Posters

Poster sessions were held during the conference and below is the list of posters that were presented at the designated session times. Click on the title of a poster to jump to its abstract. Posters

Poster Listing

An Institutional Framework for Disaster Recovery

David Swanson (University of Arkansas)

Natural disasters have increased exponentially in the last century. Disasters reported from 1900-2008 have elevated from 50 disasters per year to over 500 (Emergency Events Database 2008). Some of the developing areas of the world are increasingly susceptible to natural disasters because of population growth and the high concentration of people in disaster prone areas (Brown 1979). The capacity available to respond to this increased frequency of natural disasters is inadequate. The United States response system in place has antiquated logistics systems, poor planning, and is under staffed. In 2006, the Senate report on Katrina concluded, “FEMA’s logistics failure during the Katrina crisis was no surprise.” On the contrary, many private responses to hurricane Katrina were successful and beneficial. To improve the situation, lessons can be learned from the private sector.

The author proposes frameworks for institutional response in disaster recovery logistics. This study begins with an overview of humanitarian logistics literature and the differences between humanitarian logistics and commercial logistics. The study then provides an overview of the United Nations and FEMA policies and operational procedures, a synopsis on where existing policy is inadequate, and ultimately proposes operational frameworks that can be used by the private sector to assist in disaster recovery. The frameworks are discussed and rated by logistics experts using a Delphi technique. Statistical analysis of the responses suggests ranking the frameworks by level of importance. Comments by the experts are also compared with the humanitarian logistics literature. Then, patterns and similarities are drawn between expert comments and the literature. The research ends with conclusions and suggestions for management about how corporations can become involved in disaster recovery.

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Analytic Solution to the Susceptible-Infective Disease Spread Model with Varying Contact Rate

Hamed Yarmand (North Carolina State University), Julie Ivy (North Carolina State University)

A common class of epidemiological models developed for the spread of infectious diseases is the Kermack-McKendrick model and its variations. These models are represented as systems of ordinary differential equations which in most cases are strongly nonlinear and cannot be solved analytically. In this research, we consider SEIR and SI models, two variations of Kermack-McKendrick model. The model names are based on the epidemiological classes included in the model: S for susceptible, E for exposed, I for infective, and R for recovered. One important parameter which affects the infection rate is the contact rate, the rate at which individuals make effective contacts (contacts which result in the disease transmission). We let contact rate be a function of number of infective individuals, which is an indicator of the disease spread during the course of the outbreak. We use the Markov process approach to represent SEIR and SI models as continuous-time Markov chains. The result would be a pure death (or birth) process with state-dependent rates for the SI model, for which we find the transient as well as the steady-state probability distribution of the associated continuous-time Markov chain by solving the Kolmogorov forward equations. Finally we use the solution to the Markov chain to find the analytic solution to the original SI model.

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C4G BLIS

Hiral Modi (Georgia Institute of Technology), Ruban Monu (Georgia Institute of Technology), Santosh S. Vempala (Georgia Institute of Technology)

We describe the C4G Basic Laboratory Information System (BLIS), a joint initiative of Computing for Good(C4G) at Georgia Institute of Technology, the Centers for Disease Control and Prevention (CDC) and Ministries of Health in several countries in Africa. BLIS focuses on addressing two major areas of public health systems in developing regions: (1) the need to efficiently manage and maintain data about patients, specimens and test results generated within a laboratory facility and (2) the need for an efficient way of dissemination of aggregate laboratory data to public health officials. The system is designed to work in low-resource laboratories and across sites with intermittent or no internet availability. To overcome challenges of varied practices, workflow and terminology being utilized across laboratories in PEPFAR countries, the system has been developed to enable each laboratory to customize and configure the system in a way that suits them best. We outline other challenges that developing the system entailed.

C4G BLIS in its current version is a low-cost, easy-to-configure solution that enables the laboratories to manage clinical data and disseminate aggregate trends in real time. A pilot phase is ongoing in several laboratories in Cameroon and Uganda with similar efforts to begin in Ghana and Tanzania. Throughout this pilot phase, the emphasis has been on incorporating feedback as it is received and sending out regular, incremental updates to the pilot laboratories. By the end of this period of testing with the volume of clinical laboratory data being accumulated, we aim to obtain a stable and reliable version of the system. If quantitative measurements of benefits, usability and sustainability of C4G BLIS indicate that it is an effective tool for laboratory information management, then it be scaled up to all other laboratories within the participating countries.

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Capacity Factor Analysis: A Decision Support Model for Selecting & Designing Basic Needs Infrastructure in Developing Countries

Justin Henriques (University of Virginia), Garrick Louis (University of Virginia)

Lack of access to basic needs infrastructure in developing countries has negative public health, environmental, and economic effects. For example, more than 1.5 million deaths per year are caused by air pollution from the use of wood, dung, coal, and other traditional fuels as household energy. Most of these deaths are young children and their mothers, with nearly 800,000 child deaths per year resulting from indoor air smoke from cooking. In the case of water access, 884 million people are without safe drinking water and 2.5 billion without improved sanitation, leading to 1.6 million deaths per year in low-income countries. Safe drinking water is essential to human health, is a basic human right, and is a necessary condition for disease reduction.

This research presents developments in Capacity Factor Analysis, a decision support model first developed for the systematic selection of water and sanitation in developing communities, and extended by the author for household energy systems. The four components of this model are a (1) multi-criteria community assessment, (2) multi-criteria technology assessment, (3) matching algorithm, and (4) implementation design. The community assessment is a tool for systematically rating the capacity of a developing community to manage technology. The technology assessment is an elaborate scoring of the capability required to manage technologies. From these two assessments, the matching algorithm guides decision makers in the selection of household energy technologies that are within the community’s capability to manage.

Author’s contributions include the extension of the model to household energy and new matching algorithms and technology assessments for water and greywater. The author has applied the model in Namawanga, Kenya for the selection, design, and implementation of biogas digester for cooking and photovoltaic technology for lighting. The author further applied the model in Cimahi, Indonesia for assessing the urban water supply.

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Case Study of a Multi-disciplinary Engineers Without Borders Project in Njinikom, Cameroon

Hannah Kates (Georgia Institute of Technology), Courtney Pare (Georgia Institute of Technology), Christian Weil (Georgia Institute of Technology), Chris Quintero (Georgia Institute of Technology), Andrew Foote (Emory University/Georgia Institute of Technology)

Engineers Without Borders Georgia Tech (EWB-GT) is currently working with a community in northwest Cameroon, to increase their access to clean water. The group is pursuing a multifaceted solution. Now in the 3rd year out of the 5 year plan, the project serves as a student-led case study for “the opportunities and challenges in humanitarian and world health” (Health Humanitarian Logistics-Objective).

The project began with EWB-GT assessing the community, understanding the community challenges and values. With this understanding the group was able to make simple improvements to the water quality and quantity of community’s current water distribution system. The next step involved designing a new system to create access to safe drinking water to the upper third of the community. After an extensive alternatives assessment, considering a wide range of water quantity improvements, EWB-GT and the community decided to implement a well with a solar pump, panels and distribution system. Throughout the project, every initiative was supplemented with hygiene and sanitation behavior change messages.

We found these educational messages to be vital to the success of technological solutions and to a wide scale of health problems in the community. In terms of hygiene education, it was found to be effective to train a recently formed hygiene, sport, sanitation, and theatre youth group as peer educators. These peer educators were empowered to “be the change they wanted to see in the world” by administering hygiene, sanitation, malaria and respiratory infection behavior and health questionnaires. The peer educators were trained to supplement the questionnaires with best practice promotion for each behavior.

EWB-GT has been extremely successful in finding a multi-faceted sustainable solution to a complex problem. The success can be contributed to EWB-USA’s framework of community commitment, extensive review and documentation, and a multidisciplinary team of students, engineers, non-engineers, faculty, and industry.

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Catch-up Scheduling for Childhood and Adult Vaccination

Hannah Smalley (Georgia Institute of Technology), Faramroze Engineer (University of Newcastle), Pinar Keskinocak (Georgia Institute of Technology), Larry Pickering (Centers for Disease Control and Prevention)

Purpose and Scope: Recommended immunization schedules for children, adolescents, and adults are published annually by ACIP. These schedules contain specific rules regarding the timing of each vaccine, as well as gaps between different doses of the same vaccine. These rules must be followed when constructing a catch-up vaccination schedule for someone who has fallen behind on one or more vaccinations – a task which is challenging and time consuming. Inappropriately constructed schedules may prevent timely administration of vaccination, potentially increasing the risk for contracting a vaccine-preventable disease.

