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

Lookup NU author(s): Dr Daniel ArchambaultORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Author(s). 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.

Publication metadata

Author(s): Chen M, Abdul-Rahman A, Archambault D, Dykes J, Ritsos PD, Slingsby A, Torsney-Weir T, Turkay C, Bach B, Borgo R, Brett A, Fang H, Jianu R, Khan S, Laramee RS, Matthews L, Nguyen PH, Reeve R, Roberts JC, Vidal FP, Wang Q, Wood J, Xu K

Publication type: Article

Publication status: Published

Journal: Epidemics

Year: 2022

Volume: 39

Print publication date: 01/06/2022

Online publication date: 28/04/2022

Acceptance date: 19/04/2022

Date deposited: 15/09/2023

ISSN (print): 1755-4365

ISSN (electronic): 1878-0067

Publisher: Elsevier BV


DOI: 10.1016/j.epidem.2022.100569

PubMed id: 35597098


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