EuroVA2021
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Browsing EuroVA2021 by Author "Kohlhammer, Jörn"
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Item LFPeers: Temporal Similarity Search in Covid-19 Data(The Eurographics Association, 2021) Burmeister, Jan; Bernard, Jürgen; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, JürgenWhile there is a wide variety of visualizations and dashboards to help understand the data of the Covid-19 pandemic, hardly any of these support important analytical tasks, especially of temporal attributes. In this paper, we introduce a general concept for the analysis of temporal and multimodal data and the system LFPeers that applies this concept to the analysis of countries in a Covid-19 dataset. Our concept divides the analysis in two phases: a search phase to find the most similar objects to a target object before a time point t0, and an exploration phase to analyze this subset of objects after t0. LFPeers targets epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures.Item Towards the Detection and Visual Analysis of COVID-19 Infection Clusters(The Eurographics Association, 2021) Antweiler, Dario; Sessler, David; Ginzel, Sebastian; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, JürgenA major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics framework to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our systems supports the identification of clusters by public health experts and discuss ongoing developments and possible extensions.