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

Article

Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., Enright, J., Fang, H., Firat, E., Freeman, E., Gönen, T., Harris, C., Jianu, R., John, N., Khan, S., Lahiff, A., Laramee, R.S., Matthews, L., Mohr, S., Nguyen, P.H., Rahat, A.A.M., Reeve, R., Ritsos, P., Roberts, J., Slingsby, A., Swallow, B., Torsney-Weir, T., Turkay, C., Turner, R., Vidal, F., Wang, Q., Wood, J. and Xu, K. 2022. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 380 (2233). https://doi.org/10.1098/rsta.2021.0299

Publication dates
Type	Article
Title	Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations
Authors	Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., Enright, J., Fang, H., Firat, E., Freeman, E., Gönen, T., Harris, C., Jianu, R., John, N., Khan, S., Lahiff, A., Laramee, R.S., Matthews, L., Mohr, S., Nguyen, P.H., Rahat, A.A.M., Reeve, R., Ritsos, P., Roberts, J., Slingsby, A., Swallow, B., Torsney-Weir, T., Turkay, C., Turner, R., Vidal, F., Wang, Q., Wood, J. and Xu, K.
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’.
Keywords	epidemiological modelling; visual analytics; visual design; computational notebooks; visualization
Sustainable Development Goals	9 Industry, innovation and infrastructure
Middlesex University Theme	Health & Wellbeing
Publisher	The Royal Society
Journal	Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
ISSN	1364-503X
Electronic	1471-2962
Online	15 Aug 2022
Print	03 Oct 2022
Publication process dates
Submitted	14 Oct 2021
Accepted	18 Mar 2022
Deposited	05 Mar 2026
Output status	Published
Publisher's version	rsta.2021.0299.pdf License CC BY 4.0 File Access Level Open
Copyright Statement	© 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, provided the original author and source are credited
Additional information	This article has a correction: Correction to: ‘Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations’ (2022) by Dykes et al [https://royalsocietypublishing.org/rsta/article/380/2235/20220296/11...].
Digital Object Identifier (DOI)	https://doi.org/10.1098/rsta.2021.0299
Related Output
Has version	https://openaccess.city.ac.uk/id/eprint/28123
Has version	https://eprints.whiterose.ac.uk/id/eprint/190242/
Language	English

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