Rocco Sergio Palermo, & Antonio De Nicola. (2022). A Simulation Framework for Epidemic Spreading in Semantic Social Networks. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 266–273). Tarbes, France.
Abstract: Epidemic spreading simulation in social networks denotes a set of techniques that allow to assess the temporal evolution and the consequences of a pandemic. They were largely used by governments and International health organizations during the COVID-19 world crisis to decide the appropriate countermeasures to limit the diffusion of the disease. Among them, the existing simulation techniques based on a network model aimed at studying the infectious disease dynamics have a prominent role and are widely adopted. However, even if they leverage the topological structure of a social network, they disregard the intrinsic and individual features of its members. A semantic social network is defined as a structure consisting of interlinking layers, which include a social network layer, to represent people and their relationships and a concept network layer, to represent concepts, their ontological relationships and implicit similarities. Here, we propose a novel epidemic simulation framework that allows to describe a community of people as a semantic social network, to adopt the most commonly used compartmental models for describing epidemic spreading, such as Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Removed (SIR), and to enable semantic reasoning to increase the accuracy of the simulation. Finally, we show how to use the framework to simulate the impact of a pandemic in a community where the job of each member is known in advance.
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Simon Mille, Gerard Casamayor, Jens Grivolla, Alexander Shvets, & Leo Wanner. (2022). Automatic Multilingual Incident Report Generation for Crisis Management. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 299–309). Tarbes, France.
Abstract: Successful and effucient crisis management depends on the availability of all accessible relevant information on the incidents during a crisis. The sources of this information are very often multiple and manifold – in particular in the case of environmental crises such as wild fires, floods, drought, etc. For the staff of the control centres it can be a challenge to follow up on all of them. In this paper, we present work in progress on an automatic multilingual incident report generator that produces summaries of all environmental incidents communicated by citizens or authorities in a given time range for a given region in terms of a text message, an audio, a video or an image and analyzed by dedicated modules into uniform knowledge representation structures.
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Elmhadhbi Linda, Karray Mohamed Hedi, & Archimède Bernard. (2018). Towards an Operational Emergency Response System for Large Scale Situations: POLARISC. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 778–785). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: After a lot of recent natural and human-made disasters all over the word, the large scale emergency response process is becoming very critical and challenging. Lives can be lost and property can be harmed. To respond to these major threats, an effective operational emergency response system needs to address the necessity of data sharing, information exchange and correlation between different Emergency Responders (ERs) including firefighters, police, health care services, army, municipality and so on to successfully respond to large scale disasters. Therefore, the goal of this paper is to introduce POLARISC, an interoperable software solution based on a common and modular ontology shared by all the ERs. Its main objective is to solve the problem of semantic difference and heterogeneity of data to guarantee a common understanding among the various ERs in order to coordinate and to obtain a real time operational picture of the situation.
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Jürgen Moßgraber, Désirée Hilbring, Hylke van der Schaaf, Philipp Hertweck, Efstratios Kontopoulos, Panagiotis Mitzias, et al. (2018). The sensor to decision chain in crisis management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 754–763). 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. Decision Support Systems for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.
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Efstratios Kontopoulos, Panagiotis Mitzias, Jürgen Moßgraber, Philipp Hertweck, Hylke van der Schaaf, Désirée Hilbring, et al. (2018). Ontology-based Representation of Crisis Management Procedures for Climate Events. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1064–1073). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more “all-around” lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management.
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