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RAMPVIS : answering the challenges of building visualisation capabilities for large-scale emergency responses
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(2022) RAMPVIS : answering the challenges of building visualisation capabilities for large-scale emergency responses. Epidemics, 39 . 100569. doi:10.1016/j.epidem.2022.100569
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WRAP-RAMPVIS-answering-challenges-building-visualisation-capabilities-large-scale-emergency-responses-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3830Kb) | Preview |
Official URL: https://doi.org/10.1016/j.epidem.2022.100569
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: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine |
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| Divisions: | Faculty of Social Sciences > Centre for Interdisciplinary Methodologies | ||||||||
| Library of Congress Subject Headings (LCSH): | Visualization , Visual analytics , Information visualization, Information storage and retrieval systems -- Medical care, Medical care -- Computer simulation, Emergency management -- Computer simulation, COVID-19 (Disease) -- Computer simulation | ||||||||
| Journal or Publication Title: | Epidemics | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 1755-4365 | ||||||||
| Official Date: | June 2022 | ||||||||
| Dates: |
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| Volume: | 39 | ||||||||
| Article Number: | 100569 | ||||||||
| DOI: | 10.1016/j.epidem.2022.100569 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| RIOXX Funder/Project Grant: |
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