Methods: We developed easy-to-use decision support tools which construct optimized catch-up immunization schedules by means of a dynamic programming (DP) algorithm. Required input for each tool consists of the user’s vaccination history, which is entered via a user-friendly interface, and vaccine-specific recommendations compiled in a vaccine library. Output from each tool is a personalized immunization schedule which gives exact dates for all future doses to be administered.
Results: Given an individual’s vaccination history and vaccine-specific recommendations for that individual as determined by ACIP, the tools consistently construct an optimized immunization schedule for remaining doses to be administered. Schedules constructed maximize the number of doses given, and minimize the total delay in administering doses.

Conclusions: The problem of constructing catch-up immunization schedules is faced regularly by health-care professionals. The tools developed provide a means of constructing optimized schedules quickly. The tools targeting children through age 6 and adults ages 19+ are available for download from CDC’s website (http://www.cdc.gov/vaccines/recs/scheduler/catchup.htm, http://www.cdc.gov/ vaccines/recs/Scheduler/AdultScheduler.htm). The tool for children has been downloaded over 67,000 times since June 2008, and the tool for adults, available since January 2010, has been downloaded over 12,000 times. A companion tool for adolescents ages 7 through 18 will be available early 2011.

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Decisions in Disaster Recovery Operations: A Game Theory Perspective on Actor Cooperation, Communication, and Resource Utilization

John Coles (State University of New York at Buffalo), Jun Zhuang (State University of New York at Buffalo)

Using perspectives from game theory in the problem of cooperative interaction between international and local agencies, we discuss the potential for improvement in humanitarian logistics using cooperative strategies in the developing world. The Indian Ocean tsunami which struck on December 26th, 2004 killed over 160,000 people and destroyed much of the infrastructure in the region. As a result, one of the largest international relief efforts in modern history was mounted to save both life and property and stabilize the devastated region. With the recent disasters in Haiti and Chile in January and February 2010 respectively, the need for a more holistic approach to interagency cooperation has become increasingly clear in order to increase the efficacy of these partnerships. A lack of sensitivity to these critical issues could render the desired positive long-lasting changes or recovery of the region impossible due to the failure of aid to address economic and social issues in a culturally acceptable manner. Although sensitivity to cultural issues is challenging in the initial response phase, identifying and employing sustainable initiatives throughout the recovery phase is critical to the acceptance of aid and long-term recovery of the region.

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Designing Optimal Water Quality Monitoring Network for River Systems and Application to Altamaha River

Chuljin Park (Georgia Institute of Technology), Seong-Hee Kim (Georgia Institute of Technology), Ilker Telci (Georgia Institute of Technology), Mustafa Aral (Georgia Institute of Technology)

The problem of designing a water quality monitoring network for river systems is to find the optimal location of a finite number of monitoring devices that minimizes the expected detection time of a contaminant spill event with good detection reliability. We formulate this problem as a stochastic optimization problem with a stochastic constraint on detection reliability where both detection time and reliability need to be estimated by simulation. Existing Optimization via Simulation (OvS) algorithms with global or local convergence are not directly applicable to our problem because they are developed without consideration of stochastic constraints. We propose a method called Penalized Objective (PO) that integrates general stochastic constraints into the original objective function and thus converts an optimization problem with stochastic constraints into an unconstrained problem. Then we apply PO to the water quality monitoring problem for the Altamaha River and solves it with an OvS algorithm called the nested partition method.

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Development of a Virtual Training System for Transitional Shelter Management in Second Life

Fuminori Toyasaki (York University), Ali Asgary (York University), John Reid (TRP360), Albert Kong (York University)

Post disaster shelter management is an important element of disaster response and recovery operations that requires well trained personnel and volunteers. Virtual learning systems (VLS) can be an effective means of enhancing, motivating, stimulating, as well as for reducing educational costs and have great impacts on the modernization of learning. The paper presents the outcome of an ongoing project aiming to develop a virtual learning system in form of a virtual transitional shelter management (VTSMS). The proposed system simulates the agents who are involved in management of transitional shelter for the purpose of training and education of students, practitioners, and volunteers. In this VTSMS, there will be two types of agents: agents which are controlled by the system using Agent Based Modeling (ABM); avatar agents which are controlled by the real persons (i.e. students, or volunteer). We simulate the VTSMS agents and environment and their interactions and allow students to be part of the simulation by playing the role of transitional shelter management agents in a typical emergency scenario. The VTSMS will be a significant teaching, learning, and research tool in transitional shelter management field because: 1) access to and use of real transitional emergency shelter sites by students is very limited; 2) simulation of post disaster/emergency transitional shelter sites in real world for training purposes are very costly and beyond the budget limits for many educational institutions, Humanitarian NGOs; 3) unlimited number of simulations can be developed and tested. This paper explains various aspects of this system and its usage by students.

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Disaster Management: Repositioning Disaster Centers and Determining Stock Levels

Nur Timurlenk (Bilkent University), Okan Dukkanci (Bilkent University), Oner Kosak (Bilkent University), Ali Irfan Mahmutogullari (Bilkent University), Hasim Ozlu (Bilkent University)

In our project, the aim is to reposition disaster centers of Turkish Red Crescent (TRC) and determine stock levels. In disaster management, TRC is responsible for interventions aiming to rescue as many people as possible by efficient techniques. Disaster victims are provided with relief services such as sheltering and food. As their organizational goal, TRC wants to reach disaster places in 2 hours to supply disaster victims with urgent sheltering and psychological support. Moreover, TRC has to keep reasonable amount of inventory for an emergency so that they can supply enough relief material for victims in a short time. To observe whether they are successful with current applications, we examined current strategy of TRC in disaster interventions. We evaluated centers by modeling current locations via ILOG OPL optimization-tool and we found out that with these locations, TRC cannot cover whole country in 2 hours. Moreover, when stock levels are examined after an abroad relief, we observed that they are under critical levels. In order to effectively solve these problems, a mathematical model -including both locations and inventory problems- is constructed. In this model, we give risk points for each city/district so that a place having higher risk point becomes a powerful candidate for being a disaster center or model lessens distances between disaster centers and risky settlements, in addition to 2 hours constraint. Moreover, model allows multi-sourcing so that relief materials can be supplied from different disaster centers in an emergency case. We coded the model in ILOG OPL to determine the locations of TRC centers and their stock levels. In order to observe the implementation of proposed system, we developed a simulation model. For sustainability of model, we provided TRC with a user interface to open or close a disaster center and to determine stock levels for future needs.

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Disaster Relief Routing: Integrating Research and Practice

Luis de la Torre (Northwestern University), Irina Dolinskaya (Northwestern University), Karen Smilowitz (Northwestern University)

Disaster relief presents many unique logistics challenges, with problems including damaged transportation infrastructure, limited communication, and coordination of multiple agents. Central to disaster relief logistics is the distribution of life-saving commodities to beneficiaries. In order to understand the characteristics of disaster relief goods distribution in practice, we have interviewed over twenty-five representatives from organizations involved in relief, including U.S. government organizations, non-governmental non-profit organizations, and commercial partners of government and non-profit relief organizations. We describe key findings from these interviews, such as practices in allocation of limited goods, safety issues, and the implications of limited technology on the transportation and distribution of goods. We discuss characteristics of current operations research models in disaster relief goods distribution from a comprehensive survey of these models. We identify areas with great potential to build on the current body of relief transportation models, based on our interviews and literature survey. Of particular significance, we find that uncertainty is an important characteristic of goods distribution occurring in many parts of the distribution process. Areas with high uncertainty identified by relief organizations include availability of the supply of goods; availability of delivery vehicles and drivers, uncertainty in need for goods, especially in the earliest phases of relief; and uncertainty in the ability to deliver to remote and rural beneficiaries due to safety concerns. A number of authors have addressed uncertainty in distribution of relief goods in modeling the initial effects of a natural disaster’s damage to pre-positioned goods, transportation infrastructure, and uncertain demand. Along with many other areas of potential future work, there is great possibility for building on current stochastic relief distribution models to represent uncertainty in post-disaster relief beyond a disaster’s initial damage.

