Visualising Contact Networks in Response to COVID-19
Contact networks – dynamic networks of when, where and between whom infections spread -- carry invaluable information for researchers to understand the dynamics of pandemics, for policy-makers to develop interventions to control outbreaks, and for the government and the media in informing the public in rich and relatable ways while also making the policy decisions and potential outcomes transparent. The ongoing COVID-19 pandemic revealed, however, that the 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 project will follow a human-centred design 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. As a team comprising of human-data interaction and visualisation researchers, and epidemiologists, the project will involve continuous engagement with the different stakeholders and large-scale crowd-sourced studies to understand the needs, opportunities, and affordances of those who analyse, make decisions with, and engage with the complex information in these contact networks.
In addition to the findings and learnings through these studies, the project aims to deliver
- 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.
This project is funded by EPSRC under UKRI's COVID-19 response funding programme (project ID: EP/V033670/1). The project is a collaboration between CIM and the Computer Science Department at Swansea University and the Zeeman Institute at University of Warwick. The investigators are:
- Daniel Archambault, Swansea University
- Cagatay Turkay, University of Warwick
- Matt Keeling, University of Warwick