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.