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Disease Spread Model to Evaluate the Effectiveness of Home Confinement Strategy during Pandemic Influenza

Arsalan Paleshi (University of Louisville), Gerald W. Evans (University of Louisville), Sunderesh S. Heragu (University of Louisville), Kamran S. Moghaddam (University of Louisville)

Pandemic influenza has caused large-scale casualties and billions of dollars of loss in human history. The ever changing characteristics of the influenza virus makes new pandemic attacks unavoidable. Intervention strategies to mitigate the transmission of the disease are of great importance for authorities as an alternative to reduce the ill effects of a pandemic attack. The aim of this study is to evaluate the effectiveness of a home confinement intervention strategy on the reduction of the number of infected people during the course of a pandemic influenza. According to this strategy infected people with disease symptoms are confined to their homes until they recover. An agent based simulation model is developed to depict the progress of the disease within the body, and the interaction of agents (i.e. individuals) in various environments such as households, schools, workplaces, and communities in a US metropolitan area. The model is run before and after application of the intervention strategy. The results show that home confinement reduces the number of infected people by 31% when 50% of the infected individuals comply with the rules.

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Engineering a Specimen Transport Carrier: Collaborative Humanitarian Research and Design for the Public Good

Victoria M. Gammino (Global Immunization Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention), Michael F. C. Moreland (SEEDR L3C), Olen Kew (Division of Viral Diseases, US Centers for Disease Control and Prevention), Sue Gerber (Global Immunization Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention)

BACKGROUND
Temperature stability is critical for some laboratory specimens, medicines, biologics, and diagnostic test kits used in public health, because freezing and heat exposure can affect the viability of temperature-sensitive products. For diseases such as polio, which is slated for global eradication, case-confirmation is dependent upon viral isolation in fecal specimens. Thus, a well-functioning “reverse” cold chain (RCC) system—the temperature-sensitive transport chain between the field and laboratory setting—is critical. In the absence of specifically-designed RCC equipment, healthcare workers improvise specimen transport containers (STCs) from available materials. Breaks in RCC can destroy specimens, leading to missed cases and inaccurate surveillance.

OBJECTIVE
Through a unique public-private partnership, stakeholders from the private, public, and academic sectors engineered an STC to support polio eradication efforts and other laboratory-based activities requiring thermally-stable transport equipment. Although no international RCC performance standards currently exist, we sought to provide maximal “cold-life” (time between 2-10C? at 37C? ambient) while preventing inadvertent freeze-thaw cycles, designing for a minimal carbon-footprint, and simplifying pack-out requirements to prevent user error and cargo damage.

METHODS
SEEDR utilized HEEDS, a proprietary engineering software to optimize materials and design elements for a one-liter STC. This process included three-dimensional finite element modeling for virtual mechanical and thermal simulation and physical prototyping for validation and field testing.

RESULTS
Engineering data demonstrate that the one-liter STC prototype holds 24 standard fecal specimen tubes between 2-10C for +/- 65 hours without freezing. The STC design affords access to the specimen compartment without exposing icepacks, thereby improving temperature stabilization over existing ad-hoc carriers.

CONCLUSIONS
Identification of a durable, lightweight and temperature stable container to support vital public health activities is critical in the global fight against diseases. This unique public-private partnership demonstrates the value of humanitarian design to bring together new technologies and engineering processes to create life-saving products.

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Evaluation of Reverse Cold Chain for Wild Polio Virus Surveillance

Allison Taylor (US Centers for Disease Control and Prevention, Johns Hopkins Bloomberg School of Public Health), A.J. Williams (US Centers for Disease Control and Prevention), Steve Oberste (US Centers for Disease Control and Prevention), Steve Wassilak (US Centers for Disease Control and Prevention), Mark Pallansch (US Centers for Disease Control and Prevention)

BACKGROUND
Polio is targeted for global eradication. As wild poliovirus (WPV) transmission decreases, accurate surveillance based on isolation of poliovirus from stool specimens is critical to determine the extent of circulating virus. To maintain viability, specimens are transported to the laboratory via “reverse cold chain” (RCC), ideally at a controlled temperature of 2- 8?C or continuously frozen.

OBJECTIVES
1) Evaluate RCC systems to identify current polio specimen collection practices and improve transport guidelines; and 2) Measure the impact of temperature on virus viability in specimens.

METHODS
Electronic monitors were placed in specimen carriers to measure specimen transport temperatures in 41 RCC cargo shipments during 2010. Monitors recorded time and temperature every 30 minutes from specimen collection to receipt in the laboratory. We measured decay curves over 28 days at varying temperatures, using known-titer WPV-positive stool, to assess impact of temperature on specimen virus titers.

RESULTS
Of the 17094 temperature readings, 1,776(10.4%) were within the recommended 2- 8?C range; 13,081 (76.5%) readings were below 2?C and 2,237 (13.1%) readings were above 8?C. Mean travel time from collection to receipt in the laboratory was 220.7 hours. Provisional data indicate WPV was greatly reduced over time in specimens stored at increased temperatures.

CONCLUSIONS
Defining the effect of observed field transport conditions on specimen viability contributes to quality assurance of laboratory results. Failure to identify WPV cases in the last stages of polio eradication as a result of specimens stored at high temperatures during transport might contribute to a false assurance that endemic WPV transmission has ended or result in failure of timely detection of WPV importations.

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Field Investigation on the Comparative Performance of Alternative Humanitarian Logistic Structures after the Port au Prince Earthquake: Preliminary Findings and Suggestions

José Holguín-Veras (Rensselaer Polytechnic Institute), Miguel Jaller Martelo (Rensselaer Polytechnic Institute), Tricia Wachtendorf (Disaster Research Center, University of Delaware)

This research analyzes the performance of alternative humanitarian logistic structures after the 2010 Haiti’s earthquake. The research is based on field work observations and interviews with individuals involved in the humanitarian response. In total, more than 10 trips were made to Haiti, Dominican Republic, and other centers of the Haitian diaspora such as Miami. The analyses clearly revealed the existence of two radically different humanitarian logistic operational models: (1) agency-centric efforts (ACEs); and (2) collaborative aid networks (CANs). The former refers to distribution efforts in which a relief agency attempted to conduct the entire chain of humanitarian logistic functions (ranging from the transportation of critical supplies to the disaster site, to the distribution of aid to the victims). After their arrival, ACEs had to create the human/logistic network to distribute the aid, gather the necessary manpower and equipment, and integrate the operations of the newly assembled network. The evidence shows that this was an insurmountable obstacle for the ACEs to overcome. On the other side, CANs designates the efforts of large networks of individuals—that already had in place large, operationally active, and well functioning human/logistic networks—and decided to extend their mission to humanitarian logistics. In contrast to ACEs, CANs were able to distribute substantial amounts of aid with great efficiency, and without a single incident or any of the problems that plagued the ACEs.

In essence, the limitations of ACEs to respond to large urban disasters in which the local leadership has been impacted are discussed. The fundamental implication is that the best way to distribute aid after large urban disasters is for the ACEs to focus on the transportation of large flows of aid to the disaster site; and then articulate with the local CANs to distribute the aid among the victims.

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GIS as a Decision Support Tool in Healthcare (Hospital and Bed Availability Analysis for Jefferson County, KY)

Trivikram Rao (University of Louisville)

The existence of hospitals within any community is critical to ensure the functioning of its people in a healthy, uninterrupted manner. This study analyzes the availability of hospitals and beds to the population of Jefferson County, Kentucky in an attempt to gauge the medical preparedness of the county under normal and emergency circumstances. The following Hypotheses are proposed for the purposes of this project:
1. A significant portion of the population in Jefferson County does not have immediate access to hospitals.
2. The number of hospital beds (and other proportional resources) available to the people of Jefferson County is insufficient, especially during emergencies.

Hypothesis #1 was proven to be right, especially for emergency scenarios as 54.16 % of Jefferson county hospitals do not have a hospital within a two mile radius. This value increases to more than 80% without access when only West Jefferson is considered. The number of hospital beds (and other proportional resources) per 1000 people for Jefferson county is a healthy 6.02, well above the state and national averages. However, when we consider individual districts, the west region has a ‘zero’ hospital bed per 1000 people index. Thus the author concludes that the new and current hospitals, beds and other medical resources need to be more evenly distributed throughout the county to enable greater access to the population and facilitate better emergency preparedness.

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Health and Humanitarian Logistics: What Beyond "Relief"?

