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

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

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Volume: 380, Issue: 2233

Swansea University Authors: Daniel Archambault Orcid Logo, Rita Borgo Orcid Logo, Alma Rahat Orcid Logo, Tom Torsney-Weir Orcid Logo

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DOI (Published version): 10.1098/rsta.2021.0299

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...

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Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
ISSN: 1364-503X 1471-2962
Published: The Royal Society 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59890
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spelling v2 59890 2022-04-20 Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false c4675d4072e4b2b3921ae57666f1d9ff 0000-0003-2875-6793 Rita Borgo Rita Borgo true false 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 6675d91d11195ef4c16eefd3fa316474 0000-0002-0329-2198 Tom Torsney-Weir Tom Torsney-Weir true false 2022-04-20 SCS 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. Journal Article Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 380 2233 The Royal Society 1364-503X 1471-2962 visualization, visual analytics, epidemiologicalmodelling, computational notebooks, visualdesign 3 10 2022 2022-10-03 10.1098/rsta.2021.0299 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19 Funder: Engineering and Physical Sciences Research Council (EPSRC) Visual Analytics for Explaining and Analysing Contact Tracing Networks Funder: Engineering and Physical Sciences Research Council (EPSRC) Grant number: EP/V054236/1, Grant number: EP/V033670/1, UKRI/STFC grant ST/V006126/1 2023-11-15T12:48:34.3478401 2022-04-20T12:44:54.6568359 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jason Dykes 0000-0002-8096-5763 1 Alfie Abdul-Rahman 0000-0002-6257-876x 2 Daniel Archambault 0000-0003-4978-8479 3 Benjamin Bach 0000-0002-9201-7744 4 Rita Borgo 0000-0003-2875-6793 5 Min Chen 0000-0001-5320-5729 6 Jessica Enright 7 Hui Fang 8 Elif E. Firat 0000-0001-9497-7928 9 Euan Freeman 0000-0002-6586-6951 10 Tuna Gönen 0000-0002-0938-2522 11 Claire Harris 0000-0003-0852-2340 12 Radu Jianu 0000-0002-5834-2658 13 Nigel W. John 0000-0001-5153-182x 14 Saiful Khan 15 Andrew Lahiff 0000-0002-2785-4116 16 Robert S. Laramee 17 Louise Matthews 0000-0003-4420-8367 18 Sibylle Mohr 0000-0002-9089-6327 19 Phong H. Nguyen 20 Alma Rahat 0000-0002-5023-1371 21 Richard Reeve 0000-0003-2589-8091 22 Panagiotis D. Ritsos 0000-0001-9308-3885 23 Jonathan C. Roberts 0000-0001-7718-3181 24 Aidan Slingsby 0000-0003-3941-553x 25 Ben Swallow 0000-0002-0227-2160 26 Tom Torsney-Weir 0000-0002-0329-2198 27 Cagatay Turkay 0000-0001-6788-251x 28 Robert Turner 29 Franck P. Vidal 0000-0002-2768-4524 30 Qiru Wang 0000-0003-3397-308x 31 Jo Wood 0000-0001-9270-247x 32 Kai Xu 0000-0003-2242-5440 33 59890__25809__83a6175acc314cb78ab726982cfc5938.pdf 59890.pdf 2022-11-16T14:57:06.0055981 Output 1217710 application/pdf Version of Record true Copyright: 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
spellingShingle Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
Daniel Archambault
Rita Borgo
Alma Rahat
Tom Torsney-Weir
title_short Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
title_full Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
title_fullStr Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
title_full_unstemmed Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
title_sort Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
author_id_str_mv 8fa6987716a22304ef04d3c3d50ef266
c4675d4072e4b2b3921ae57666f1d9ff
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author_id_fullname_str_mv 8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
c4675d4072e4b2b3921ae57666f1d9ff_***_Rita Borgo
6206f027aca1e3a5ff6b8cd224248bc2_***_Alma Rahat
6675d91d11195ef4c16eefd3fa316474_***_Tom Torsney-Weir
author Daniel Archambault
Rita Borgo
Alma Rahat
Tom Torsney-Weir
author2 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. Nguyen
Alma Rahat
Richard Reeve
Panagiotis D. Ritsos
Jonathan C. Roberts
Aidan Slingsby
Ben Swallow
Tom Torsney-Weir
Cagatay Turkay
Robert Turner
Franck P. Vidal
Qiru Wang
Jo Wood
Kai Xu
format Journal article
container_title Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
container_volume 380
container_issue 2233
publishDate 2022
institution Swansea University
issn 1364-503X
1471-2962
doi_str_mv 10.1098/rsta.2021.0299
publisher The Royal Society
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description 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.
published_date 2022-10-03T12:48:37Z
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