Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

Jason Dykes*, Alfie Abdul-Rahman, Daniel Archambault, Benjamin Bach, Rita Borgo, Min Chen, Jessica Enright, Hui Fang, Elif E Firat, Euan Freeman, Tuna Gönen, Claire Harris, Radu Jianu, Nigel W John, Saiful Khan, Andrew Lahiff, Robert S Laramee, Louise Matthews, Sibylle Mohr, Phong H NguyenAlma A M Rahat, Richard Reeve, Panagiotis D Ritsos, Jonathan C Roberts, Aidan Slingsby, Ben Swallow, Thomas Torsney-Weir, Cagatay Turkay, Robert Turner, Franck P Vidal, Qiru Wang, Jo Wood, Kai Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
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Abstract

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. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/.

Original languageEnglish
Article number20210299
Number of pages33
JournalPhilosophical Transactions of the Royal Society. A, Mathematical, Physical and Engineering Sciences
Volume380
Issue number2233
Early online date15 Aug 2022
DOIs
Publication statusPublished - 3 Oct 2022

Keywords

  • Visualization
  • Visual analytics
  • Epidemiological modelling
  • Computational notebooks
  • Visual design

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  • RAMP-VIS/RAMPVIS-PhilTransA-Supplement: RAMPVIS_PhilTransA_Supplement_v2.0

    Roberts, J. (Contributor), Gonen, T. (Contributor), Turner, R. (Contributor), Xu, K. (Contributor), Reeve, R. (Contributor), Swallow, B. T. (Contributor), Enright, J. (Contributor), Laramee, R. (Contributor), Fang, H. (Contributor), Bach, B. (Contributor), Wood, J. (Contributor), Lahiff, A. (Contributor), Freeman, E. (Contributor), Slingsby, A. (Contributor), Nguyen, P. (Contributor), Mohr, S. (Contributor), Ritsos, P. (Contributor), Abdul-Rahman, A. (Contributor), Matthews, L. (Contributor), Archambault, D. (Contributor), Wang, Q. (Contributor), Harris, C. (Contributor), Chen, M. (Contributor), Firat, E. (Contributor), John, N. (Contributor), Jianu, R. (Contributor), Torsney-Weir, T. (Contributor), Borgo, R. (Contributor), Rahat, A. (Contributor), Dykes, J. (Contributor), Khan, S. (Contributor), Vidal, F. (Contributor) & Turkay, C. (Contributor), Zenodo, 2022

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