1. Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces.
- Author
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Antweiler, Dario, Sessler, David, Rossknecht, Maxim, Abb, Benjamin, Ginzel, Sebastian, and Kohlhammer, Jörn
- Subjects
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CONTACT tracing , *COVID-19 , *INFECTIOUS disease transmission , *GRAPH algorithms , *COVID-19 pandemic , *VISUAL analytics , *PREVENTIVE medicine - Abstract
A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 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 dashboard 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 dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions. [Display omitted] • Visual analysis of infections supports the work of public health experts in new ways. • We can predict undocumented links between infected persons from few features. • Geospatial, temporal and graph visualization provide insights into the infection data. • Public health experts give positive feedback on the visual analytics framework. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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