Shibu Mani (Tata Institute of Social Sciences), Mohammed Irshad (Tata Institute of Social Sciences), Saswata Sanyal (Tata Institute of Social Sciences), Faisel Illiyas (Mahatma Gandhi University), Amalraj M (Ernakulam District Collectorate)

The humanitarian logistics in developing countries have a focus on the initial stage/period of the relief phase. The review of such an approach has increasingly been a felt need at the academic side of disaster management. We have aimed at finding the operational limits, opportunities and challenges within the frame work of humanitarian logistics by reviewing case studies/study (original research) from India on two aspects: (a)solar disinfection of contaminated drinking water (b)logistics operations in a cyclone affected region.

The introduction of custom made household level solar water treatment system (during 2001-2006 period)in different physiographic settings (arid, alluvial plains and coastal)but had prevalence of gastro-enteritis in common has shown effectiveness of the method by many well being indicators. The acceptance level of the technology for the long term has been found to be satisfactory with some people whereas with others practicing on a daily basis was found to be very low. It is important to consider the customer based needs in the innovation research for the effective technology transfer.

The field based review of logistic operations in the cyclone Aila affected region in India has shown that: majority of the organizations maintained a steady logistics flow for the first three to four months. The withdrawal of organizations (due to the limitations in operational capacity/time) put the community in a crisis situation almost similar to that of the initial days after the cyclone. How to resolve such an issue where thrust on social infrastructure development is below minimum standards is a question to be addressed.

The lack of social overhead capitals such as health and ware house infrastructures would critically challenge the estimated impact of humanitarian logistics. To go further up (beyond relief), there is a need for linking humanitarian logistics with the developmental paradigm which encompasses all sections of the society.

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Hub Location Model for Determining Relief Center Locations During Disaster

Ornurai Sangsawang (Clemson University), Mary Elizabeth Kurz (Clemson University)

After a natural disaster, victims need fast assistance as well as emergency supplies through an efficient distribution system. In this paper, we present the hub location model applied to the decision of locations for relief centers, which will provide medical care, basic human services, public information and transportation. The centers function as hub facilities which interact with affected areas and other centers. The distance is considered as an important factor since it directly affects the time required to get services to the public. The uncapacitated single allocation p - hub median model is considered with a speed-rate factor for flows which are directly transferred between hubs. By designating hubs before the natural disaster occurs, we can influence the order in which the transportation system itself is repaired, so that the routes between hubs can be prioritized. Well - recognized metaheuristics (Tabu Search and Simulated Annealing) are investigated with respect to solution and CPU time.

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Identifying Performance Measures in Inventory Management for Disaster Relief Operations

Indraneel Dabhade (Clemson University ), Selina Begum (Clemson University )

The aim of this study is to identify key performance measures for inventory management for disaster relief operations. The results are presented in a format of a balanced scorecard. The indicators have been divided into six major categories covering the horizon from value-based measures to keeping a track of the different utilization factors contributing towards effective and responsive inventory management strategies. A scoring system model has been developed to track the performance of the measures at all periodic reviews. Implementing the scorecard with the scoring model would help the different stake holders be informed of the inventory positions as well as the on field volunteers to optimize the ordering process.

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Improving Influenza Pandemic Mitigation Policy through Agent-based Modeling and Simulation Experiments

Michael Beeler (University of Toronto), Dionne Aleman (University of Toronto), Michael Carter (University of Toronto)

We assess influenza pandemic mitigation strategies using an agent-based simulation experiment. The scenarios tested include 11 levels of voluntary self-quarantine amongst infected individuals and two levels of public vaccination. Both self-quarantine and vaccination reduced the simulated pandemic’s impact considerably. The statistical properties of the distributions of the simulation output bear implications for pandemic simulation research designs and pandemic mitigation policy. We observed that simulation outcomes followed non-normal distributions and conclude that longstanding analytical approaches to pandemic modeling may be inadequate. The sample sizes required for properly designed simulation experiments exceed the capacity of most agent-based models under development to generate data. We discuss strategies for reducing the computational complexity of pandemic simulation models without undermining model validity.

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Improving the Performance of a Surgical Department

Pinar Keskinocak (Georgia Institute of Technology), Pengyi Shi (Georgia Institute of Technology), Monica Villarreal (Georgia Institute of Technology)

Purpose: Apply Operation Research tools to help improve the operations of a surgical department of a private hospital.

Methods: We conducted an empirical analysis of the surgical data from 14 operating rooms (OR) at University Hospital in Augusta, Georgia. We assessed the current scheduling of surgical procedures and the operational guidelines by computing several OR performance measures, including the room utilization, distribution of turnover time and case duration. We also conducted a time study for the Surgical Care Center (SCC) to access the pre-surgery process and the labor levels. We tested some of our recommendations via simulation for their impacts on the entire surgical department.

Results: In the current scheduling system, the allocated OR time has two general components: patient setup and surgical procedure, and cleaning and room setup for the next case. From the empirical analyses, we identified major reasons causing tardiness of cases and delays of the procedures. First, cases could be delayed because the room was not available. Our analysis showed that the time allocated for patient setup and procedure was consistently insufficient, and so was for the time assigned for room cleaning and setup. Second, cases could be delayed because the patient was not ready. If the patient did not arrive at SCC on time, or the pre-surgery process took too long, the patient’s wheels-in could be delayed. Besides the assessment on tardiness, we also revised the OR budgeted labor by comparing with the daily needs of the surgical department, and made recommendations for a better labor planning.

Conclusion: We used Operations Research tools such as empirical analysis and simulation to assess the operations of the surgical department. Our results showed that the efficiency and performance of the system can be improved by better labor planning, efficient use of resources, and accurate prediction of case duration.

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Increasing Survival Chances of EMS Patients While Equitably Locating Facilities

Sunarin Chanta (Clemson University), Maria Mayorga (Clemson University), Laura McLay (Virginia Commonwealth University)

For public sector services, especially basic services, such as Emergency Medical Service (EMS), providing equity of service is an important factor to consider when allocating resources. In contrast to private sector services, the objective here is not to maximize profit, but to save more lives while minimizing inequity of access to service. We present a facility location model which considers the envy associated with p-serving facilities. The minimum p-envy model we present emphasizes survivability by incorporating a survival function which depends on the distribution of operating facilities. The inequity of the system is reduced by minimizing the differences in quality of service between all possible pairs of demand zones with respect to their ordered priority serving facilities weighted by the number of emergency calls in the zones. The chance of vehicles being busy is captured in order to reflect the real operating system using the hypercube model. Since the objective function is complex, a heuristic approach is developed to solve the problem. The model was tested on a real world data set from the EMS system at Hanover County, VA, and also compared to other location models. The results indicate that placing facilities more effectively, following the solution suggested by the proposed model, can save more lives without adding extra facilities. The solution of the proposed model yielded higher number of lives saved than other location models selected to be compared. Sensitivity analysis revealed that the advantage of the proposed model increases as the number of vehicles decreases. Furthermore, the model performs well in terms of coverage, a traditional EMS performance measure. These results provide guidance that is useful and actionable to the field of EMS response planning.

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Influence of Framing on Inventory Prepositioning Decisions

Jaime Castañeda (University of Lugano), Paulo Goncalves (University of Lugano)

Some Humanitarian Relief Organizations (HROs) must preposition emergency items to prepare to serve beneficiaries in the aftermath of a humanitarian emergency. These prepositioning decisions are suitable of being modeled by the Newsvendor Model given that the demand of emergency items coming from beneficiaries is not known beforehand.

Laboratory experiments on the Newsvendor Model have shown that prescribed inventory prepositioning is lower than the optimum when a high stock is required, whereas the prescribed inventory prepositioning is higher than the optimum when a low stock is required; this is known as the anchor and insufficient adjustment bias.

In an experimental design based on the cognitive dissonance theory, we argue that the perceived importance of an item in joint decisions may de-bias the anchor and insufficient adjustment bias. According to this theory, when two simultaneously held cognitions (pieces of knowledge) are inconsistent, the decision maker will experience a state of cognitive dissonance. The theory states that dissonance, being unpleasant, motivates the decision maker to change his/her cognitions.

The design considers two manipulations: a cognitively consonant manipulation, where we bundle a high safety stock decision with a high-importance item and a low safety stock decision with a low-importance item, and a cognitively dissonant manipulation, where the safety stock conditions are reversed. We compare their results against the results of a baseline treatment where such decisions are made independently. Analyses show that bundling two consonant decisions improves results, while bundling two dissonant decisions worsens them. Moreover, the consonant decisions outperform the dissonant ones.

