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	Author	Erik Borglund; Jonas Hansson
	Title	Tactical Police Interventions: Design Challenges for Situational Awareness			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1037-1047
	Keywords	Police; Police tactics; Situational Awareness; Tactical Intervention
	Abstract	Police officers’ situational awareness during tactical intervention can be crucial for how they act and whether they use the correct level of force in extreme situations. This paper presents preliminary findings in ongoing research focusing on police tactical interventions and situational awareness. Twenty-one police officers were interviewed, and a video sequence of a shorter car chase was used to set the scene in the interviews. The interviewed police officers described their tactical decisions applying the standardized tactical approach applied in the Swedish police. In the analysis, a focus on how situational awareness is gained and how situational awareness is affected by tactical decisions is presented. The study indicates that the situational awareness process begins before the actual intervention (pre-intervention phase). During the actual intervention, situational awareness is very complex. Technology supporting police officers’ cognition, as well as management and control of one or many risk areas, is identified.
	Address	Mid Sweden University & Umeå University, Police education; Umeå University, Police education
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Open Track	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2469
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	Author	Amanda Hughes; Keri Stephens; Steve Peterson; Hemant Purohit; Anastazja G. Harris; Yasas Senarath; S. Ashley Jarvis; Carolyn E. Montagnolo; Karim Nader
	Title	Human-AI Teaming for COVID-19 Response: A Practice & Research Collaboration Case Study			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1048-1057
	Keywords	Research; practice; crisis informatics; digital volunteers
	Abstract	Practice and research collaborations in the disaster domain have the potential to improve emergency management practices while also advancing disaster science theory. However, they also pose challenges as practitioners and researchers each have their own culture, history, values, incentives, and processes that do not always facilitate collaboration. In this paper, we reflect on a 6-month practice and research collaboration, where researchers and practitioners worked together to craft a social media monitoring system for emergency managers in response to the COVID-19 pandemic. The challenges we encountered in this project fall into two broad categories, job-related and timescale challenges. Using prior research on team science as a guide, we discuss several challenges we encountered in these two categories and show how our team sought to overcome them. We conclude with a set of best practices for improving practice and research collaborations.
	Address	Brigham Young University; University of Texas – Austin; National Institutes of Health; George Mason University
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Open Track	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2470
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	Author	Ana María Cintora; Eva Teresa Robledo; Cristina Gomez; Raquel Lafuente; Ricardo García; Cristina Horrillo
	Title	Analysis of the Chemical Incidents from Seveso Directive according to Direct Fatalities and Injuries			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1058-1067
	Keywords	Major accident hazards; Seveso; chemical accidents; loss data; emergency preparedness
	Abstract	This paper provides a descriptive analysis of the eMARS database, which contains compulsory information on major chemical incidents under the SEVESO Directive. This analysis serves to assess the installations with the highest number of direct fatalities and injuries. At present, the data collected to assess the status of chemical accident risk globally are rather limited. There are some sources of data on chemical accidents in government and industry that might be used to estimate the frequency and severity of some types of events, but they are far from providing a complete perspective that covers all chemical accidents, thus limiting the possibilities of obtaining a more homogeneous picture of the risk of chemical accidents worldwide. Waste storage, treatment and disposal is one of the industrial areas with the highest number of fatalities and injuries, so we must emphasize the importance of this type of industry within the risk maps.
	Address	Prehospital Emergencies Medical Service Madrid Region (SUMMA112) Madrid, Spain; Prehospital Emergencies Medical Service Madrid Region (SUMMA112) Madrid, Spain; Prehospital Emergencies Medical Service Madrid Region (SUMMA112) Madrid, Spain; Prehospital Eme
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Technologies for First Responders	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2471
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	Author	Zijun Long; Richard McCreadie
	Title	Is Multi-Modal Data Key for Crisis Content Categorization on Social Media?			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1068-1080
	Keywords	Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning
	Abstract	The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail.
