The government of Haiti was immobilized with a significant percentage of the national leadership dead or missing as a result of the earthquake. With little ability to assess damage or mobilize and manage the few resources that were not destroyed in the quake, the surviving population were left to rely on the response of other nations to help rescue those trapped in collapsed buildings and to provide = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 11 – food, water, medicine, and shelter. Lack of physical infrastructure, especially in underdeveloped and poor countries, causes long lead-times in transportation, which was evident in Haiti. The consequences of poor governance and weak institutions were evident in the Haiti disaster, and so, in spite of the proactive deployment from the rest of the world, suffering persists. C. Hurricane Katrina On August 29, 2005, Hurricane Katrina struck the city of New Orleans. It was known days in advance that the hurricane might make landfall in New Orleans, and although the city had warned residents, there were many who remained and were killed, stranded, or left homeless as a direct result of the storm’s violence or the failure of the levee system that otherwise protected the city from flooding. Hurricane Katrina was a slow-onset, localized disaster (see quadrant III of Figure 3) and one of the most devastating and costly hurricanes to strike the United States. Once the storm had passed, more than 80% of the city of New Orleans was under water, approximately 1,700 people were dead, 1 million persons were displaced, and an estimated $135 billion in damage along the Gulf coast was incurred (Plyer, 2010). The official plan for the city was for displaced residents to gather in the New Orleans Superdome football arena in the downtown center as a refuge of last resort. However, due to failed infrastructure and lack of planning for needed supplies to be delivered to the affected area, those who sought refuge during the critical first week following the landfall of the storm found thousands of people confined in a large open building whose roof was torn open and which had no functioning utilities, such as electricity or water. The state of Louisiana activated the National Guard and after several days, buses were organized to begin evacuating those still in the city to outlying areas. D. Influenza “Swine Flu” Epidemic of 2009 Quadrant IV describes a context where the onset is slow but the affected area is geographically dispersed. When the disaster area consists of a large or scattered = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 12 – geographical area, it may take substantial planning, resource allocation, and coordination among the military, humanitarian organizations, and local, federal, and perhaps even foreign, government representatives. The 2009 influenza epidemic is an example of a slow-onset, geographically dispersed disaster event affecting multiple countries (see Figure 4). The epidemic was responsible for more than 14,000 known deaths (European Centre for Disease Prevention and Control [ECDC], 2010), which occurred throughout the world. Subsequent research has shown that the seriousness of the influenza cases was not necessarily greater than other influenza outbreaks (Centers for Disease Control and Prevention [CDC], 2010), but the population affected was different, with children affected in much higher numbers than other influenza outbreaks on record at the time (Belongia et al., 2010). Although the numbers of people who have died from the H1N1 influenza have been modest since the pandemic in 2009, there remain