Impact of COVID-19 Forecast Visualizations on Pandemic Risk Perceptions

Abstract

Policy-makers and the general public have made decisions using COVID-19 data visualizations that have affected the health of the global population. However, the impact that such wide use of data visualizations has had on people's beliefs about their personal risk for COVID-19 is unclear. We conducted two experiments (N = 2,549) during the height of the COVID-19 epidemic in the United States to examine if real-time COVID-19 visualizations influenced participants' beliefs about the risk of the pandemic to themselves and others. This work also examined the impact of two elements of COVID-19 data visualizations, data properties (cumulative- vs. incident-death metrics) and uncertainty visualization techniques (historical data only, and forecasts with no uncertainty, vs. nine uncertainty visualization techniques). The results revealed that viewing COVID-19 visualizations with rising trends resulted in participants believing themselves and others at greater risk than before viewing the COVID-19 visualizations. Further, uncertainty visualization techniques that showed six or more models evoked the largest increases in risk estimates compared to the visualizations tested. These results could inform the design of public pandemic risk communication.