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Robert T. Brigantic, David S. Ebert, Courtney D. Corley, Ross Maciejewski, George A. Muller, & Aimee E. Taylor. (2010). Development of a quick look pandemic influenza modeling and visualization tool. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Federal, State, and local decision makers and public health officials must prepare and exercise complex plans to contend with a variety of possible mass casualty events, such as pandemic influenza. Through the provision of quick look tools (QLTs) focused on mass casualty events, such planning can be done with higher accuracy and more realism through the combination of interactive simulation and visualization in these tools. If an event happens, the QLTs can then be employed to rapidly assess and execute alternative mitigation strategies, and thereby minimize casualties. This can be achieved by conducting numerous “what-if” assessments prior to any event in order to assess potential health impacts (e.g., number of sick individuals), required community resources (e.g., vaccinations and hospital beds), and optimal mitigative decision strategies (e.g., school closures) during the course of a pandemic. In this presentation, we overview and demonstrate a pandemic influenza QLT, discuss some of the modeling methods and construct and visual analytic components and interface, and outline additional development concepts. These include the incorporation of a user selectable infectious disease palette, simultaneous visualization of decision alternatives, additional resource elements associated with emergency response (e.g., first responders and medical professionals), and provisions for other potential disaster events.
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Elisa Canzani, & Ulrike Lechner. (2015). Insights from Modeling Epidemics of Infectious Diseases ? A Literature Review. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: The relevance of modeling epidemics? spread goes beyond the academic. The mathematical understanding of infectious diseases has become an important tool in policy making. Our research interest is modeling of dynamics in crisis situations. This paper explores the extant body of literature of mathematical models in epidemiology, with particular emphasis on theories and methodologies used beyond them. Our goal is to identify core building blocks of models and research patterns to model the dynamics of crisis situations such as epidemics. The wide range of applications of epidemic models to many other disciplines that show biological analogies, makes this paper helpful for many modelers and mathematicians within the broader field of Crisis Management.
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Edward J. Glantz. (2014). Community crisis management lessons from Philadelphia's 1793 epidemic. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 556–564). University Park, PA: The Pennsylvania State University.
Abstract: Public health organizations, including the Centers for Disease Control and Prevention, the World Health Organization, and the U.S. Department of Health and Human Services, are greatly concerned that a new influenza type A outbreak will result in a rapid spread of infectious disease, overwhelming existing medical response infrastructures. Each of these organizations has published planning guides that call upon local and community organizers to begin planning for such an event. To establish insight and provide context for these organizers, this paper presents a case analysis of the Philadelphia yellow fever outbreak of 1793.
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Olga Vybornova, Pierre-Alain Fonteyne, & Jean-Luc Gala. (2015). Ontology-Based Knowledge Representation and Information Management in a Biological Light Fieldable Laboratory. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: A comprehensive ontology has been developed to model the operational domain knowledge and provide information management for a light fieldable laboratory (LFL) performing molecular microbiological analyses. LFL is considered as a toolbox where all operational functions and tools used to execute these functions are incorporated into a single system. The ontology is used to facilitate the LFL mission preparation and management, to provide technical compatibility of sharable information between tools, and to align the terminology and definitions between tools while complying with standards, best practices and procedures. The LFL domain is a formalised and structured modelling the LFL concepts, procedures, functions, prescribing the necessary functions and delimiting those which are incompatible with the given mission or scenario. Such consistent logical modelling allows to efficiently plan and configure the LFL mission selecting only the necessary functions and tools from the whole collection and to activate them appropriately in due time.
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