These preliminary results suggest that framing may influence newsvendor-type decisions. In particular, bundling newsvendor-type decisions that differ in their perceived importance may motivate decision makers to change his/her cognitions and, hence, may help to de-bias ordering behavior in newsvendor-type environments.

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Integration of Field Test Data for Validation and Analysis of a Cold Chain Simulation Model

Trustin Clear (Georgia Institute of Technology), Michael F. C. Moreland (SEEDR L3C), William Rouse (Georgia Institute of Technology)

This work continues an effort to understand the role of insulated containers in the vaccine and specimen transport cold chains (CC), vital to public health activities worldwide. To facilitate this, we have constructed a discrete event simulation to model container performance in realistic distribution scenarios; our collaborators at SEEDR L3C, an engineering design firm, developed prototype containers designed to exceed WHO Performance Quality Standards for this type of equipment. Using data collected in field tests, we aim to validate the model, refine assumptions, and analyze CC performance with different container types.

A strategy is presented to use operational details and container performance data to construct and validate simulations of equipment field tests, and to make quantitative estimates of system performance using these simulations. These are critical steps in connecting improved container performance with value-creation in the CC, and represent progress toward the goal of building tools to aid public health activities, such as immunization and disease surveillance.

Field tests, designed by CDC and SEEDR, will measure internal and external container temperature versus time during each transport leg; covariates will include refrigerant volume and condition, and transport mode. Data will be analyzed for each link in the distribution network, and model parameters will be adjusted to fit the calibration sample; model fit will be checked for remaining data. Individual link estimates will be combined to yield best-fit parameters for each container type, which will be used to construct a simulation of the complete distribution network.

Field tests begin in February 2011. The analysis of a theoretical data set is included to illustrate the methods under discussion, pending the availability of field test data to model the impact of containers on CC performance.

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Locating Facilities for the Strategic National Stockpile

Hugh Medal (University of Arkansas), Ed Pohl (University of Arkansas), Manuel Rossetti (University of Arkansas)

The United States Government has observed that a large-scale bioterror attack on a large city would necessitate such a large amount of medicine and pharmaceuticals that local inventories would quickly be depleted. To address this, the Strategic National Stockpile (SNS) was implemented to provide supplies that are ready to be deployed in a medical emergency. The SNS system consists of strategically placed warehouses around the country and reportedly has the capability to concurrently provide supplies for several emergencies in large cities within 12 hours.

The goal of this research is to recommend how to efficiently design the SNS system and give insight into how conflicting objectives trade off against each other. Specifically, we recommend where to locate warehouses and response vehicles in order to minimize the worst case response time. We use an integer programming approach to solve this problem; in particular we model the problem as a generalization of the set cover problem. We report computational results for our solution method and perform various trade off analyses.

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Logistics of Medical Attention for Relief Operations after an Earthquake in Bogotá

Raha Akhavan-Tabatabaei (Universidad de los Andes), Diomar Noreña (Universidad de los Andes), Luis E. Yamin (Universidad de los Andes), Wilfredo Ospina (Universidad de los Andes), Raha Akhavan-Tabatabaei Raha Akhavan-Tabatabaei (Universidad de los Andes)

Bogotá is situated in a region with a high risk of natural disasters including earthquakes, floods, and wild fires. Over the past few years the city officials have been developing high level emergency plans to cope with such disasters. For the specific case of earthquakes the emergency plan of the city lacks detailed measures such as the logistics of accommodating the injured people into the permanent and temporary hospitals within the first few days of disaster response.

This research has been conducted per request of the City of Bogotá in order to evaluate the current plan against a specific earthquake damage scenario and from the point of view of attending the injured and trapped victims within the first four days of an earthquake. The main objective of this study is to find the gaps in the current plan and suggest practical policies in order to cover the gaps.

We present a simulation model for five districts in the city of Bogotá to transfer the injured people to the temporary and permanent hospitals within the 96 hours of an earthquake with magnitude of 6.2 on the Richter scale, return period of 250 years, 23 kilometers of depth and epicenter distance of 39.5 kilometers from the frontal fault of the Eastern Andean mountain range in Colombia. The model uses information such as the current capacity and occupation rate of the permanent hospitals, the current number of ambulances in the zone, the approximate duration of routes in a destructed situation, among others. The principle outcome of this model shows the expected time to attend patients in the medical care centers and can be used by the government agencies to reduce the uncertainty impact on planning the logistics of the relief operations.

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Managing Debris Operations

Kael Stilp (Georgia Institute of Technology), Ozlem Ergun (Georgia Institute of Technology), Pinar Keskinocak (Georgia Institute of Technology), Antonio Carbajal (Georgia Institute of Technology), Monica Villarreal (Georgia Institute of Technology)

Debris removal is costly, long and complicated process requiring the careful consideration of both short term and long term effects on people’s health and safety, and the environment. In the short term, the main consideration is the clearance of debris to allow for the transportation of relief resources and access to disaster areas or critical facilities for lifesaving activities. Given that the debris may contain toxic or hazardous waste, one needs to weigh the benefits of rapid clearing with the long term impact to ensure that their management would not pose a future threat to human health or the environment.

The first stage of operations that we model, clearance of debris, is a computationally difficult network expansion problem over multiple periods. In this model there are big M constraints, which are a common and well known family of constraints which often make computation difficult. We use the idea of over-restricting these M values, using estimates to set them at much smaller values than is actually valid. These over-restrictions have shown an ability to both speed up heuristics but also give significant improvement in solution quality. Furthermore, we introduce a secondary heuristic which we can use to prove quality of the heuristic solutions generated.

For the second stage that we model, collection of debris, we consider the recent earthquake in Haiti. For Port-au-Prince we create a road network suitable for our needs, estimate debris across the network based on openly available data, and focus the model on minimizing time to completion. We then use various strategies to observe the impact of considering the road network and disposal site openings when designing a collection plan for Port-au-Prince.

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Mobilizing Disaster Communication with LifeNet

Hrushikesh Mehendale (Georgia Institute of Technology), Amit Prakash (Jamshetji Tata Centre for Disaster Management, Tata Institute of Social Sciences), Soma Sinha (Jamshetji Tata Centre for Disaster Management, Tata Institute of Social Sciences), Shibu Mani (Jamshetji Tata Centre for Disaster Management, Tata Institute of Social Sciences), Santosh S. Vempala (Georgia Institute of Technology)

In the wake of major disasters, the failure of existing communications infrastructure and the subsequent lack of an effective communication solution results in increased casualties and considerable resource wastage. Current options such as satellite communication are expensive and have limited functionality. A robust communication solution should be affordable, easy-to-deploy, require low-to-zero infrastructure, consume little power and facilitate Internet access.

LifeNet is a WiFi-based data communication solution designed for post-disaster scenarios. It is open-source software and runs on consumer devices including laptops, mobile-phones and wireless routers. A novel multi-path ad-hoc routing protocol present at its core, enables LifeNet to meet all the aforementioned needs. Important applications like resource-need matching, messaging, information management systems, etc. can be deployed over LifeNet.

After having obtained positive results in preliminary field tests, we have partnered with the Centre for Disaster Management at the Tata Institute of Social Sciences, India (TISS) for a pilot deployment. Guhagar, a village on the West coast of India has been selected for the deployment. Guhagar is a cyclone-prone zone. Recently (November 2009), a cyclonic storm ‘Phyan’ crossed the West coast of India over Guhagar causing massive destruction. Infrastructure (communication and transport) was severely damaged and many lives were lost. Failure of telecommunication as a result of damaged infrastructure was the identified to be the root cause of the high casualty count.

We are creating a LifeNet testbed in Guhagar, which would be integrated with the existing early warning systems. The testbed covers Guhagar, some villages nearby and a few hundred meters inside the sea. Currently we are establishing ties with stakeholders on and off the field. After a few refinements to LifeNet (based on site visits), the field deployment in collaboration with the TISS team, ready for the next emergency, is expected to commence in April 2011.

