RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses

Chen, M. ORCID logoORCID: https://orcid.org/0000-0001-5320-5729, Abdul-Rahman, A. ORCID logoORCID: https://orcid.org/0000-0002-6257-876X, Archambault, D. ORCID logoORCID: https://orcid.org/0000-0003-4978-8479, Dykes, J. ORCID logoORCID: https://orcid.org/0000-0002-8096-5763, Ritsos, P. D. ORCID logoORCID: https://orcid.org/0000-0001-9308-3885, Slingsby, A. ORCID logoORCID: https://orcid.org/0000-0003-3941-553X, Torsney-Weir, T. ORCID logoORCID: https://orcid.org/0000-0002-0329-2198, Turkay, C. ORCID logoORCID: https://orcid.org/0000-0001-6788-251X, Bach, B., Borgo, R. ORCID logoORCID: https://orcid.org/0000-0003-2875-6793, Brett, A., Fang, H. ORCID logoORCID: https://orcid.org/0000-0001-9365-7420, Jianu, R. ORCID logoORCID: https://orcid.org/0000-0002-5834-2658, Khan, S. ORCID logoORCID: https://orcid.org/0000-0002-6796-5670, Laramee, R. S. ORCID logoORCID: https://orcid.org/0000-0002-3874-6145, Matthews, L., Nguyen, P. H. ORCID logoORCID: https://orcid.org/0000-0001-5643-0585, Reeve, R. ORCID logoORCID: https://orcid.org/0000-0003-2589-8091, Roberts, J. C. ORCID logoORCID: https://orcid.org/0000-0001-7718-3181, Vidal, F. P. ORCID logoORCID: https://orcid.org/0000-0002-2768-4524, Wang, Q. ORCID logoORCID: https://orcid.org/0000-0003-3397-308X, Wood, J. ORCID logoORCID: https://orcid.org/0000-0001-9270-247X and Xu, Kai ORCID logoORCID: https://orcid.org/0000-0003-2242-5440 (2022) RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses. Epidemics, 39 , 100569. ISSN 1755-4365 [Article] (doi:10.1016/j.epidem.2022.100569)

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Abstract

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.

Item Type: Article
Keywords (uncontrolled): Pandemic responses, Data visualisation, Model development, COVID-19, Visual analytics
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 35029
Notes on copyright: Published version © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Depositing User: Jisc Publications Router
Date Deposited: 11 May 2022 10:05
Last Modified: 06 Jun 2022 11:34
URI: https://eprints.mdx.ac.uk/id/eprint/35029

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