Huse, L., Schwedhelm, M., & Steinecker, H. (2023). Improving Visibility for Proactive Tactics in Emerging Situations. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1078–1079). Omaha, USA: University of Nebraska at Omaha.
Abstract: Whether it’s an infectious disease, a natural disaster, a human-made disaster, or a loss in utilities and resources, state and local leaders need visibility into the real-time resources of the entire healthcare continuum from labs, hospitals, long-term care settings, and shelters. By connecting public health and healthcare systems, information, and resources, leaders can be more agile and predictive in where to deploy limited resources before and during an emerging situation. The panelists will discuss how technology and data analytics can be utilized in real-time to resource decisions, bi-directional communication, transparency to stakeholders, and policy development. They will also explore the public health and healthcare continuum for mutual strategy, predictive modeling and reduction of excess loss of life. The panel will consist of a short introduction by each panelist followed by a facilitated discussion, and questions from the audience.
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Lennart Landsberg, David Ganske, Christopher Munschauer, & Ompe Aimé Mudimu. (2020). Using Existing Data to Support Operational Emergency Response in Germany – Current Use Cases, Opportunities and Challenges. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 406–415). Blacksburg, VA (USA): Virginia Tech.
Abstract: The availability of resources in the fire and ambulance services in Germany is facing a radical change. Demographic and social transition is reducing the availability of volunteer personnel, and increasing traffic congestion in cities is resulting in longer travel times for emergency vehicles. This paper presents the findings of the definition phase of a research project that addresses these changes. It shows the basic idea of how resilience of fire and ambulance services can be improved by analyzing operational data from past incidents using artificial intelligence (AI). The primary objective is the development of a decision support system for control center dispatchers, which ensures optimal use of available resources. As the result of the definition phase, this paper gives an overview of existing data, current as well as future use cases and also highlights risks and challenges that have to be considered.
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Derya Ipek Eroglu, Duygu Pamukcu, Laura Szczyrba, & Yang Zhang. (2020). Analyzing and Contextualizing Social Vulnerability to Natural Disasters in Puerto Rico. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 389–395). Blacksburg, VA (USA): Virginia Tech.
Abstract: As the third hurricane the U.S. experienced in 2017, Hurricane María generated impacts that resulted in both short term and long term suffering in Puerto Rico. In this study, we aim to quantify the vulnerability of Puerto Ricans by taking region and society specific characteristics of the island into account. To do this, we follow Cutter et al.'s social vulnerability calculation, which is an inductive approach that aims to represent a society based on its characteristics. We adapted the Social Vulnerability Index (SoVI) for Puerto Rico by using data obtained from the U.S. Census Bureau. We analyzed the newly calculated SoVI for Puerto Rico and compared it with the existing deductive approach developed by the Center for Disease Control (CDC). Our findings show that the new index is able to capture some characteristics that the existing vulnerability index is unable to do.
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Yossi Nygate, William Johnson, Mark Indelicato, Miguel Bazdresch, & Clark Hochgraf. (2018). Intelligent Wireless Infrastructure Management for Emergency Communications. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1156–1160). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: This poster describes the research of a collaborative faculty-led research that will enable first responders to identify and visualize geo-located quality of service and coverage gaps in wireless and deployable networks during an emergency event and support the deployment additional LTE base stations within FirstNet to augment network coverage and capacity. Our crowd sourced cellular metrics system uses big data analytics to detect changes in coverage and usage patterns and recommends where to deploy additional communication assets. The approach uses machine learning methods to measure and model coverage gaps and automatically implement bandwidth prioritization on whatever communication assets are available.
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