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

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)

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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:Mohr, Dr Sibylle and Enright, Dr Jessica and Reeve, Professor Richard and Swallow, Dr Ben and Matthews, Professor Louise and Freeman, Dr Euan
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

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
311856Open Epidemiology for COVID-19: a transparent, traceable, open source pipeline for reproducible scienceRichard ReeveScience and Technology Facilities Council (STFC)ST/V006126/1Institute of Biodiversity, Animal Health and Comparative Medicine