Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations

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  • Jason Dykes
    City University of London
  • Alfie Abdul-Rahman
    King's College London
  • Daniel Archambault
    Swansea University
  • Benjamin Bach
    University of Edinburgh
  • Rita Borgo
    King's College London
  • Min Chen
    University of Oxford
  • Jessica Enright
    University of Glasgow
  • Hui Fang
    Loughborough University
  • Elif E. Firat
    University of Nottingham
  • Euan Freeman
    University of Glasgow
  • Tuna Gönen
    University of Oxford
  • Claire Harris
    Biomathematics and Statistics Scotland
  • Radu Jianu
    City University of London
  • Nigel W. John
    University of Chester
  • Saiful Khan
    University of Oxford
  • Andrew Lahiff
    UK Atomic Energy Authority
  • Robert S. Laramee
    University of Nottingham
  • Louise Matthews
    University of Glasgow
  • Sibylle Mohr
    University of Glasgow
  • Phong H. Nguyen
    University of Oxford
  • Alma A. M. Rahat
    Swansea University
  • Richard Reeve
    University of Glasgow
  • Panagiotis D. Ritsos
  • Jonathan C. Roberts
  • Aidan Slingsby
    City University of London
  • Ben Swallow
    University of Glasgow
  • Thomas Torsney-Weir
    Swansea University
  • Cagatay Turkay
    University of Warwick
  • Robert Turner
    University of Sheffield
  • Franck Vidal
  • Qiru Wang
    University of Nottingham
  • Jo Wood
    City University of London
  • Kai Xu
    Middlesex UniversityUniversity of Nottingham
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs – a series of ideas, approaches and methods taken from existing visualization research and practice – deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type; and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond.


  • visualization, epidemiological modelling, visual design, computational notebooks, Visual analytics
Original languageEnglish
Article number20210299
Number of pages33
JournalPhilosophical Transactions of the Royal Society A
Issue number2233
Early online date15 Aug 2022
Publication statusPublished - 3 Oct 2022

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