In 2006, the United States Congress passed the Pandemic All-Hazards Preparedness Act (PAHPA) which mandated that all emergency preparedness planning shall address at-risk populations. Further, in 2013, the reauthorization of this act, known as PAHPRA, defined at-risk individuals as "children, older adults, pregnant women, and individuals who may need additional response assistance." This vague definition leaves emergency managers, planners, and public health officials with the difficult task of understanding what it means to be at-risk. Further, once identified, the geographic location of at-risk individuals must be obtained. This research first uses the concept of social vulnerability to enhance the understanding of what it means to be "at-risk." Then, by comparing two data disaggregation techniques, areal weighted interpolation and dasymetric mapping, I demonstrate how error of estimation is affected by different scenarios of population distribution and service area overlap. The results extend an existing framework of vulnerability by stratifying factors into quantifiable and subjective types. Also, dasymetric mapping was shown to be a superior technique of data disaggregation compared to areal weighted interpolation. However, the difference in error estimates is low, 5 percent or less in 72 percent of the test cases. Only through local collaboration with community entities can emergency planners access the appropriate data to both: 1) understand the nature of at-risk individuals in their service areas and 2) spatially target resources needed to ensure all individuals are planned for in case of a bio-emergency.

The presence of naturally occurring and man-made public health threats necessitate the design and implementation of mitigation strategies, such that adequate response is provided in a timely manner. Since multiple variables, such as geographic properties, resource constraints, and government mandated time-frames must be accounted for, computational methods provide the necessary tools to develop contingency response plans while respecting underlying data and assumptions. A typical response scenario involves the placement of points of dispensing (PODs) in the affected geographic region to supply vaccines or medications to the general public. Computational tools aid in the analysis of such response plans, as well as in the strategic placement of PODs, such that feasible response scenarios can be developed. Due to the sensitivity of bio-emergency response plans, geographic information, such as POD locations, must be kept confidential. The generation of synthetic geographic regions allows for the development of emergency response plans on non-sensitive data, as well as for the study of the effects of single geographic parameters. Further, synthetic representations of geographic regions allow for results to be published and evaluated by the scientific community. This dissertation presents methodology for the analysis of bio-emergency response plans, methods for plan optimization, as well as methodology for the generation of synthetic geographic regions.

With disasters increasing in frequency and costs each year, this study seeks to explore ways greater public participation can assist emergency managers in their mission to keep communities safe. Specifically this study examines the policy process and administrative functions of emergency management to illuminated the benefits and hindrances involved in greater participation. This study conducted a qualitative analysis of governmental documents, disaster case studies, international research, as well as political science and administrative doctrines, to arrive at its conclusions. The results of this study reveal that the public is a largely untapped resource in the emergency management field. Engaging the public dialogically in early policy stages and emergency management phases is essential to successful inclusion for both administrators and communities. Specifically, public inclusion creates expanded knowledge, shared learning, personal responsibility, and increased social capital. Faced with the growing threat from disasters, emergency management can create communities that are both more resilient and sustainable by increasing public participation.

The use of wireless sensor networks for a myriad of applications is increasing. They can be used in healthcare for emergency management. In Florida, hurricanes are the main source of natural disasters. There has been a high incidence of hurricanes over the past decade. When a hurricane warning is issued it is important that people who live in potentially dangerous areas, such as along the coast, evacuate for their safety. Nursing homes and other care facilities for elderly or disabled people experience difficulty with the evacuation as their residents require additional assistance. The characteristics and challenges of a hurricane evacuation are investigated. A patient-centric hurricane evacuation management system is proposed to allow healthcare providers the ability to continuously monitor and track patients. During a hurricane there are usually scarce energy resources and a loss of basic communication services such as cellular service and Internet access. We propose the architecture of the system that allows it to operate in the absence of these services. The hardware and software architectures are also presented along with the main phases of operation. The system was then validated and the performance evaluated via simulation using the OPNET Modeler. / by Arny Isonja Ambrose. / Vita. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.