RAPID: Visualizing Epidemical Uncertainty for Personal Risk Assessment

RAPID：可视化流行病不确定性以进行个人风险评估

• 批准号: 2235625
• 负责人: Enrico Bertini
• 金额: $ 19.17万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235625&HistoricalAwards=false
• 关键词: RAPID; Visualizing; Epidemical; Uncertainty; Personal

COVID-19 is one of the most deadly and fastest transmitting viruses in modern history. In response to this pandemic, news agencies, government organizations, citizen scientists, and many others have released hundreds of visualizations of pandemic forecast data. While providing people with accurate information is essential, it is unclear how the average person understands the widely distributed depictions of pandemic data. Prior research on uncertainty communication shows that even common visualizations can be confusing. One possible source of inappropriate responses to COVID-19 is the lack of knowledge about personal risk and the nature of pandemic uncertainty. The goal of this research is to test how people understand currently available COVID-19 data visualizations and create communication guidelines based on these findings. Further, the researchers develop an application to help people understand the factors that contribute to their risk. Users are able to interact with the application to learn about the impact of their actions on their risk. This research provides immediate solutions for teaching people about their personal risk associated with COVID-19 and how their actions influence the risks of others, which could improve the public's response and decrease fatalities. Additionally, this work supports decision making for future pandemics and any subsequent outbreaks of COVID-19 or other viruses.Specifically, the research team empirically examines how people in high and low impact regions reason with pandemic uncertainty by testing the effects of currently available visualizations on personal risk judgments and behavior. By studying how changes in factors influence risk perceptions, the research can contribute to understanding how people conceptualize compound uncertainties from different sources (e.g., uncertainties associated with location, time, demographics and risk behaviors). The researcher then use this information to produce a visualization application that allows people to change the parameters of a simulation to see how the resulting changes affect their risk judgments. For example, users in one city are able to see the pandemic risk to individuals of their age in their zip code and then see how that risk would change if the infection rate increased or decreased. The aim is to promote intuitive understanding of the epidemiological uncertainty in the forecast through participants’ experimentation with the application. While in line with current recommendations for intrinsic uncertainty visualization, this work is the first of its kind to test the effect of user interaction to convey uncertainty through visualization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COVID-19是现代历史上最致命、传播最快的病毒之一。为了应对这次大流行，新闻机构、政府组织、公民科学家和许多其他人发布了数百个大流行预测数据的可视化。虽然向人们提供准确的信息至关重要，但尚不清楚一般人如何理解大流行数据的广泛分布描述。先前对不确定性通信的研究表明，即使是常见的可视化也可能令人困惑。对COVID-19应对不当的一个可能原因是缺乏对个人风险和大流行不确定性性质的了解。这项研究的目的是测试人们如何理解目前可用的COVID-19数据可视化，并根据这些发现创建沟通指南。此外，研究人员开发了一个应用程序，帮助人们了解导致他们患病的因素。用户能够与应用程序进行交互，以了解他们的操作对风险的影响。这项研究为教育人们了解与COVID-19相关的个人风险以及他们的行为如何影响他人的风险提供了直接的解决方案，这可以改善公众的反应并减少死亡人数。此外，这项工作还为未来的大流行以及随后的COVID-19或其他病毒爆发提供决策支持。具体而言，研究小组通过测试目前可用的可视化对个人风险判断和行为的影响，实证研究了高影响地区和低影响地区的人们如何对大流行的不确定性进行推理。通过研究因素的变化如何影响风险感知，本研究有助于理解人们如何概念化来自不同来源的复合不确定性（例如，与地点、时间、人口统计和风险行为相关的不确定性）。然后，研究人员利用这些信息制作了一个可视化应用程序，允许人们改变模拟的参数，看看由此产生的变化如何影响他们的风险判断。例如，一个城市的用户能够看到其邮政编码所在年龄段的个人面临的大流行风险，然后看到感染率上升或下降时这种风险将如何变化。目的是通过参与者对应用程序的实验，促进对预测中流行病学不确定性的直观理解。虽然与当前对内在不确定性可视化的建议一致，但这项工作是第一次测试用户交互通过可视化传达不确定性的效果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts

标记的排列影响所提供的消息：条形图中的排序、分区、间距和着色

• DOI: 10.1109/tvcg.2023.3326590
• 发表时间: 2023
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Fygenson, Racquel;Franconeri, Steven;Bertini, Enrico
• 通讯作者: Bertini, Enrico

Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations

多重预测可视化 (MFV)：多重 COVID-19 预测可视化中信任与性能的权衡

• DOI: 10.1109/tvcg.2022.3209457
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Padilla, Lace;Fygenson, Racquel;Castro, Spencer C.;Bertini, Enrico
• 通讯作者: Bertini, Enrico