Antweiler Dario, Sessler David, Rossknecht Maxim, Abb Benjamin, Ginzel Sebastian, Kohlhammer Jörn
Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Fraunhofer Center for Machine Learning, Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
Comput Graph. 2022 Aug;106:1-8. doi: 10.1016/j.cag.2022.05.013. Epub 2022 May 26.
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.
公共卫生部门（DPHs）在应对持续的COVID-19大流行时面临的一项重大挑战是追踪SARS-CoV-2感染群呈指数级增长的接触者。防止疾病进一步传播需要全面记录个人与感染群之间的联系。由于不明来源感染数量众多，医疗保健分析师需要通过积累的流行病学知识及其数据库中感染的元数据来识别相关病例和感染群。在此，我们提供了一个可视化分析仪表板，用于识别、评估和可视化COVID-19接触者追踪网络中的感染群。此外，我们展示了基于图的机器学习方法如何用于发现感染群之间缺失的联系，从而支持全面了解感染事件的任务。这项工作是通过与德国的公共卫生部门密切合作开展的。我们阐述了我们的仪表板如何支持公共卫生专家识别感染群，讨论了正在进行的开发和可能的扩展。
