Edjossan-Sossou, A., Selouane, K., Sayah, M. A., Ouabou, M., Vignote, C., Capitaine, M., et al. (2023). An innovative scenario-based modeling tool for the management of resilient water resources. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 808–821). Omaha, USA: University of Nebraska at Omaha.
Abstract: As freshwater availability for domestic and agro-industrial uses is highly sensitive to climate change, there is an urgent need for the management of this critical resource to be resilient, i.e., to cope with and rapidly recover from climate risks. To achieve this resilient goal, decision-makers need to have a comprehensive understanding of (i) the current and future local water resources, (ii) the ways these resources are and will be impacted by climate change, and (iii) the effects their management decisions can have. In this paper, we present an innovative scenario based modeling tool that help decision-makers make the most appropriate decision towards managing water resources: the Resilience Performance Assessment (RPA). This GIS-based decision support tool illustrates the current and future effects of climate change on local water resources and simulates the outcomes of different water resources management strategies. The RPA helps guide decision-makers towards the implementation of context specific adaptation strategies.
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Rode-Hasinger, S., Haberle, M., Racek, D., Kruspe, A., & Zhu Xiao Xiang. (2023). TweEvent: A dataset of Twitter messages about events in the Ukraine conflict. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 407–416). Omaha, USA: University of Nebraska at Omaha.
Abstract: Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events.
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Encarnación, T., & Wilks, C. R. (2023). Role of Expressed Emotions on the Retransmission of Help-Seeking Messages during Disasters. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 340–352). Omaha, USA: University of Nebraska at Omaha.
Abstract: Emergency managers rely on formal and informal communication channels to identify needs in post-disaster environments. Message retransmission is a critical factor to ensure that help-seekers are identified by disaster responders. This paper uses a novel annotated dataset of Twitter posts from four major disasters that impacted the United States in 2021, to quantify the effect that expressed emotions and support typology have on retransmission. Poisson regression models are estimated, and the results show that messages seeking instrumental support are more likely to be retransmitted. Expressions of anger, fear, and sadness increase overall retweets. Moreover, expressions of anger, anticipation, or sadness increase the likelihood of retransmission for messages that seek instrumental help.
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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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Sterl, S., Billig, A., Taffo, F. W., & Gerhold, L. (2023). Visualizing the Psychosocial Situation in Crises and Disasters: Conceptualizing a Multi-Functional Crisis Information Platform (CIP-PS). In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 252–262). Omaha, USA: University of Nebraska at Omaha.
Abstract: Crises and disasters are becoming more frequent, long-lasting, complex, and interdependent. This can lead to negative psychosocial consequences in vulnerable population groups, increasing the need to (1) monitor psychosocial indicators and (2) make information on psychosocial topics available to decision-makers, the scientific community, and the public. In this WiPe paper, we present a way to systematically visualize, research, and document different types of psychosocial data in crises and disasters by developing a “Multi-Functional Crisis Information Platform for Psychosocial Situations”, called CIP-PS. The CIP-PS has three components, i.e., an information dashboard (CIP-DAB), a research platform (CIP-REP), and a documentation (CIP-DOC) component which together help visualize, research and document psychosocial topics, such as the psychosocial situation picture in Germany. The platform is a valuable tool for presenting relevant psychosocial information in the context of disaster public health. Its strength lies in an extensive connection between the three components related to healthcare informatics.
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