	Address	University of Glasgow; University of Glasgow
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2472
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	Author	Rob Grace; Hossein Baharmand
	Title	19th ISCRAM Conference Proceedings			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1-1080
	Keywords
	Abstract	The theme of ISCRAM 2022 was The Future Vision of Large-scale Crisis Management in a Post-COVID World. These proceedings highlight the range of interdisciplinary research required to understand the design, behavior, and performance of crisis and emergency management systems. We are pleased to present the included papers, which offer excellent contributions on a wide range of topics related to the use of information systems in crisis response and management.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	2411-3387	Medium
	Track	Proceedings	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2473
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	Author	Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang
	Title	A Question Answering System Applied to Disasters			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	2-16
	Keywords	Emergency Management, Disaster, Natural Language Processing, Deep Learning
	Abstract	In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph.
	Address	Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	gxj19@mails.tsinghua.edu.cn			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2308
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	Author	Nada Matta; Thomas Godard; Guillaume Delatour; Ludovic Blay; Franck Pouzet; Audrey Senator
	Title	Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	17-27
	Keywords	Social Media analysis, TextMining, sentiment analysis, crisis management, decision making
	Abstract	Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data.
	Address	University of Technology of Troyes; University of Technology of Troyes; University of Technology of Troyes; VISOV; CS Group; ENSOSP
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	nada.matta@utt.fr			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2309
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	Author	Audun Stolpe; Jo Hannay
	Title	On the Adaptive Delegation and Sequencing of Actions			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	28-39
	Keywords	Decision support, AI Planning, Delegation, Sequencing, Adaptivity, Cognitive processes
	Abstract	Information systems support to crisis response and management relies crucially on presenting actionable information in a manner that supports cognitive processes, and does not overwhelm them. We outline how AI Planning can be used viably to support the \emph{delegation and sequencing} of tasks. The idea is to use standard operating procedures as initial specifications of plans in terms of actors, actions and delegation rules. When expressed in the AI planning language \textit{Answer set Programming} (ASP), machine reasoning can be used in a \textit{pre-incident review} to display relevant delegation and sequencing inherent in a plan. % together with measures of goal achievement. The purpose of this is to uncover weaknesses in the initial plan and to optimize sequencing and delegation to increase the likelihood of achieving goals. Further, adaptive planning can be supported in \textit{during-incident reviews} by updating the current status, upon which ASP will then compute new alternatives. % and corresponding goal achievement measures. At this point, initial goals may no longer be viable and the explicit suggestion of prior sub-optimal goals now worth pursuing can be a game-changer under stress. The conceptual basis we lay out in terms of delegation and sequencing can be readily extended with further planning factors, such as resource requirements, role transfer and goal achievement.
	Address	Norwegian Computing Center; Norwegian Computing Center
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	audun.stolpe@its.uio.no			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2310
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	Author	Nilani Algiriyage; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston; Minura Punchihewa; Santhoopa Jayawardhana
	Title	Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	40-48
	Keywords	Computer Vision, Traffic Flow, YOLOv4, CCTV Big Data
	Abstract	Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions.
	Address	Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington; University of Kelaniya; Univerity of Kelaniya
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	rangika.nilani@gmail.com			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2311
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	Author	Kenneth Johnson; Javier Cámara; Roopak Sinha; Samaneh Madanian; Dave Parry
	Title	Towards Self-Adaptive Disaster Management Systems			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	49-61
	Keywords	disaster management, self-adaptive systems, formal verification, probabilistic model checking, constraint solving
	Abstract	Disasters often occur without warning and despite extensive preparation, disaster managers must take action to respond to changes critical resource allocations to support existing health-care facilities and emergency triages. A key challenge is to devise sound and verifiable resourcing plans within an evolving disaster scenario. Our main contribution is the development of a conceptual self-adaptive system featuring a monitor-analyse-plan-execute (MAPE) feedback loop to continually adapt resourcing within the disaster-affected region in response to changing usage and requirements. We illustrate the system's use on a case study based on Auckland city (New Zealand). Uncertainty arising from partial knowledge of infrastructure conditions and outcomes of human participant's actions are modelled and automatically analysed using formal verification techniques. The analysis inform plans for routing resources to where they are needed in the region. Our approach is shown to readily support multiple model and verification techniques applicable to a range of disaster scenarios.
