Visual Analytics for explaining and analysisng contact tracing networks

Contact tracing networks carry invaluable information for researchers to understand the spread of the virus, for policy-makers to control the COVID-19 outbreak, and for the government and the media in informing the public in rich ways. However, current data science tools fall short for the exploratory and explanatory analysis of the temporal, spatial and social aspects of these networks, and little is known on how most effectively the results of such analyses can be communicated broadly. This lack of a toolbox leads to organisations wasting resources on developing partial solutions designed without broad stakeholder engagement. To this end, this project aims to follow a user-centred approach to develop visual analytics methods for the analysis of large collections of contact tracing networks along with techniques for the communication of analysis results in transparent, comprehensive, yet engaging ways. Contact networks come with noteworthy technical and ethical challenges: inherent uncertainties due to the variation in their generation mechanisms, e.g., apps, hospital records, by volunteers; and high volumes of complex and sensitive information represented as event-based interactions with spatio-temporal facets. This project responds to these challenges through two deliverables comprising visualisation methods working simultaneously at group and individual levels while communicating the general trends in the spread: - Visualisations aimed at experts for understanding collections of contact networks to inform public health policies and make in-depth investigations without compromising individuals' privacy. - Visualisations for communicating analysis results with the general public for information and evidencing policy recommendations with representations having a purely explanatory emphasis.