RAPID: COVID-19 Information Visualizations

新冠肺炎颠覆了全球的日常生活。政府领导人、医学界和媒体正在通过描述流行病模型的预测来传达各种公共卫生措施的影响，例如社会距离。社交媒体上充斥着为帮助传达这些措施的需求而创建的可视化画面。人们的日常决策，以及他们对公共卫生政策的支持，将取决于他们对新冠肺炎疫情的理解。这项研究确定了向公众传达新冠肺炎风险数据并帮助人们了解不同行为和政策的潜在影响的最佳方式。公众对什么行为是安全的有很多疑问。如果结果表明模拟可以帮助将信息传达给公众，那么在人们驾驭围绕新冠肺炎的不确定性时，围绕人们提出的特定问题进行的模拟将是一个有价值的工具。这些模拟可以向公众开放，也可以与新闻媒体分享。人们的日常决策，以及他们对公共卫生政策的支持，将取决于他们对新冠肺炎疫情的理解。不幸的是，缺乏理解导致人们声称，公共卫生官员的可怕警告只是吓人的宣传策略。总体而言，对假设数据和结果的不确定模拟存在根本性的误解和不信任。目前的项目开发了用于传达重要的与风险相关的COVID流行病学模型的可视化，以支持对基于科学的预测和建议的理解和信任，并改进与COVID相关的决策。这项研究测试了提出的主要可视化设计特征，以评估它们在当前大流行中的价值。学者们还确定了个体差异因素(算术能力、对科学的信任和当前的焦虑水平)对不同可视化设计特征在理解个人和全球风险模型、信任、宏观(一般行为，如社交距离)和微观水平(购物时戴口罩)新冠肺炎决策的有效性的影响。这项拟议的研究测试了关键认知原则在现实生活中对形象化的概括能力。虽然之前的研究在人工环境中独立地考虑了这些因素，但有限的工作解决了这些因素如何相互作用，以及这些因素如何不仅影响理解，还影响信任和行为意图。如果在这些人工背景下制定的原则不能推广到COVID，这将需要修订风险可视化指南。因此，这项工作的智力影响是提高我们对如何向具有不同背景和先前信仰的个人传达复杂风险模型的理解。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 has upended daily life across the globe. Government leaders, medical professions, and the media are communicating the impact of various public health measures such as social distancing by describing predictions of epidemiological models. Social media has been inundated with visualizations that have been created to help communicate the need for these measures. People’s everyday decisions, as well as their support of public health policy, will depend on their understanding of the COVID-19 pandemic. The research identifies the best way to communicate COVID-19 risk data to the public and to help people understand the potential impacts of different behaviors and policies. The public has many questions about what behaviors are safe. If the results show that simulations can help convey the information to the public, simulations that center on specific questions people are asking will be a valuable tool as people navigate the uncertainty surrounding COVID-19. The simulations are available to the general public and shared with the news media. People’s everyday decisions, as well as their support of public health policy, will depend on their understanding of the COVID-19 pandemic. Unfortunately, lack of understanding has led to claims that public health officials’ dire warnings are merely scare tactics of propaganda. In general, there is a fundamental misunderstanding and distrust in uncertain simulations of hypothetical data and outcomes. The current project develops visualizations for communicating important risk-related COVID epidemiological models to support comprehension and trust in science-based forecasts and recommendations and improving COVID-related decision making. The research tests key proposed visualization design features to assess their value in the current pandemic. The scholars also determine the influence of individual difference factors (numeracy, trust in science, and current anxiety levels) on the effectiveness of different visualization design features on comprehension of personal and global risk models, trust, and macro- (general actions such as social distancing) and micro-level (using a face mask while shopping) COVID-19 decisions asked before and after experience with the visualizations. The proposed research tests the generalizability of key cognitive principles to visualizations in a real-life context. While prior research has independently considered these factors in artificial contexts, limited work has addressed how these factors interact with each other, and also how the factors influence not only comprehension but also trust and behavioral intentions. If principles developed in these artificial contexts do not generalize to COVID, this would necessitate revision of risk visualization guidelines. Thus, the intellectual impact of this work is to improve our understanding of how to communicate complex risk models to individuals with varying backgrounds and prior beliefs.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.