	Address	Auckland University of Technology; University of York; Auckland University of Technology; AUT university; Auckland University of Technology
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	kenneth.johnson@aut.ac.nz			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2312
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	Author	Tina Mioch; Reinier Sterkenburg; Tatjana Beuker; Mark A. Neerincx
	Title	Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	62-70
	Keywords	Situation Awareness, Actionability, Decision support, Chemical hazard
	Abstract	Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability.
	Address	Tno; Tno; Tno; Tno
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	tina.mioch@tno.nl			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2313
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	Author	Oussema Ben Amara; Daouda Kamissoko; Frédérick Benaben; Ygal Fijalkow
	Title	Hardware architecture for the evaluation of BCP robustness indicators through massive data collection and interpretation			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	71-78
	Keywords	Business Continuity Plan, Social sciences, Risk Management, Robustness, Embedded Hardware
	Abstract	Recently, the concept of robustness measurement has become clearly important especially with the rise of risky events such as natural disasters and mortal pandemics. In this context, this paper proposes an overview of a hardware architecture for massive data collection in the aim of evaluating robustness indicators. This paper essentially addresses the theoretical and general problems that the scientific research is seeking to address in this area, offers a literature review of what already exists and, based on preliminary diagnosis of what the literature has, presents a new approach and some of the targeted findings with a focus on the leading aspects, having a primary objective of explaining the multiple aspects of this research work.
	Address	IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; INU Champollion, University of Toulouse
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	oussema.ben_amara@mines-albi.fr			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2314
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	Author	Antonio De Nicola; Maria Luisa Villani; Francesco Costantino; Andrea Falegnami; Riccardo Patriarca
	Title	Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	79-85
	Keywords	distributed situational awareness, knowledge fusion, WAx framework, crisis management, cyber-socio-technical systems
	Abstract	Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios.
	Address	ENEA; ENEA – CR Casaccia; Sapienza University of Rome; Sapienza University of Rome; Sapienza University of Rome
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	antonio.denicola@enea.it			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2315
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	Author	Rouba Iskandar; Julie Dugdale; Elise Beck; Cécile Cornou
	Title	PEERS: An integrated agent-based framework for simulating pedestrians' earthquake evacuation			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	86-96
	Keywords	Seismic risk, human behavior, interdisciplinarity, evacuation, agent-based model
	Abstract	Traditional seismic risk assessment approaches focus on assessing the damages to the urban fabric and the resultant socio-economic consequences, without adequately incorporating the social component of risk. However, the human behavior is essential for anticipating the impacts of an earthquake, and should be included in quantitative risk assessment studies. This paper proposes an interdisciplinary agent-based modeling framework for simulating pedestrians' evacuation in an urban environment during and in the immediate aftermath of an earthquake. The model is applied to Beirut, Lebanon and integrates geo-spatial, socio-demographic, and quantitative behavioral data corresponding to the study area. Several scenarios are proposed to be explored using this model in order to identify the influence of relevant model parameters. These experiments could contribute to the development of improved of emergency management plans and prevention strategies.
	Address	Université Grenoble Alpes, ISTerre, Pacte, LIG; Université Grenoble Alpes, LIG; Université Grenoble Alpes, Pacte; Université Grenoble Alpes, ISTerre
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	rouba.iskandar@univ-grenoble-alpes.fr			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2316
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	Author	Yasas Senarath; Jennifer Chan; Hemant Purohit; Ozlem Uzuner
	Title	Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	97-105
	Keywords	Public Health, Disaster Informatics, Health Informatics, UMLS, Metathesaurus
	Abstract	During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources.
