RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

Min Chen; Alfie Abdul-Rahman; Daniel Archambault; Jason Dykes; Aidan Slingsby; Panagiotis D. Ritsos; Thomas Torsney-Weir; Cagatay Turkay; Benjamin Bach; Rita Borgo; Alys  Brett; Hui Fang; Radu Jianu; Saiful Khan; Robert S. Laramee; Phong H. Nguyen; Richard Reeve; Jonathan C. Roberts; Franck Vidal; Qiru Wang; Jo Wood; Kai Xu

doi:10.1016/j.epidem.2022.100569

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

Min Chen
, Alfie Abdul-Rahman
, Daniel Archambault
, Jason Dykes
, Aidan Slingsby
, Panagiotis D. Ritsos
, Thomas Torsney-Weir
, Cagatay Turkay
, Benjamin Bach
, Rita Borgo
, Alys Brett
, Hui Fang
, Radu Jianu
, Saiful Khan
, Robert S. Laramee
, Phong H. Nguyen
, Richard Reeve
, Jonathan C. Roberts
, Franck Vidal
, Qiru Wang

Jo Wood, Kai Xu

School of Computer Science & Engineering

Research output: Contribution to journal › Article › peer-review

253 Downloads (Pure)

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.

Original language	English
Article number	100569
Number of pages	15
Journal	Epidemics
Volume	39
Early online date	28 Apr 2022
DOIs	https://doi.org/10.1016/j.epidem.2022.100569
Publication status	Published - Jun 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Data visualisation
Visual Analytics
Pandemic responses
COVID-19
Model development

Access to Document

10.1016/j.epidem.2022.100569Licence: CC BY

Chen_et_al_RAMVIS_EpidemicsAccepted author manuscript, 6.75 MB
RAMPVISFinal published version, 3.74 MBLicence: CC BY

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19
Vidal, F. (PI)
1/02/21 → 1/08/22
Project: Research

Challenges and Opportunities in Data Visualization Education: A Call to Action
Bach, B., Keck, M., Rajabiyazdi, F., Losev, T., Meirelles, I., Dykes, J., Laramee, R. S., AlKadi, M., Stoiber, C., Huron, S., Perin, C., Morais, L., Aigner, W., Kosminsky, D., Boucher, M., Knudsen, S., Manataki, A., Aerts, J., Hinrichs, U. & Roberts, J. C. & 1 others, Carpendale, S., Jan 2024, In: IEEE Transactions on visualization and computer graphics. 30, p. 649-660 12 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
98 Downloads (Pure)
RAMPVIS: Answering the Challenges of Building Visualization Capabilities for Large-scale Emergency Responses
Chen, M., Abdul-Rahman, A., Archambault, D., Dykes, J., Ritsos, P. D., Slingsby, A., Torsney-Weir, T., Turkay, C., Bach, B., Borgo, R., Brett, A., Fang, H., Jianu, R., Khan, S., Laramee, R. S., Matthews, L., Nguyen, P. H., Reeve, R., Roberts, J. C. & Vidal, F. & 3 others, Wang, Q., Wood, J. & Xu, K., Oct 2023, IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes). IEEE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data
Gönen, T., Xing, Y., Turkay, C., Abdul-Rahman, A., Jianu, R., Fang, H., Freeman, E., Vidal, F. & Chen, M., Oct 2023, IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes). IEEE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

EduVis : Workshop on Visualization Education, Literacy, and Activities
Keck, M. (Chair), Huron, S. (Chair), Panagiotidou, G. (Chair), Stoiber, C. (Chair), Rajabiyazdi, F. (Chair), Perin, C. (Chair), Roberts, J. (Chair) & Bach, B. (Chair)
22 Oct 2023 → 27 Oct 2023
Activity: Participating in or organising an event › Participation in Academic conference

Cite this

Chen, M., Abdul-Rahman, A., Archambault, D., Dykes, J., Slingsby, A., Ritsos, P. D., Torsney-Weir, T., Turkay, C., Bach, B., Borgo, R., Brett, A., Fang, H., Jianu, R., Khan, S., Laramee, R. S., Nguyen, P. H., Reeve, R., Roberts, J. C., Vidal, F., ... Xu, K. (2022). RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses. Epidemics, 39, Article 100569. https://doi.org/10.1016/j.epidem.2022.100569

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title = "RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses",

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.",

keywords = "Data visualisation, Visual Analytics, Pandemic responses, COVID-19, Model development",

author = "Min Chen and Alfie Abdul-Rahman and Daniel Archambault and Jason Dykes and Aidan Slingsby and Ritsos, \{Panagiotis D.\} and Thomas Torsney-Weir and Cagatay Turkay and Benjamin Bach and Rita Borgo and Alys Brett and Hui Fang and Radu Jianu and Saiful Khan and Laramee, \{Robert S.\} and Nguyen, \{Phong H.\} and Richard Reeve and Roberts, \{Jonathan C.\} and Franck Vidal and Qiru Wang and Jo Wood and Kai Xu",

year = "2022",

month = jun,

doi = "10.1016/j.epidem.2022.100569",

language = "English",

volume = "39",

journal = "Epidemics",

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publisher = "Elsevier BV",

}

Chen, M, Abdul-Rahman, A, Archambault, D, Dykes, J, Slingsby, A, Ritsos, PD, Torsney-Weir, T, Turkay, C, Bach, B, Borgo, R, Brett, A, Fang, H, Jianu, R, Khan, S, Laramee, RS, Nguyen, PH, Reeve, R, Roberts, JC , Vidal, F, Wang, Q, Wood, J & Xu, K 2022, 'RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses', Epidemics, vol. 39, 100569. https://doi.org/10.1016/j.epidem.2022.100569

TY - JOUR

T1 - RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

AU - Chen, Min

AU - Abdul-Rahman, Alfie

AU - Archambault, Daniel

AU - Dykes, Jason

AU - Slingsby, Aidan

AU - Ritsos, Panagiotis D.

AU - Torsney-Weir, Thomas

AU - Turkay, Cagatay

AU - Bach, Benjamin

AU - Borgo, Rita

AU - Brett, Alys

AU - Fang, Hui

AU - Jianu, Radu

AU - Khan, Saiful

AU - Laramee, Robert S.

AU - Nguyen, Phong H.

AU - Reeve, Richard

AU - Roberts, Jonathan C.

AU - Vidal, Franck

AU - Wang, Qiru

AU - Wood, Jo

AU - Xu, Kai

PY - 2022/6

Y1 - 2022/6

N2 - 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.

AB - 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.

KW - Data visualisation

KW - Visual Analytics

KW - Pandemic responses

KW - COVID-19

KW - Model development

U2 - 10.1016/j.epidem.2022.100569

DO - 10.1016/j.epidem.2022.100569

M3 - Article

SN - 1755-4365

VL - 39

JO - Epidemics

JF - Epidemics

M1 - 100569

ER -

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

Abstract

UN SDGs

Keywords

Access to Document

Fingerprint

Projects

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19

Research output

Challenges and Opportunities in Data Visualization Education: A Call to Action

RAMPVIS: Answering the Challenges of Building Visualization Capabilities for Large-scale Emergency Responses

Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data

Activities

EduVis : Workshop on Visualization Education, Literacy, and Activities

Cite this