Dykes, J. et al. (2022) Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233), 20210299. (doi: 10.1098/rsta.2021.0299) (PMID:35965467) (PMCID:PMC9376715)
![[img]](https://eprints.gla.ac.uk/style/images/fileicons/text.png) |
Text
269436.pdf - Published Version Available under License Creative Commons Attribution. 1MB |
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’.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Reeve, Professor Richard and Freeman, Dr Euan and Swallow, Dr Ben and Matthews, Professor Louise and Mohr, Dr Sibylle and Enright, Dr Jessica |
| Authors: | Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., Enright, J., Fang, H., Firat, E. E., Freeman, E., Gönen, T., Harris, C., Jianu, R., John, N. W., Khan, S., Lahiff, A., Laramee, R. S., Matthews, L., Mohr, S., Nguyen, P. H., Rahat, A. A. M., Reeve, R., Ritsos, P. D., Roberts, J. C., Slingsby, A., Swallow, B., Torsney-Weir, T., Turkay, C., Turner, R., Vidal, F. P., Wang, Q., Wood, J., and Xu, K. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine College of Science and Engineering > School of Computing Science College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
| Publisher: | Royal Society |
| ISSN: | 1364-503X |
| ISSN (Online): | 1471-2962 |
| Published Online: | 15 August 2022 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 380(2233): 20210299 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Data DOI: | 10.5281/zenodo.6303486 |
University Staff: Request a correction | Enlighten Editors: Update this record
Altmetric
Altmetric