	Address	George Mason University; Northwestern University; George Mason University; George Mason University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	ywijesu@gmu.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2317
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	Author	Dashley Rouwendal van Schijndel; Audun Stolpe; Jo Erskine Hannay
	Title	Toward an AI-based external scenario event controller for crisis response simulations			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	106-117
	Keywords	Scenario event controller, AI Planning, Modelling and Simulation, Simulation controller
	Abstract	There is a need for tool support for structured planning, execution and analysis of simulation-based training for crisisresponse and management. As a central component of an architecture for such tool support, we outline the design ofan AI-based scenario event controller. The event controller is a component that uses machine reasoning to computethe next state in a scenario, given the actions performed in the corresponding simulation (execution of the scenario).Scenarios are specified in Answer Set Programming, which is a logic programming language we use for automatedplanning of training scenarios. A plan encoding in ASP adds expressivity in scenario specification and enablesmachine reasoning. For exercise managers this gives AI-based tool support for before-action and during-actionreviews to optimize learning. In line with Modelling and Simulation as as Service, our approach externalizes eventcontrol from any particular simulation platform. The scenario, and its unfolding in terms of events, is externalizedas a service. This increases interoperability and enables scenarios to be designed and modified readily and rapidlyto adapt to new training requirements.
	Address	University of Oslo; Norsk Regnesentral; Norsk Regnesentral
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	d.k.rouwendal@its.uio.no			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2318
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	Author	Julian Zobel; Patrick Lieser; Tobias Meuser; Lars Baumgärtner; Mira Mezini; Ralf Steinmetz
	Title	Modeling Civilian Mobility in Large-Scale Disasters			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	119-132
	Keywords	Civilian disaster communication, Delay-Tolerant Networks, Human mobility, Mobility models, Disaster response, Simulation
	Abstract	When disasters destroy critical communication infrastructure, smartphone-based Delay-Tolerant Networks (DTNs) can provide basic communication for civilians. Although field tests have shown the practicability of such systems, real-world experiments are expensive and hardly repeatable. Simulations are therefore required for the design and extensive evaluation of novel DTN protocols, but meaningful assertions require realistic mobility models for civilians. In this paper, trace files from a large-scale disaster field test are analyzed to identify typical human behavior patterns in a disaster area. Based on this, we derive a novel civilian disaster mobility model that incorporates identified behaviors such as group-based movement and clustering around points-of-interests such as hospitals and shelters. We evaluate the impact of mobility on DTN communication performance by comparing our model with other established mobility models as well as the trace file dataset in a simulative evaluation based on the field test scenario. In general, our mobility model leads to a more realistic assessment of DTN communication performance compared to other mobility models.
	Address	Technical University of Darmstadt; Technical University of Darmstadt; Technical University of Darmstadt; Technical University of Darmstadt; Technical University of Darmstadt; Technical University of Darmstadt
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	julian.zobel@kom.tu-darmstadt.de			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2319
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	Author	Tobias Meuser; Lars Baumgärtner; Patrick Lieser
	Title	Pandemic Skylines: Digital Twins for More Realism in Epidemic Simulations			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	133-145
	Keywords	Simulation, Disaster Communication, Pandemic
	Abstract	In the recent months, many measures have been taken by governments to fight the COVID-19 pandemic. Due to the unknown properties of the disease and a lack of experience with handling pandemics, the effectiveness of measures taken was often hard to evaluate the effectiveness of measures, leading to inefficient measures and late execution of efficient measures. Many models have been proposed to evaluate the performance of these measures on the spreading of a pandemic, but these models are commonly vastly simplified and, thus, limited in expressiveness. To extend the expressiveness of the models, we developed a epidemic simulation inside of a flexible and scalable city simulation game to analyse the counter measures to a pandemic in this city and spot common places of infection on a microscopic level. The configurability of our developed epidemic simulation will also be useful for potential future pandemics.