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Modeling Debris Cleanup Operations

Gary Fetter (Western Carolina University), Mauro Falasca (East Carolina University)

Within the area of disaster recovery, debris collection and disposal represents a major task that requires significant operational planning and control and can severely impact local, state, and federal financial resources. The unique nature of disaster debris and the extreme amounts generated as a result of the disaster event create challenges for decision makers that are not typically encountered during everyday solid-waste disposal operations. The collection and disposal of disaster debris can be quite challenging because the amount of debris is usually extremely significant and is generated very quickly (in a matter of hours or minutes, depending on the type of disaster), far exceeding typical amounts of solid-waste generated on an annual basis. In addition, in the case of large-scale disasters, debris is often spatially scattered throughout a large area encompassing several regions, counties, or states. This research is aimed at identifying the unique aspects of disaster debris disposal and developing a series of decision support tools to assist emergency management coordinators with allocating resources during debris cleanup operations. The nature and importance of debris cleanup to the success of recovery operations has primarily been discussed qualitatively in the relatively few articles published in the academic literature. This research represents an addition to the few quantitative research studies addressing debris cleanup, one of the most important and costly aspects of disaster recovery and management. We present a decision support system framework, discuss aspects of the knowledge base, model base, as well as the user interface, and show how an emergency management coordinator would use the system during ongoing debris cleanup operations. Finally, we demonstrate the usefulness of the system using real-world data from a past Atlantic hurricane.

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Modeling of Antibiotic Distribution in Response to Anthrax Attack

Adam Montjoy (University of Maryland), Jeffrey Herrmann (University of Maryland)

The release of anthrax spores as part of a bioterrorist attack into a highly-populated area will require a quick and efficient response from federal, state, and local public health officials to reduce illness and death. Deaths from anthrax can be prevented by the timely consumption of antibiotics. The distribution of antibiotics will occur at the county or state level. Public health emergency preparedness planners will need to organize various aspects of distribution including preparing Points of Dispensing (PODs) and routing vehicles from a central depot to deliver the antibiotics to the PODs. Timely delivery is important in reducing the likelihood that PODs run out of medication, disrupting operation. This problem is formulated as a capacitated vehicle routing problem over a short time frame. Deliveries to PODs will likely occur while dispensing of medication to exposed persons has already begun. Thus, planners seek to make even deliveries to all PODs with respect to time and quantity based on expected demand. This goal is captured in the objective function rather than having a traditional shortest time or least cost scheme. A solution to this problem is a schedule specifying a route for each vehicle, starting times for each delivery, and a quantity to deliver to each POD. Techniques explored include heuristics that separate the problem into routing and scheduling and an adaptive large neighborhood search for finding routes, which provides better schedules than routing by heuristic only. The problem is also formulated as a mixed integer program and solved with a column generation approach that attempts to solve the routing and scheduling simultaneously. The goal of this project includes implementing the formulation and techniques into freely-available software for public health emergency preparedness planners. This research is funded by the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response.

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Motivation for Health Information Exchanges: A Patient Crossover Study

Jacqueline Griffin (Georgia Institute of Technology), Hannah Smalley (Georgia Institute of Technology), Pinar Keskinocak (Georgia Institute of Technology), David Laborde (Emory University School of Medicine), George Mathew (Emory University School of Medicine)

To demonstrate the importance of a Health Information Exchange (HIE) between Atlanta area hospitals, we examine the rate of crossover among neurosurgical inpatients treated at Emory University Hospital (EUH) and Grady Memorial Hospital (GMH). We also identify the impact of diagnoses on crossover rates and movement patterns to determine where initial investments in HIE should be made. Using electronic medical record data from EUH and GMH, unique patients who visited both hospitals were identified through classification by name and age at time of visit. A study of the frequency of flow patterns by crossover patients, including time between visits, was conducted. The significant crossover, especially for patients with specific diagnoses demonstrate the importance of implementing a health information exchange between the two hospitals studied. An HIE could prevent duplicate testing and has the potential for improving patient care.

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Patient Allocation Problem during Pandemic Influenza Outbreak

Li Sun (University of Louisville), Gail DePuy (University of Louisville)

It is suggested that a future pandemic influenza is inevitable and likely imminent, considering recent incidents of H1N1 and the pandemic influenza cases in history (1918, 1957, and 1968). It is important to prepare response plans for how to react to a pandemic influenza outbreak. During an influenza pandemic outbreak, hospitals would be overwhelmed by the surge demand of influenza patients. The preparedness tasks cannot be accomplished by hospitals individually but rather require collaboration among hospitals both in planning and in response. This paper focuses on how to allocate the patients to appropriate hospitals in a healthcare network. Mathematical models are developed to optimize the patient allocation in terms of minimizing the patients’ cost of access to services (e.g. travel distance), balancing the workload among hospitals, and satisfying the hospital resource capacity. Moreover, the models help predict resource shortages during the outbreak and the hospitals can be alerted to consider increasing the medical capacity or requesting additional capacity from the state or national agencies.

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Policies for Blood Allocation in Developing Countries

Melih Celik (Georgia Institute of Technology), Ozlem Ergun (Georgia Institute of Technology), Mallory Soldner (Georgia Institute of Technology), Julie Swann (Georgia Institute of Technology)

Blood is a scarce resource, and it is especially so for developing countries. Adding to this the fact that it is also a perishable product, it becomes important to effectively allocate blood units. In this study, we consider the allocation of blood units to hospitals from a single collection center in a developing country. Demand is classified under two types: urgent and regular. Hospitals apply different policies to satisfy the incoming demand, and the collection center, observing the policies at each hospital, has to allocate the on-hand units before demand is actually observed. We first characterize the optimal allocation under two different usage policies: prioritization of urgent demand and first-come, first-served. We derive bounds on the relative performance of the two policies. We also consider the cases where allocation assumes a certain usage policy, but the actual policy is different than assumed, and find out the cost of not incorporating behavior. Furthermore, we assume a centralized system where demand is handled by a single unit, and derive how much improvement can be achieved over the decentralized system. Our tools are tested on a realistic example based on the US vaccination campaign in 2009.

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Prepositioning Supplies for Improving Efficiency Response under Predictable Natural Disaster Settings

Gina Galindo Pacheco (SUNY at Buffalo), Rajan Batta (SUNY at Buffalo)

In a natural disaster, demand for essential items like food, medicine and water arises from affected areas. For certain types of natural disasters, like hurricanes, it is possible to plan for prepositioning of supplies so as to improve the efficiency of the post-disaster relief effort. In this work, a preliminary model for prepositioning supplies in such a setting is developed. Our model includes a facility location analysis, since supply points are to be selected from a set of candidate nodes. Then, relief units to be prepositioned, will be sent from a Main Distribution Center (MDC) to the selected supply points in order to be used to satisfy promptly and efficiently the resulting demand once the disaster has occurred. Two decision are then to be made: the location of supply points and the storage level at selected places. The objective pursued is to minimize the total expected logistic cost. We consider the following components of the cost: the total distribution cost, the fixed cost of opening supply points and the cost associated with losing units at destroyed supply points. Supply units stored at destroyed supply points become useless during the emergency. Then if supply point i is destroyed, all the units planned to be sent from such a supply point are thought to be delivered from the main distribution center. Uncertainty related to forecasted demand is considered. A constraint limiting the maximum expected number of units allowed to get destroyed is included. A solution for a hypothetical case study is offered along with a cluster approach for larger problems. Then we will present our ongoing work related to an improved scenario-based model.

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Priority Dispatching Strategies for EMS Systems

Damitha Bandara (Clemson University), Maria Mayorga (Clemson University)

The major focus of an emergency medical service (EMS) system is to save lives and to minimize the effect of an emergency health incident by dispatching appropriate paramedic support to the scene. Achieving this goal depends on the arrival time of paramedic support to the scene. Rapid response times by EMS systems may reduce the fatality of the incident. Effective dispatching strategies for EMS systems provide rapid paramedic support and thereby increase patients’ survival probability in emergency incidents.

The objective of this research is to find dispatching policies for EMS systems in order to increase the survival probability of patients. The dispatching policies are developed incorporating the degree of the urgency of the call. A simulation model is developed to evaluate the performance of the EMS systems. Performance is measured in terms of patients’ survival probability rather than measuring the response time thresholds, because survival probability mirrors the patients outcome directly. Different response strategies are evaluated to obtain optimal dispatching policies. Results show that dispatching the closest vehicle is not always optimal and dispatching vehicles considering priority of the call leads to an increase in the expected average survival probability of the patients. A heuristic algorithm is developed to dispatch the ambulances incorporating the degree of the urgency of the call. Computational examples show that the dispatching algorithm is valuable in increasing the patients’ survival probability.

