Jendreck, M., Hellriegel, J., Allmann, J., Restel, H., Pfennigschmidt, S., Meissen, U., et al. (2023). ROBUST communication platform – A decentralized, distributed communi cation platform for the earthquake early warning system ROBUST. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 822–836). Omaha, USA: University of Nebraska at Omaha.
Abstract: Strong earthquakes of great intensity pose a severe threat to human life and property. Earthquake early warning systems are designed to give people in endangered areas valuable seconds to save their lives and property. The basis of an efficient warning system is a communication infrastructure that provides high-speed and reliable communication between the components of the warning system. This paper presents the distributed, decentralized communication platform for the ROBUST project. It discusses the key challenges and requirements such as resilience, real-time capability and target group-specific information distribution that are placed on such a communication platform. In addition, it presents the conception of the communication platform, which is based on a subscriber procedure between autonomous, decentralized peers (nodes), in order to be able to realize the requirements. Finally, it details the technical implementation, practical realization, and evaluation of the communication platform.
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Sterl, S., Almalla, N., & Gerhold, L. (2023). Conceptualizing a Pandemic Early Warning System Using Various Data: An Integrative Approach. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 284–294). Omaha, USA: University of Nebraska at Omaha.
Abstract: Covid-19 demonstrated the vulnerability of various systems and showed, however, that digital tools and data can serve not only to stop infections but also to detect viruses before or immediately after a zoonosis has occurred, thus preventing a potential pandemic. Although several pandemic early warning systems (P-EWS) and German pandemic-related projects (G-PRP) exist, they often use a limited data range or rely on third-party data. Here, we present a concept of an integrative pandemic early warning system (IS-PAN) applied to Germany using various data such as health data (e.g., clinical/syndromic) or internet data (e.g., social media/apps). Based on a systematic literature research of P-EWS and G-PRP on scientific and public health platforms, we derived indicators that help to detect virus threats with a system consisting of modules monitored in parallel. By integrating various pre collected digital data, this approach can help to identify a potential health threat efficiently and effectively.
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Stella van Esch, Marc van den Homberg, & Kees Boersma. (2021). Looking Beyond the Data: an Assessment of the Emerging Data Ecosystem of Nepal's Flood Early Warning Systems. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 282–293). Blacksburg, VA (USA): Virginia Tech.
Abstract: Increasingly, data-driven instruments are used in disaster risk reduction to foster more efficient, effective, and evidence-based decision-making. This data revolution brings along opportunities and challenges, which are sometimes related to the data itself, but more often seem related to the environment in which the data is put to use. To provide insight into such an emerging data ecosystem, this paper uses a qualitative case study to assess the use of data in flood early warning systems (EWS) in Nepal. In response to the research question 'How does the data ecosystem impact the opportunities and challenges regarding data use in flood early warning systems in Nepal?', this paper discusses the importance of considering the broader context instead of regarding data as an entity unto itself. It shows how actors, policies and other contextual factors impact the effectiveness of data use by either presenting opportunities, like the establishment of a national disaster data repository, or challenges, like inadequate human resources for working with data.
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Andrew Marinik, Ludwig Gantner, Scott Fritz, & Sean Smith. (2020). Developing Performance Metrics of an Emergency Notification System. 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. 663–668). Blacksburg, VA (USA): Virginia Tech.
Abstract: The use of emergency notification systems (ENS), or early warning systems, are not only common practice among Institutes of Higher Education (IHEs), but are required by law in the United States. The dramatic increase in use is matched by the increase in community expectation. This community expectation corresponding with societal shifts challenges Public Safety leaders to implement and maintain a broad and highly reliable ENS. Most Public Safety programs lack the internal resources to consistently assess system risk, reliability, and messaging validity of their ENS sufficient to match the required system performance. Virginia Tech Emergency Management is proposing an ENS evaluation system capable of supporting assessment of reliability and risk across the entire system through the lens of Socio-Technical Systems (STS) theory at a practitioner level. By organizing emergency notification/early warning systems through Human Subsystems, Technical Subsystems, and Task Design the practitioner can assess their system by performance and risk.
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Anastasios Karakostas, Stefanos Vrochidis, Yiannis Kompatsiaris, Boris Kantsepolsky, Jürgen Moßgraber, Stamatia Dasiopoulou, et al. (2018). beAWARE: Enhancing Decision Support and Management Services in Extreme Weather Climate Events. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1136–1139). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. The main goal of beAWARE framework is to provide support in all the phases of an emergency incident. More specifically, we propose an integrated solution to support forecasting, early warnings, transmission and routing of the emergency data, aggregated analysis of multimodal data and management the coordination between the first responders and the authorities.
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