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

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journal contribution
posted on 04.05.2022, 09:17 authored by Min Chen, Alfie Abdul-Rahman, Daniel Archambault, Jason Dykes, Panagiotis Ritsos, Aidan Slingsby, Thomas Torsney-Weir, Cagatay Turkay, Benjamin Bach, Rita Borgo, Alys Brett, Hui FangHui Fang, Radu Jianu, Saiful Khan, Robert Laramee, Louise Matthews, Phong Hai Nguyen, Richard Reeve, Jonathan Roberts, Franck Vidal, Qiru Wang, Joseph Wood, Kai Xu

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.

History

School

  • Science

Department

  • Computer Science

Published in

Epidemics

Volume

39

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

19/04/2022

Publication date

2022-04-28

Copyright date

2022

ISSN

1755-4365

Language

en

Depositor

Dr Hui Fang. Deposit date: 20 April 2022

Article number

100569