Loading...
Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations
Dykes, Jason Abdul-Rahman, Alfie Archambault, Daniel Bach, Benjamin Borgo, Rita Chen, Min Enright, Jessica Fang, Hui Firat, Elif E. Freeman, Euan
Dykes, Jason
Abdul-Rahman, Alfie
Archambault, Daniel
Bach, Benjamin
Borgo, Rita
Chen, Min
Enright, Jessica
Fang, Hui
Firat, Elif E.
Freeman, Euan
Advisors
Editors
Other Contributors
EPub Date
Publication Date
2022-08-15
Submitted Date
Collections
Files
Loading...
Main article
Adobe PDF, 13.72 MB
Other Titles
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.
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.2021.0299
Publisher
The Royal Society
Journal
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Research Unit
DOI
10.1098/rsta.2021.0299
PubMed ID
PubMed Central ID
Type
Article
Language
Description
Series/Report no.
ISSN
1364-503X
EISSN
1471-2962