SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Variations in ED Visits During Hurricane Irma in Florida and Potential Cost Savings Using Telehealth

Background: Periods of natural disasters like hurricanes can lead to traumas, worsening of existing medical conditions, travel restrictions, or healthcare systems’ inability to provide critical and timely care. A promising approach is telehealth use to provide care to remote patients in shelters or their homes. However, there is a need to better understand evacuees’ behavior and ED use during such events. Methods: We conducted a retrospective study using 2017 archival billing data in Florida during Hurricane Irma. The NYU ED algorithm was used to classify visits into emergent and non-emergent categories. Comparison groups included counties under mandatory evacuations and those with extended power outages. Comparison timelines were defined as pre-, post-, and hurricane quarters. Results: Hurricane evacuations caused more individuals to seek emergent and non-emergent care outside of their home counties during the hurricane quarter. Extended power outages caused an increase in in-county emergent and non-emergent visits after the hurricane. Telehealth could have potentially led to over $296 M in cost savings during the hurricane quarter. Discussion: Telehealth investments can be extended to meet the needs of a disaster-affected population. The availability of a robust telehealth infrastructure, appropriate planning and resource allocation, and supporting policies and regulation can make the continuity of care possible

Emergency Management Training and Exercises for Transportation Agency Operations, MTI Report 09-17

Training and exercises are an important part of emergency management. Plans are developed based on threat assessment, but they are not useful unless staff members are trained on how to use the plan, and then practice that training. Exercises are also essential for ensuring that the plan is effective, and outcomes from exercises are used to improve the plan. Exercises have been an important part of gauging the preparedness of response organizations since Civil Defense days when full-scale exercises often included the community. Today there are various types of exercises that can be used to evaluate the preparedness of public agencies and communities: seminars, drills, tabletop exercises, functional exercises, facilitated exercises and full-scale exercises. Police and fire agencies have long used drills and full-scale exercises to evaluate the ability of staff to use equipment, protocols and plans. Transit and transportation agencies have seldom been included in these plans, and have little guidance for their participation in the exercises. A research plan was designed to determine whether urban transit systems are holding exercises, and whether they have the training and guidance documents that they need to be successful. The main research question was whether there was a need for a practical handbook to guide the development of transit system exercises

Organizational Complexity, Plan Adequacy, and Nursing Home Resiliency: A Contingency Perspective

Some social and organizational behavior scientists measure resiliency through anecdotal qualitative research, i.e. personality analyses and stories of life experience. Empirical evidence remains limited for identifying measurable indicators of resiliency. Therefore, a testable contingency model was needed to clarify resiliency factors pertinent to organizational performance. Two essential resiliency factors were: 1) a written plan and 2) affiliation with a disaster network. This contingency study demonstrated a quantifiable, correlational effect between organizational complexity, disaster plan adequacy and organizational resiliency. The unit of analysis, the skilled nursing facility proved vulnerable, therefore justifying the need for a written emergency management plan and affiliation with a disaster network. The main purpose of this research was to verify the significance of emergency management plans within a contingency framework of complexity theory, resource dependency, systems theory, and network theory. Distinct sample moments quantified causal relationships between organizational complexity (A), plan adequacy (B) and resiliency (C). Primary and secondary research data were collected from within the context of public health and emergency management sectors within the State of Florida

“You Came to Not Normal Land”: Nurses\u27 Experience of the Environment of Disaster: A Phenomenological Investigation

Previous research suggests US nurses are unprepared for disaster, and suffer from adverse psychosocial outcomes following their disaster response. Current disaster preparedness focuses on providing hospital-centric trauma and acute care in fully resourced Western conditions, and does not include the environmental realities of the disaster setting. This study utilized an existential phenomenological approach to explore the meaning of the nurse’s experience of the disaster environment. Eleven nurses with broad disaster expertise and training levels participated in this research. The essence of their disaster experiences can be summed up by the central theme of “You came to not normal land.” Four global themes that describe this “not normal land” were “All the resources was gone”; “You prepare, you prepare, and you are unprepared”; “It can be done; it’s just different”; and “Stuff that sticks with you.” The environment of disaster was both “not normal” and challenging owing to the many simultaneous breakdowns in healthcare supportive systems. Nurses were surprised and unprepared for the environmental conditions surrounding them. Reductions in systems (i.e. water, power), structures, staff, and supplies were coupled with lack of familiarity with alternative care sites, unaccustomed patient populations, the prevailing need for public health and fundamental nursing, and the isolated nature of disaster environments. Policies and regulations that “normally” guide nurses’ actions were disregarded in the immediacy of providing care when the usual social framework no longer existed. Nurses continue to relive the disaster setting’s sights, sounds, smells, and stories of the people they encountered. A strong sense of pride, duty, and willingness to respond again prevailed in these nurses. Nurses can be prepared for the likely conditions of reduced resources and damaged infrastructure following disaster by including the contextual setting of disaster nursing in disaster education, practice, training, and policy. Suggestions for further research include determining the relevance of current disaster training to the nurses’ actual disaster experience; determining what non-clinical knowledge or skills or training disaster nurses think would be useful; and identifying and measuring the contribution of environmental factors to disaster nurses’ stress