	Address	TU Darmstadt – KOM; TU Darmstadt – STG; TU Darmstadt – KOM
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	tobias.meuser@kom.tu-darmstadt.de			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2320
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	Author	Usman Anjum; Vladimir Zadorozhny; Prashant Krishnamurthy
	Title	TBAM: Towards An Agent-Based Model to Enrich Twitter Data			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	146-158
	Keywords	Agent-Based Model, Twitter, Modeling and Simulation, Event Detection
	Abstract	Twitter is widely being used by researchers to understand human behavior, e.g. how people behave when an event occurs and how it changes their microblogging pattern. The changing microblogging behavior can have an important application in the form of detecting events. However, the Twitter data that is available has limitations in it has incomplete and noisy information and has irregular samples. In this paper we create a model, calledTwitter Behavior Agent-Based Model (TBAM)to simulate Twitter pattern and behavior using Agent-Based Modeling(ABM). The generated data can be used in place or to complement the real-world data and improve the accuracy of event detection. We confirm the validity of our model by comparing it with real data collected from Twitter
	Address	University of Pittsburgh; University of Pittsburgh; University of Pittsburgh
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	usa3@pitt.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2321
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	Author	Xiaoyong Ni; Hong Huang; Wenxuan Dong; Chao Chen; Boni Su; Anying Chen
	Title	Scenario Prediction and Crisis Management for Rain-induced Waterlogging Based on High-precision Simulation			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	159-173
	Keywords	Rain-induced waterlogging, Scenario prediction, High-precision simulation, Crisis management
	Abstract	Many cities, especially those in developing countries, are not well prepared for the devastating disaster of exceptional rain-induced waterlogging caused by extreme rainfall. This paper proposes a waterlogging scenario prediction and crisis management method for such kind of extreme rainfall conditions based on high-precision waterlogging simulation. A typical urban region in Beijing, China is selected as the study area in this paper. High-precision and full-scale data in the study area requested for the waterlogging simulation are introduced. The simulation results show that the study area is still vulnerable to extreme rainfall and the subsequent waterlogging. The waterlogging situation is much more severe with the increase of the return period of rainfall. This study offers a good reference for the relevant government departments to make effective policy and take pointed response to the waterlogging problem.
	Address	Tsinghua University; Tsinghua University; Tsinghua University; Beijing Water Authority; Electric Power Planning & Engineering Institute; Tsinghua University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	nxy15@mails.tsinghua.edu.cn			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2322
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	Author	Alexander Gabriel; Babette Tecklenburg; Yann Guillouet; Frank Sill Torres
	Title	Threat analysis of offshore wind farms by Bayesian networks – a new modeling approach			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	174-185
	Keywords	Threat analysis, Bayesian networks, process modeling, Critical infrastructurs
	Abstract	As a result of the ongoing commitment to climate protection in more and more countries and the corresponding expansion of renewable energies, the importance of renewables for the security of electricity supply is also increasing. Wind energy generated in offshore wind farms already accounts for a significant share of the energy mix and will continue to grow in the future. Therefore, approaches and models for security assessment and protection against threats are also needed for these infrastructures. Due to the special characteristics and geographical location of offshore wind farms, they are confronted with particular challenges. In this context, this contribution outlines how an approach for threat analysis of offshore wind farms is to be developed within the framework of the new research project “ARROWS” of the German Aerospace Center. The authors first explain the structure of offshore wind farms and then present a possible modeling approach using Qualitative function models and Bayesian networks.
