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

Research output: Contribution to journalArticlepeer-review

Jason Dykes, Alfie Abdul-Rahman, Daniel Archambault, Benjamin Bach, Rita Borgo, Min Chen, Jessica Enright, Hui Fang, Elif Firat, Euan Freeman, Tuna Gonen, Claire Harris, Radu Jianu, Nigel John, Saiful Khan, Andrew Lahiff, Robert S. Laramee, Louise Matthews, Sibylle Mohr, Phong Nguyen & 13 more Alma Rahat, Richard Reeve, Panagiotis Ritsos, Jonathan Roberts, Aidan Slingsby, Ben Swallow, Thomas Torsney-Weir, Cagatay Turkay, Robert Turner, Franck Vidal, Qiru Wang, Jo Wood, Kai Xu

Original languageEnglish
Article number20210299
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume380
Issue number2233
Early online date15 Aug 2022
DOIs
Accepted/In press18 Mar 2022
E-pub ahead of print15 Aug 2022
Published3 Oct 2022

Bibliographical note

Funding Information: This work was supported in part by the UKRI/EPSRC grant nos. EP/V054236/1 and EP/V033670/1 and UKRI/STFC grant no. ST/V006126/1. Acknowledgements Publisher Copyright: © 2022 The Authors.

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King's Authors

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/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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