RAPID: Visualizing Epidemical Uncertainty for Personal Risk Assessment

新冠肺炎是现代史上最致命、传播最快的病毒之一。为了应对这场大流行，新闻机构、政府组织、公民科学家和其他许多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是至关重要的，但尚不清楚普通人如何理解广泛分布的大流行数据描述。先前对不确定性交流的研究表明，即使是常见的可视化也可能会令人困惑。对新冠肺炎做出不当反应的一个可能来源是缺乏对个人风险和大流行不确定性性质的了解。这项研究的目标是测试人们如何理解当前可用的新冠肺炎数据可视化，并基于这些发现制定沟通指南。此外，研究人员还开发了一个应用程序，帮助人们了解导致他们风险的因素。用户能够与应用程序交互，以了解其操作对其风险的影响。这项研究为教育人们了解他们与新冠肺炎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立竿见影的解决方案，从而可以改善公众的反应，降低死亡人数。此外，这项工作还支持对未来的大流行以及随后爆发的任何新冠肺炎或其他病毒的决策。具体地说，研究团队通过测试当前可用的可视化对个人风险判断和行为的影响，经验地检查了高影响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，这项研究有助于理解人们如何从不同的来源(例如，与地点、时间、人口统计和风险行为相关的不确定性)概念化复合不确定性。然后，研究人员利用这些信息制作一个可视化应用程序，允许人们更改模拟的参数，以了解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在他们的邮政编码中看到他们年龄段个人的大流行风险，然后看到如果感染率增加或减少，风险将如何变化。其目的是通过参与者对应用程序的试验，促进对预测中流行病学不确定性的直观理解。虽然这项工作与当前关于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NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.