@article{Chen_M-2022_86452,
title = {RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses},
author = {Chen, M. and Abdul-Rahman, A. and Archambault, D. and Dykes, J. and Ritsos, P. and Slingsby, A. and Torsney-Weir, T. and Turkay, C. and Bach, B. and Borgo, R. and Brett, A. and Fang, H. and Jianu, R. and Khan, S. and Laramee, R. and Matthews, L. and Nguyen, P. and Reeve, R. and Roberts, J. and Vidal, F. and Wang, Q. and Wood, J. and Xu, K.},
month = {jun},
year = {2022},
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.},
volume = {39},
journal = {Epidemics},
publisher = {Elsevier},
url = {http://dx.doi.org/10.1016/j.epidem.2022.100569},
}