	Address	German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; German Aerospace Center – Institute for the Protection of Maritime Infrastructures; Ger
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	Alexander.Gabriel@dlr.de			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2323
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	Author	Ivar Svare Holand; Peter Mozelius; Trond Olav Skevik
	Title	A structured and dynamic model for emergency management exercises			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	186-197
	Keywords	Emergency exercises, Vulnerability assessment, Non-linear emergency exercise model, Norwegian-Swedish cross-border collaboration, Gaining Security Symbiosis (GSS) projects
	Abstract	Emergencies are management challenges, and emergency exercises that involve multiple collaborating parties is a means towards mastering them. Such exercises are often conducted in a virtual training environment based on complex disaster scenarios. The reported study was carried out using a requirement-focused design approach. The aim was to describe and discuss a relevant design for lean, dynamic, and cost-efficient emergency management exercise systems. Data were gathered from a literature study and analyses of earlier emergency management projects in which the authors had participated. Despite the complexity of many current emergency management exercises, the scenarios usually involve only the response phases and have a linear structure that hinders both didactic aspects and the software structure. The conclusion drawn from the study is that an emergency management exercise model should focus on managing the activities that correspond to alternatives that unfold from a dynamic scenario. Finally, the authors recommend the principles of alternate reality games as a way towards more dynamic and cost-efficient emergency exercise systems.
	Address	Nord University; Mid Sweden University; Nord University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	ivar.s.holand@nord.no			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2324
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	Author	Victor A. Bañuls; Andrzej M. Skulimowski; José Antonio Román Begines
	Title	Disaster Resilience Modeling of Municipal Water Supply Infrastructures in the Context of Atmospheric Threats			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	198-207
	Keywords	Disaster Modeling, Urban Resilience, Water Supply Infrastructures, Climate Change, Scenarios
	Abstract	The resilience of water supply infrastructure (WSI) is of utmost importance as threats to predominantly, although not exclusively, urban WSI may accompany virtually all kinds of natural disasters. In this paper, we present some of the challenges posed by climate change in modeling emergencies in WSIs. Climate change is a global phenomenon that significantly impacts global lifestyle. It is expected that increase in global temperatures causes sea levels to rise, increases the number of extreme weather events such as floods, droughts, and storms while highly impacting WSI. In this respect, the challenge is to be prepared for the unexpended by modeling various complex scenarios. Only with a multidisciplinary approach at the global, regional, national, and local levels, can success be achieved. We discuss some of the specific challenges posed by climate change in modeling emergencies in WSIs with a case study modeled using EMERTIC. EMERTIC is a software based on AI and scenarios, that is aimed at supporting decision making at different stages of the Emergency Management cycle.
	Address	Universidad Pablo de Olavide; AGH University of Science and Technology; EMASESA
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	vabansil@upo.es			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2325
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	Author	Duygu Pamukcu; Christopher Zobel; Yue Ge
	Title	Analysis of Orange County, Florida 311 System Service Requests During the COVID-19 Pandemic			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	208-217
	Keywords	Disaster management, COVID-19, 311 system, Orlando
	Abstract	The Orlando metropolitan area in Florida, where Walt Disney World is located, is intimately familiar with impacts of natural disasters because of the yearly threat of hurricanes in the southeastern United States. One of the tools that has aided them in their efforts to monitor and manage such disasters is their 311 non-emergency call system, through which local residents can issue requests to the municipality for disaster-related information or other services. This paper provides a preliminary examination of the potential for the Orange County 311 system to provide actionable information to them in support of their efforts to manage a different type of disaster: the COVID-19 pandemic. The potential of the system to support the County in this context is illustrated through several preliminary analyses of the complete set of service requests that were registered in the first ten months of 2020.
	Address	Virginia Tech; Virginia Tech; University of Central Florida
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	duygu@vt.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2326
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	Author	Anmol Haque; Duygu Pamukcu; Ruixiang Xie; Mohsen Zaker Esteghamati; Margaret Cowell; Jennifer L. Irish
	Title	Cascading Effects of Mass Gatherings on COVID-19 Infections from a Multi-hazard Perspective: A Case Study of New York City			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	218-227
	Keywords	COVID-19 pandemic, Mass gatherings, Multi-hazard, Vulnerability
	Abstract	The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed.
	Address	Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	anmol91@vt.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2327
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