Jose J. Gonzalez, Colin Eden, Eirik Abildsnes, Martin Hauge, Monica Trentin, Luca Ragazzoni, et al. (2021). Elicitation, analysis and mitigation of systemic pandemic risks. 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. 581–596). Blacksburg, VA (USA): Virginia Tech.
Abstract: The Covid-19 pandemic has disrupted the health care system and affected all sectors of society, including critical infrastructures. In turn, the impact on society's infrastructures has impacted back on the health care sector. These interactions have created a system of associated risks and outcomes, where the outcomes of risks are risks themselves and where the resulting consequences are complex vicious cycles. Traditional risks assessment methods cannot cope with interdependent risks. This paper describes a novel risk systemicity approach to elicit and mitigate the systemic risks of a major pandemic. The approach employed the internet-based software strategyfinder[TM] in workshops to elicit relevant risk information from sixteen appropriately selected experts from the health care sector and major sectors impacted by and impacting back on the health care sector. The risk information was processed with powerful analytical tools of strategyfinder to allow the experts to prioritise portfolios of strategies attacking the vicious cycles.
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Ke Wang, Yongsheng Yang, Genserik Reniers, Jian Li, & Quanyi Huang. (2021). An Attribute-based Model to Retrieve Storm Surge Disaster Cases. 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. 567–580). Blacksburg, VA (USA): Virginia Tech.
Abstract: In China, storm surge disasters cause severe damages in coastal regions. One of the most important tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides useful information for the government to make real-time response plans.
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Maki Tagashira, & Toshihiro Osaragi. (2021). Accessibility Assessment of Vulnerable Roadside Areas after a Major Earthquake. 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. 553–566). Blacksburg, VA (USA): Virginia Tech.
Abstract: In order to reduce human casualty after a large earthquake, it is vital to secure the traffic function of main roads. Local governments promote the seismic reinforcement of roadside buildings, however, the project is not going well as planned. There is a high demand for appropriate information of its effect. In this paper, we proposed a method to identify the roadside areas with vulnerable accessibility to disaster bases after a large earthquake. First, we defined the accessibility indices; Link Isolation ratio (LI ratio) and Network Isolation ratio (NI ratio). Then, using the simulation model, we evaluated the accessibility to disaster base hospitals using emergency transportation roads in the Tokyo Metropolitan Area. LI ratio tended to be low in areas with a sparse road network. Furthermore, some hospitals indicated a severely high NI ratio. In secondary medical areas with these hospitals, it is necessary to consider the measures to improve accessibility.
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Miguel Ramirez de la Huerga, Victor A. Bañuls, Pilar Ortiz Calderon, & Rocio Ortiz Calderon. (2020). A Delphi-Based Approach for Analysing the Resilience Level of Local Goverments in a Regional Context. 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. 602–611). Blacksburg, VA (USA): Virginia Tech.
Abstract: This article shows the research process carried out by Regional Government of southern Europe, with more than 8 million citizens, to create an Information System to serve as a diagnostic and certification model for the resilience level of the municipalities of that region. This Information System will allow the local authorities of the regional governments to know in what situation they are and what they should do to improve their resilience level. The research framework is based on the best practices in urban resilience. One of the relevant characteristics of the work is the integration of the knowledge of a very heterogeneous group of experts for the identification of the special needs of the target region that has been articulated through a Delphi process.
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Ana-Gabriela Núñez, Sebastián Cedillo, Andrés Alvarado Martínez, & Ma Carmen Penadés. (2020). Towards the Building of a Resilient City able to Face Flood Risk Scenarios. 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. 593–601). Blacksburg, VA (USA): Virginia Tech.
Abstract: Despite the efforts that have been made to inform the community about the possible environmental risks, there is still a general lack of information. Currently, we are working on a flood risk scenario focused on a proposal towards a resilient culture together with the support of Information Technologies (IT) as a way to manage information. The goal is twofold: (i) on the one hand, to manage data in a small scenario to analyze and process the data collected from sensors in different sites in a micro-basin. Data get from data processing such as flow and velocity will then be the input data for hydraulic models to predict floods downstream; (ii) on the other hand, to publicize the predictions and the data already processed means people can benefit from information on flood risks, and the different participants may change their perception and consider cooperating in improving resilience.
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