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Journal Article Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details
Cited 16 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Jang Won Bae, Kyohong Shin, Hyun-Rok Lee, Hyun Jin Lee, Taesik Lee, Chu Hyun Kim, Won-Chul Cha, Gi Woon Kim, Il-Chul Moon
Issue Date
2018-09
Citation
IEEE Transactions on Systems, Man and Cybernetics : Systems, v.48, no.9, pp.1454-1469
ISSN
2168-2216
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/TSMC.2017.2671340
Abstract
Many disasters have occurred around the world and have caused sizable damage. A disaster, called a mass casualty incident (MCI), generates a large number of casualties that overwhelm the capacity of local medical resources, and the disaster responses to the MCI requires many interactions among the disaster responders. To evaluate the efficiency of the disaster responses against MCIs, this paper proposes an agent-based model describing the cooperations among the responders during the overall process in the disaster responses from transporting patients to their definitive care. In particular, the proposed model includes geospatial details, such as the road network and the location of hospitals around the disaster scene, and medical information, such as the distribution of medical resources and transporting units, in the region of interest to discover the key factors of the disaster response system that customized to the target region. The case study in this paper presents that the proposed approach was applied to describe a disaster response system and illustrates how the additional details are utilized to analyze the disaster response system. We expect that the proposed method can provide comprehensive insights to a disaster response system of interest, and it can be used as groundwork for improving the disaster response system.
KSP Keywords
Case studies, Disaster Response, Key factors, Mass casualty incident, Number of casualties, Overall process, Proposed model, Region of interest(ROI), Road Network, agent-based model(ABM), medical information