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Quantitative Models for Humanitarian Logistics

Begoña Vitoriano (Complutense University of Madrid), M. Teresa Ortuño (Complutense University of Madrid), Gregorio Tirado (Complutense University of Madrid), J. Tinguaro Rodríguez (Complutense University of Madrid), Javier Montero (Complutense University of Madrid)

This presentation focuses on the activity of the research group “Mathematical Models for Humanitarian Logistics” of Complutense University. Currently, its work is mainly devoted to the development of an integrated decision support system able to face two problems in humanitarian logistics and disaster management: the assessment of disaster consequences (and therefore the resulting needs of the affected population) and the on-terrain distribution of the humanitarian aid.

Concerning the first problem, recall that in the first moments after a disaster strikes somewhere in the world, NGOs and other humanitarian agents have to make urgent strategic decisions about the convenience of undertaking a relief operation, as well as about its size and nature. As the available information in these first moments tends to be confused, imprecise or incomplete, a fuzzy-classification approach based on historical knowledge about disasters is taken, in order to classify the severity of the consequences from a reduced set of easily accessible data. This enables NGOs decision-makers to devise both the nature and the amount of aid needed to relief the suffering population. The model has been validated using the EM-DAT database.

Once this aid reaches the affected region, several on-terrain decisions have to be made in order to distribute it, which leads to the second problem. Several criteria different than the cost, as for instance equity or time of response, unlike common business logistics models, are very important in this context of humanitarian crisis. Furthermore, other attributes as road reliability or ransack risk are also taken into account when designing the itineraries of the distribution plan. A flow model for humanitarian aid distribution able to deal with several criteria through a goal programming approach has been developed. Experiments to study the sensitivity of the results to the criteria considered and their relative importance have been also carried out.

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Real-Time Decision Support System for Healthcare and Public Health Sectors Protection - Gaps Identified in HPH and ESS Sectors and the Proposed Research Work

Aman Gupta (University of Louisville), Sunderesh S. Heragu (University of Louisville), Trivikram Rao (University of Louisville), Robert Kelley (University of Louisville)

The Healthcare and Public Health (HPH) and Emergency Support Services (ESS) are two of seventeen critical infrastructure sectors identified by the Department of Homeland Security (DHS). These two sectors have developed potential capability gaps that may be seen in the HPH and ESS infrastructures in the event of a pandemic influenza attack. The gaps or gap statements describe what the state-of-the-art is, relative to pandemic response, and what capabilities might be required for an effective response. A few of the gap statements identified by the DHS/OIP (Office of Infrastructure Protection) include, 2008-001-HPH, 2008-002-HPH, and 2008-004-HPH. To understand the potential gaps in the state of Kentucky’s HPH and ESS sectors, we conducted a study by interviewing sector experts to understand the decision making tools they may require in the event of a pandemic attack. Based on the interviews, we made the following observations: Most existing systems are really data collection systems and not decision support systems; Some of the systems are so complex that it is difficult to train employees to use them effectively and also for the employees to retain that knowledge to be able to use during an emergency; the systems are not interoperable, are not well integrated, and lack the ability to capture real time data required for making timely decisions. After an understanding of the gaps, research work was proposed to provide the operational capability to minimize or eliminate the gaps.

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Reducing the Environmental Impact of Public Health Supply Chains

Michael F. C. Moreland (SEEDR L3C), Victoria M. Gammino (Global Immunization Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention), Sue Gerber (Global Immunization Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention)

The temperature-controlled supply chains transporting vaccine and specimens in immunization, disease surveillance, and clinical diagnostic programs represent a critical facet of public health infrastructure. Beyond their scientific and clinical function, the thermally-insulated containers in these systems produce carbon-dioxide emissions from petroleum-derived material composition, manufacturing processes, transport, and use. With the detrimental impact of carbon emissions on human health, their reduction is an overarching objective for the public health community. [1]

SEEDR and CDC partnered to reduce the environmental impact of vaccine and specimen transport chains by engineering a fleet of reusable, recyclable, Montreal Protocol-compliant cold chain containers with reduced carbon footprints and improved thermal efficiency over the current best-in-class equipment.

Design optimization techniques were used to incorporate a biobased insulation material into three prototype containers to offset other emissions in the products’ lifecycle and improve thermal efficiencies. Standard methods [2] [3] were used to calculate Life-Cycle Assessments/Inventories (LCAI) and transport emissions. [4]

The biobased materials reduced the LCAI of an 18-liter container to -39.9 kg per kg of the product, averting 1,141 kg of carbon compared to an estimated 1,775 kg of carbon emitted for similarly sized products. The prototypes required less refrigerant to preserve payloads at their necessary temperature for longer durations. Estimated improvements in refrigerant-to-payload ratios and cold life over current best-in-class equipment were 35% and 64% respectively.

The public-private partnership to develop cold chain containers with improved thermal performance, reduced operational energy consumption, and negative carbon emissions represents progress toward the goals of the 2008 Food, Conservation, and Energy Act, [5] and toward fulfilling Presidential Executive Order 13423, which requires Federal agencies to set greenhouse gas emissions reduction targets; increase energy efficiency; and leverage Federal purchasing power to promote environmentally-responsible products and technologies.

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Reengineering Transport Containers for an Improved Vaccine Cold Chain

Victoria M. Gammino (Global Immunization Division, US Centers for Disease Control and Prevention), Michael F. C. Moreland (SEEDR L3C), Olen Kew (Division of Viral Diseases, US Centers for Disease Control and Prevention), Sue Gerber (Global Immunization Division, US Centers for Disease Control and Prevention)

BACKGROUND
Temperature-sensitive vaccines must be maintained within strict temperature parameters to retain efficacy. Since 2001, the United States Government (USG) has contributed over $0.5 US billion to the Global Alliance for Vaccines and Immunization which increases access to routine, underutilized and new vaccines worldwide. Domestically, the USG invests approximately $3.7 billion annually for the Vaccines for Children program. Given this significant financial commitment, protecting vaccine during transport is of paramount importance.

OBJECTIVE
Through a unique public-private partnership, stakeholders from the private, public, non-governmental and academic sectors endeavored to engineer vaccine carriers (VCs) and cold boxes (CBs) that meet the increasingly complex needs of immunization programs. In addition to meeting the World Health Organization’s Performance, Quality and Safety standards, we sought to extend “cold-life” (time between 2-10C? at 37C? ambient) while preventing cargo freezing, reducing the equipment’s carbon-footprint, and simplifying pack-out requirements.

METHODS
Following a systematic deconstruction of existing equipment, SEEDR utilized HEEDS engineering software to optimize materials and design elements for one-liter VC and 18-liter CB prototypes. This process included three-dimensional finite element modeling for virtual, mechanical and thermal simulation and physical prototyping for validation and field- testing.

RESULTS
Engineering models predict a modeled cold-life of 65 hours for the one-liter VC, and an estimated minimum cold-life of 160 hours for the 18-liter CB which holds 6000 vaccine doses. These data demonstrate that these new designs potentiate extended cold-life without freezing, and offer the most efficient refrigerant to vaccine ratio among comparable containers.

CONCLUSIONS
Through an interdisciplinary research process, SEEDR developed a line of products that exceeds the performance of existing equipment for freeze prevention and cold-life, thereby affording greater protection for thermally-sensitive and costly vaccines during transport. This unique public-private partnership demonstrates the value of cross-sectoral collaboration to create life-saving public goods.

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Resource Allocation Problems during Disasters: The Cases of Points of Distribution Planning and Material Convergence Handling

Miguel Jaller Martelo (Rensselaer Polytechnic Institute), José Holguín-Veras (Rensselaer Polytechnic Institute)

As recent disaster experiences have shown, there is an urgent need for significant improvements in the efficiency of humanitarian logistics. These improvements are not only to avoid logistical failures ensuring an efficient and reliable flow of critical resources to the disaster area, but to guarantee a delivery process that takes into account economic (i.e. transportation, inventory, location, material convergence costs) and social considerations (i.e. human suffering). This research attempts to contribute to the improvement efforts by developing analytical formulations to analyze the resource allocation problem in the planning of distribution systems from two different perspectives. On one hand, considering the specific planning of points of distribution (PODs) in terms of their locations and resources required to expedite the flow of supplies to the population in need, while minimizing the social impacts brought about by their serving capacity. Specifically, the formulation developed provides an indication of the optimal number of PODs considering the cost associated with the walking distance to the POD and the social cost of waiting time for service. Results show the importance of considering waiting time or deprivation time social costs, usually overlooked by disaster planning formulations found in the literature.

