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

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

Authors

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

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ROBERT LARAMEE ROBERT.LARAMEE@NOTTINGHAM.AC.UK
Professor of Computer Science

Louise Matthews

Sibylle Mohr

Phong H. Nguyen

Alma 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

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Dr KAI XU KAI.XU@NOTTINGHAM.AC.UK
Associate Professor



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/.

Citation

Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., …Xu, K. (2022). Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 380(2233), https://doi.org/10.1098/rsta.2021.0299

Journal Article Type Article
Acceptance Date Mar 18, 2022
Online Publication Date Aug 15, 2022
Publication Date Oct 3, 2022
Deposit Date Nov 15, 2022
Publicly Available Date Nov 15, 2022
Journal Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Print ISSN 1364-503X
Electronic ISSN 1471-2962
Publisher The Royal Society
Peer Reviewed Peer Reviewed
Volume 380
Issue 2233
DOI https://doi.org/10.1098/rsta.2021.0299
Keywords General Physics and Astronomy; General Engineering; General Mathematics
Public URL https://nottingham-repository.worktribe.com/output/10082823
Publisher URL https://royalsocietypublishing.org/doi/10.1098/rsta.2021.0299

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