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

RAMP VIS：使可视化分析成为抗击 COVID-19 技术基础设施的组成部分

基本信息

批准号：
EP/V054236/1
负责人：
Min Chen
金额：
$ 54.85万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV054236%2F1
关键词：
RAMP VIS Making Visual Analytics

项目摘要

Computational modelling of the COVID-19 pandemic has been playing a significant role in the UK's effort to combat COVID-19. Across the country, there are about 100 research teams working on different models, and several dozens have provided simulation, estimation, and prediction to inform the governmental decisions in the four home nations. One noticeable oversight is the under-utilisation of visualization and visual analytics technology in supporting the scientific workflows for model development, which typically consists of a set of iterative processes, such as (a) hypothesis formulation and causality analysis; (b) model development, testing, validation, and comparison; (c) model deployment, monitoring, and improvement; and (d) scientific and public dissemination.Because visualization is widely mistaken only for information or knowledge dissemination, the technology is commonly underused in all other stages of a modelling workflow. Ideally, modelling scientists and epidemiologists could have a quick glance of dynamic data anytime there is a need (cf. stock brokers observing stock market data), access effective overviews of spatiotemporal patterns of the disease development and control (cf., meteorologists observing satellite images, contour maps, etc.), be provided with external memorization of data to stimulate hypotheses and contemplate various decisions (cf. a general pacing around in a war room in front of many maps), and receive advice from an ensemble of analytical algorithms and visualizations about similarity, anomalies, clusters, correlation, causality, and association hidden in the data (cf. a CEO consulting specialists). Ideally, there is a visual analytics infrastructure, as a "standing capacity" (Secretary of State), that can support many modelling teams performing daily observational, analytical, and model-developmental tasks.The proposed VA technical and knowledge infrastructures are essential for combating COVID-19 pandemic, as many epidemiologists are preparing for COVID-10 to be a threat for some time. With the recent introduction of localised control measures, it indicates an additional need for localised VA supports for many local scientists and healthcare professionals. When vaccination starts, there is a need for monitoring and modelling the effectiveness of different vaccines used in different regions. Such a nationwide need can be cost-effectively delivered by the VA technical and knowledge infrastructures.

COVID-19大流行的计算建模在英国打击Covid-19的努力中发挥了重要作用。在全国范围内，大约有100个研究团队从事不同的模型，几十个提供了模拟，估算和预测，以告知四个家庭国家的政府决策。一个明显的监督是在支持模型开发的科学工作流程中对可视化和视觉分析技术的利用不足，通常由一组迭代过程组成，例如（a）假设表述和因果关系分析；（b）模型开发，测试，验证和比较；（c）模型部署，监视和改进；（d）科学和公共传播。由于可视化仅因信息或知识传播而被广泛误认为，因此在建模工作流的所有其他阶段，该技术通常都没有用。理想情况下，对科学家和流行病学家进行建模时，任何需要时都可以快速了解动态数据（参见股票经纪人观察股票市场数据），访问疾病发展和控制的时空模式的有效概述（CF.，CF，CF，CF，CF，气象学家，与卫星图像，刺激图像相同的刺激性图像，均为模因的刺激性图像，等等。（请参阅在许多地图前的战争室里周围的一般步调），并从分析算法和可视化的集合中收到有关相似性，异常，集群，相关性，因果关系和关联的可视化，并隐藏在数据中（请参阅首席执行官咨询专家）。理想情况下，作为“站立能力”（国务卿）有一个视觉分析基础架构，可以支持许多建模团队进行日常观察，分析和模型开发任务。拟议的VA技术和知识基础架构对于与许多流行病学作品相比，与一定的时代相比，与一定的时代相比，必须使用covid-forevid-forevid-forevid-forevid-forevid-forevid-forevid-forevid-forevid forvi forvi forevid-forevid forvi forevid forvi forevid for a in for a in fore forevide是至关重要的。随着局部控制措施的最新引入，它表明了许多本地科学家和医疗保健专业人员的局部VA支持需求。当疫苗接种开始时，需要监测和建模不同区域中使用的不同疫苗的有效性。在全国范围内，可以通过VA技术和知识基础设施来实现成本效益。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

DOI：
10.1098/rsta.2021.0299
发表时间：
2022-10-03
期刊：
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
影响因子：
5
作者：
Dykes, Jason;Abdul-Rahman, Alfie;Archambault, Daniel;Bach, Benjamin;Borgo, Rita;Chen, Min;Enright, Jessica;Fang, Hui;Firat, Elif E.;Freeman, Euan;Gonen, Tuna;Harris, Claire;Jianu, Radu;John, Nigel W.;Khan, Saiful;Lahiff, Andrew;Laramee, Robert S.;Matthews, Louise;Mohr, Sibylle;Nguyen, Phong H.;Rahat, Alma A. M.;Reeve, Richard;Ritsos, Panagiotis D.;Roberts, Jonathan C.;Slingsby, Aidan;Swallow, Ben;Torsney-Weir, Thomas;Turkay, Cagatay;Turner, Robert;Vidal, Franck P.;Wang, Qiru;Wood, Jo;Xu, Kai
通讯作者：
Xu, Kai

Dashboard Design Patterns

仪表板设计模式

DOI：
10.48550/arxiv.2205.00757
发表时间：
2022
期刊：
影响因子：
0
作者：
Bach B
通讯作者：
Bach B

PCP-Ed: Parallel coordinate plots for ensemble data

PCP-Ed：集合数据的平行坐标图

DOI：
10.1016/j.visinf.2022.10.003
发表时间：
2023
期刊：
Visual Informatics
影响因子：
3
作者：
Firat E
通讯作者：
Firat E

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

基于可视化分析的流行病学时间序列数据搜索分析预测框架

DOI：
10.1109/vis4pandemres60343.2023.00006
发表时间：
2023
期刊：
影响因子：
0
作者：
Gonen T
通讯作者：
Gonen T

From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children

DOI：
10.1145/3544548.3581573
发表时间：
2023-04
期刊：
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Graham Mcneill;Max Sondag;Stewart Powell;P. Asplin;C. Turkay;Faron Moller;D. Archambault
通讯作者：
Graham Mcneill;Max Sondag;Stewart Powell;P. Asplin;C. Turkay;Faron Moller;D. Archambault