On the other hand, the research developed strategies to optimally allocate resources to handle the material convergence problem; that is, to maximize the flow of high priority goods. The analyses performed provide insights about the negative impacts it has in humanitarian logistics: great amount o resources need to be allocated to handle this flow (composed of different types of commodities, ranging from urgent high priority to non-priority items); if no control strategy is developed, all components reach the impacted area creating logistical nightmares. With this in mind, a control/processing strategy is developed and sensitivity analyses are performed to different parameters.

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Robust Decision Making with Limited Information: An Application of Info-Gap Theory

Selina Begum (Clemson University), William Ferrell (Clemson University)

Humanitarian logisticians face challenges of unknown demand and supply due to characteristics inherent to any disaster; time, location and impact, damaged infrastructure, nature of funding, politically volatile environment of the region and the role various stakeholders play. Decision making in such a dynamic environment is a challenge; unfortunately logisticians are often ill prepared to confront this challenge. Decisions driven by reliable information and quantitative model will yield better result. This research applies information gap decision making frame work to help decision makers to conduct relief work during the immediate response phase of a disaster relief work. Building on three constructs- system model, uncertainty model and performance requirement, info gap theory focuses on quantification of information gap to predict the system behavior. Info-gap theory revolves around what is already known and what happens when these known parameters are varied, how the solution space behaves under the circumstance. Using a simple case study we show how info gap theory help make a robust decision on the face of severe uncertainty.

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S2H: Monitoring, Validating and Analyzing Homeless Shelter Occupancy

Supraja Narasimhan (Georgia Institute of Technology), Santosh S. Vempala (Georgia Institute of Technology)

The S2H website was created to support United Way of Metro Atlanta (UWMA) in its efforts to reduce chronic homelessness in the Atlanta area. “S2H” refers to UWMA’s Street to Home program, which aims to help people secure permanent housing and supportive services for the long-term, so that they are not forced to return to the street in the future.

The S2H website was launched to address UWMA’s need to monitor shelter occupancy at participating agencies, and to track the overall success of the Street to Home program. A trends and analysis module was added later to visualize patterns in historical data and make predictions for future allocation of resources.

In September 2010, UWMA noticed that the website reported a rise in the Street to Home dropout rate, from 20% to 27%, and wanted to verify its accuracy. However the S2H database showed that participants were being reported as having “Left Program” by partner agencies in cases that should have been handled otherwise:

(1) The participant had already graduated to permanent housing or another successful outcome.
(2) The participant was never shown to be “In Housing” at a partner agency, despite being shown as having “Left Program.”
(3) The person had stayed in the program for less than the five day minimum required to be considered a participant.

To address this, we modified the website interface and increased client-side validation to better reflect the real-world practices of the Street to Home program. The revised interface restricts data input options in order to make program practices unambiguous to users entering data. Additional client-side validation standardizes data entered and helps prevent inadvertent errors. As a strategy for future sustainability, we plan to continue adding validation and adjusting the interface to better emulate the work practices of the Street to Home program.

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SAFE Water Now: Scaling up access to sustainable safe water solutions

Swetha Krishnakumar (SAFE Water Now), Ashley Cleveland (SAFE Water Now), Tracy Hawkins (SAFE Water Now)

The need for long-term solutions for clean water in developing nations is well-known and documented. Many organizations and solutions exist to address the problem, so why isn’t safe water getting to more people in need? SAFE Water Now, Inc. has completed research on this question and there are a variety of factors that affect the scalability of provisioning safe water on a global scale. This poster outlines those factors and discusses what is needed to accelerate the availability and accessibility of appropriate safe water solutions in developing countries. Furthermore, the poster will introduce the SAFE Water Now organization, including details about the filtration system they currently support, and a business model that will allow the scaling up of safe water solutions to the developing world.

SAFE Water Now Organization
SAFE Water Now is a US tax exempt 501(c)3 organization headquartered in Atlanta with operations in Tanzania. SAFE Water Now, Inc. is established as a nonprofit, charitable organization with the mission of providing clean water – initially to Africa, and then to the rest of the underserved world – through sustainable enterprise and education.

SAFE Water Now Solution
SAFE Water Now currently supports the ceramic water filter, a simple, effective, and, sustainable long-term safe water solution. The device is a round-bottom ceramic pot made from a mixture of clay and colloidal silver. The filter effectively removes up to 99.99% of pathogenic bacteria and oocytes.

SAFE Water Now Model
SAFE Water Now is committed to enabling communities in developing countries to solve their own safe water problem. Using best business practices, we work with the local community to stimulate the demand for water treatment, provide an effective and affordable solution(s), and then create jobs in the local community to meet the demand.

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SLiCE Applied to Water Treatment Systems

Molly Nelson (GTRI), Emily Woods (GTRI), Joseph Goodman (GTRI), Kevin Caravati (GTRI), Laura Kovalchick (GTRI)

The selection of appropriate water treatment systems is a prevalent difficulty facing aid and relief organizations working in emergency and developing country settings. Experience has shown that there is no silver bullet technology successful in all deployment scenarios, and humanitarian agencies often have difficulty determining appropriate selection criteria for water treatment system identification. Researchers at the Georgia Tech Research Institute (GTRI) have focused on evaluating the criteria needed to discern the system most likely to succeed under varying site specific conditions. The General Electric Foundation is sponsoring GTRI in collaboration with the Center for Global Safe Water at Emory University to research the application of community and emergency water treatment systems for relief and developing setting applications.

To facilitate comprehensive analysis of all components of water treatment systems, a multi-faceted System Life Cycle Evaluation (SLiCE) tool was developed. The SLiCE procedure provides a format for evaluating the phases of a system’s life cycle most likely to impact system success. It consists of evaluation rubrics for each phase of the life cycle where each key performance is rated on a 1-5 scale. In 2010, 7 systems were installed and evaluated using the SLiCE methodology at a system evaluation field site.
To help consumers select systems most appropriate for their applications, a SLiCE system selection tool (SST) was created. The SST compiles scores of systems from the SLiCE rubrics, and combines them into a composite score weighted using prioritization input from an end user.

Not only will the SST serve as a decision tool for humanitarian relief organizations in determining which system will be most effective for their program, but also provide baseline design targets for system manufacturers. Feedback regarding system performance will also provide advanced insight for multiple audiences including academic research teams in terms of metrics of system success.

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The Spatial Distribution of Aid Recipients in Kenya

Michael Veatch (Gordon College), Matthew Forsstrom (Gordon College), Hang Yang (Gordon College)

Give Direct, a non-profit organization, recruits and delivers aid to residents of Kenya. The aid delivered is cash funds that are directly transferred to the recipients through the use of M-Pesa agents. The organization is able to operate with low overhead costs and has small transaction costs after recipients are identified. However, the recruitment of recipients produces additional costs.

This project studies where Give Direct should deliver aid. Their goals include recruiting recipients with a variety of needs and in a significant number of communities, but visiting more and dispersed locations increases the costs of recruiting recipients. A model was developed to minimize the recruitment costs while achieving the desired diversity and total number of locations visited. Three area-specific needs, malaria risk, poverty, and drought risk, where chosen as high priority. Travel is assumed to be a single trip from Nairobi and travel cost is measured as the minimum-cost circuit for a given set of locations. Costs, including room and board and hiring a local worker, are also incurred at each location.

Give Direct’s direct transfer approach has a very streamlined cost structure in which travel costs play a major role. However, balancing the costs and benefits of geographic dispersal is relevant to many aid organizations. The method presented here might be useful for other organizations if extended to more complex cost structures.

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Vehicle Routing for the Last Mile of Power System Restoration

Carleton Coffrin (Brown University), Pascal Van Hentenryck (Brown University), Russell Bent (Los Alamos National Laboratory)

This work considers last-mile disaster recovery for power restoration, that is, how to schedule and route a fleet of repair crews to restore the power network as fast as possible after a disaster. To overcome the computational difficulties raised by this joint repair and restoration problem, this work proposes a four-stage approach based on the idea of constraint injection, which decouples the power-restoration and vehicle-routing optimization problems, while still capturing the restoration aspect in the routing component. The practical benefits of this approach are demonstrated on hurricane disaster scenarios generated by Los Alamos National Laboratory using state-of-the-art disaster simulation tools and the infrastructure of the United States.

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