Perspective Chapter: EnsembleDashVis Views and Volunteers – A Retrospective and Early History

Author:

Wang Qiru,Borgo Rita,S. Laramee Robert

Abstract

This paper offers a retrospective history of the early development stages of EnsembleDashVis, a visualization dashboard specifically crafted to support modelers in interpreting a simulation model utilized to forecast COVID-19 trends. The volunteer effort behind this dashboard was collaboratively contributed with the Scottish COVID-19 Response Consortium (SCRC), with the objective of enabling an enhanced comprehension of the complex dynamics of the pandemic through modeling of COVID-19 data collected by NHS Scotland during the first wave of the outbreak. This retrospective chronicles the design and development journey of the system, guided by feedback from domain experts, all taking place amidst the exceptional circumstances of an unprecedented pandemic. The outcome of this volunteer work is a streamlined relationship discovery process between sets of simulation input parameters and their respective outcomes, which leverages the power of information visualization and visual analytics (VIS). We hope that this retrospective will serve as an insightful resource for future effort, in VIS for pandemic and emergency responses and promote mutually beneficial engagement between scientific communities.

Publisher

IntechOpen

Reference38 articles.

1. University of Glasgow. The Scottish COVID-19 Response Consortium. The University of Glasgow; 2020. Available from: https://www.gla.ac.uk/research/az/scrc/ [Accessed: May 13, 2023]

2. Rapid Assistance in Modelling the Pandemic: RAMP — Royal Society. 2020. Available from: https://royalsociety.org/topics-policy/Health%20and%20wellbeing/ramp/ [Accessed: May 13, 2023]

3. Visualization and Visual Analytics in Support of Rapid Assistance in Modelling the Pandemic (RAMP). 2020. Available from: https://sites.google.com/view/rampvis [Accessed: May 13, 2023]

4. Chen M, Abdul-Rahman A, Archambault D, Dykes J, Ritsos P, Slingsby A, et al. RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses. Epidemics. 2022;39:100569. DOI: 10.1016/j.epidem.2022.100569 [Accessed: June 2, 2022]

5. Ackland GJ, Panovska-Griffiths J, Waites W, Cates ME. The royal society RAMP modelling initiative. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2022;380:20210316. DOI: 10.1098/rsta.2021.0316 [Accessed: May 30, 2023]

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3