THE INTERNET OF THINGS (IOT) IN DISASTER RESPONSE

Disaster management is a complex practice that relies on access to and the usability of critical information to develop strategies for effective decision-making. The emergence of wearable internet of things (IoT) technology has attracted the interests of several major industries, making it one of the fastest-growing technologies to date. This thesis asks, How can disaster management incorporate wearable IoT technology in operations and decision-making practices in disaster response? How IoT is applied in other prominent industries, including construction, manufacturing and distribution, the Department of Defense, and public safety, provides a basis for furthering its application to challenges affecting agency coordination. The critical needs of disaster intelligence in the context of hurricanes, structural collapses, and wildfires are scrutinized to identify gaps that wearable technology could address in terms of information-sharing in multi-agency coordination and the decision-making practices that routinely occur in disaster response. Last, the specifics of wearable technology from the perspective of the private consumer and commercial industry illustrate its potential to improve disaster response but also acknowledge certain limitations including technical capabilities and information privacy and security.Civilian, Virginia Beach Fire Department / FEMA - USAR VATF-2Approved for public release. Distribution is unlimited

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Disaster Decision Making: Hurricanes Katrina and Gustav in New Orleans

The goal of this project was to set standard criteria for evaluating the execution of a disaster response at the local, State, and Federal Government levels. This evaluation, which was based on the disaster responses to Hurricanes Katrina and Gustav, focused on improvements in decision making. This project\u27s procedure consisted of developing a model of decisions made and analyzing them. It was determined that there is a need for competent leadership, conducting rehearsals, and more initiative at all government levels

The Impact of Federal Emergency Management Legislation on At-Risk and Vulnerable Populations for Disaster Preparedness and Response

It is well documented that in the aftermath of a natural or human caused disaster, certain at-risk and vulnerable populations suffer significantly more than do other population groups. As a result, Congress enacted the Post-Katrina Emergency Management Reform Act (PKEMRA) in part to address deficiencies in providing aid to vulnerable populations, though little is known if the PKEMRA has resulted as it was intended. The purpose of this phenomenological study was to assess the impact of the PKEMRA on addressing emergency preparedness deficits related to at-risk and vulnerable populations. The theoretical framework followed Howard\u27s conceptualization of game and drama theory. The research questions focused on the extent to which the PKEMRA recommendations improved disaster lifecycle outcomes for at-risk and vulnerable groups in Orleans Parish, LA between Hurricanes Katrina in 2005 and Isaac in 2012. Data were collected through semi-structured interviews of 5 emergency managers with knowledge and experience local to Orleans Parish, LA. Interview data were systematically reviewed using inductive coding and categorized for thematic analysis. Key study findings indicated that the improvements made to family location registries, evacuation procedures, and disaster resources for these populations in Orleans Parish were not a result of the PKEMRA, but of the state and local emergency agencies without input from the federal government. This study contributes to social change by promoting greater transparency of federal programs targeting at-risk and vulnerable populations, making direct recommendations to use Orleans Parish as a relevant example to address the needs of these populations. Such a review will serve as an exportable model for similar communities across the country

Improving resilience in Critical Infrastructures through learning from past events

Modern societies are increasingly dependent on the proper functioning of Critical Infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient, where resilience denotes the capacity of a system to recover from challenges or disruptive events, becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exists in the context of CIs’ resilience research. This dissertation starts with a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The review identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organizational, community, and urban) and discusses the related qualitative or semi-quantitative methods. The scope of the thesis emphasizes the organizational dimension, as a socio-technical construct. Accordingly, the following research question has been posed: how can learning improve resilience in an organization? Firstly, the benefits of learning in a particular CI, i.e. the supply chain in reverse logistics related to the small arms utilized by Italian Armed Forces, have been studied. Following the theory of Learning From Incidents, the theoretical model helped to elaborate a centralized information management system for the Supply Chain Management of small arms within a Business Intelligence (BI) framework, which can be the basis for an effective decision-making process, capable of increasing the systemic resilience of the supply chain itself. Secondly, the research question has been extended to another extremely topical context, i.e. the Emergency Management (EM), exploring the crisis induced learning where single-loop and double-loop learning cycles can be established regarding the behavioral perspective. Specifically, the former refers to the correction of practices within organizational plans without changing core beliefs and fundamental rules of the organization, while the latter aims at resolving incompatible organizational behavior by restructuring the norms themselves together with the associated practices or assumptions. Consequently, with the aim of ensuring high EM systems resilience, and effective single-loop and double-loop crisis induced learning at organizational level, the study examined learning opportunities that emerge through the exploration of adaptive practices necessary to face the complexity of a socio-technical work domain as the EM of Covid-19 outbreaks on Oil & Gas platforms. Both qualitative and quantitative approaches have been adopted to analyze the resilience of this specific socio-technical system. On this consciousness, with the intention to explore systems theoretic possibilities to model the EM system, the Functional Resonance Analysis Method (FRAM) has been proposed as a qualitative method for developing a systematic understanding of adaptive practices, modelling planning and resilient behaviors and ultimately supporting crisis induced learning. After the FRAM analysis, the same EM system has also been studied adopting a Bayesian Network (BN) to quantify resilience potentials of an EM procedure resulting from the adaptive practices and lessons learned by an EM organization. While the study of CIs is still an open and challenging topic, this dissertation provides methodologies and running examples on how systemic approaches may support data-driven learning to ultimately improve organizational resilience. These results, possibly extended with future research drivers, are expected to support decision-makers in their tactical and operational endeavors