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  • Min Chen
    University of Oxford
  • Alfie Abdul-Rahman
    Kings College London
  • Daniel Archambault
    Swansea University
  • Jason Dykes
    City University of London
  • Aidan Slingsby
    City University of London
  • Panagiotis D. Ritsos
  • Thomas Torsney-Weir
    Swansea University
  • Cagatay Turkay
    University of Warwick
  • Benjamin Bach
    University of Edinburgh
  • Rita Borgo
    King's College London
  • Alys Brett
    UK Atomic Energy Authority
  • Hui Fang
    Loughborough University
  • Radu Jianu
    City University of London
  • Saiful Khan
    University of Oxford
  • Robert S. Laramee
    University of Nottingham
  • Phong H. Nguyen
    University of Oxford
  • Richard Reeve
    University of Glasgow
  • Jonathan C. Roberts
  • Franck Vidal
  • Qiru Wang
    University of Nottingham
  • Jo Wood
    City University of London
  • Kai Xu
    Middlesex UniversityUniversity of Nottingham
The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been
an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium
and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.


  • Data visualisation, Visual Analytics, Pandemic responses, COVID-19, Model development
Original languageEnglish
Article number100569
Number of pages15
Early online date28 Apr 2022
Publication statusPublished - Jun 2022

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