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

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

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RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses. / Chen, Min; Abdul-Rahman, Alfie; Archambault, Daniel et al.
In: Epidemics, Vol. 39, 100569, 06.2022.

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

HarvardHarvard

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

APA

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

CBE

Chen M, Abdul-Rahman A, Archambault D, Dykes J, Slingsby A, Ritsos PD, Torsney-Weir T, Turkay C, Bach B, Borgo R, et al. 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

MLA

Chen, Min et al. "RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses". Epidemics. 2022. 39. https://doi.org/10.1016/j.epidem.2022.100569

VancouverVancouver

Chen M, Abdul-Rahman A, Archambault D, Dykes J, Slingsby A, Ritsos PD et al. RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses. Epidemics. 2022 Jun;39:100569. Epub 2022 Apr 28. doi: 10.1016/j.epidem.2022.100569

Author

Chen, Min ; Abdul-Rahman, Alfie ; Archambault, Daniel et al. / RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses. In: Epidemics. 2022 ; Vol. 39.

RIS

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 beenan 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 consortiumand 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 beenan 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 consortiumand 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

VL - 39

JO - Epidemics

JF - Epidemics

SN - 1755-4365

M1 - 100569

ER -