Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations
Authors
Dykes, JasonAbdul-Rahman, Alfie
Archambault, Daniel
Bach, Benjamin
Borgo, Rita
Chen, Min
Enright, Jessica
Fang, Hui
Firat, Elif E.
Freeman, Euan
Gonen, 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
Affiliation
University of London; King’s College London; Swansea University; University of Edinburgh; University of Oxford; Loughborough University; Nottingham University; University of Glasgow; Biomathematics and Statistics Scotland; University of Chester; UKAEA; Bangor University; Warwick University; University of Sheffield; Middlesex UniversityPublication Date
2022-08-15
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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. https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/Citation
Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., Enright, J., Fang, H., Firat, E. E., Freeman, E., Gonen, T., Harris, C., Jianu, R., John, N. W., Khan, S., Lahiff, A., Laramee, R. S., Matthews, L., Mohr, S., ... Xu, K. (2022 - forthcoming). Visualization for epidemiological modelling: Challenges, solutions, reflections & recommendations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380, 20210299. https//doi.org/10.1098/rsta.2021.0299Publisher
The Royal SocietyJournal
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering SciencesAdditional Links
https://royalsocietypublishing.org/journal/rstahttps://royalsocietypublishing.org/doi/full/10.1098/rsta.2021.0299
Type
ArticleISSN
1364-503XEISSN
1471-2962ae974a485f413a2113503eed53cd6c53
10.1098/rsta.2021.0299
Scopus Count
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/