Visualization Lessons from the COVID-19 Pandemic

Data visualizations are a key component of science communication with the public. The COVID-19 pandemic allowed researchers to test findings from experimental psychology in a real-world context with important implications for public health. Some of these examples include exponential growth bias in the context of judgmental forecasting, stock-flow reasoning errors when reasoning about accumulation functions (i.e., cumulative curves), and examining how visualizations like icon arrays could be used to reduce vaccine hesitancy. In this review we synthesize the empirical studies on people’s understanding of COVID visualizations and how COVID visualizations were used by the media. The studies reviewed overwhelmingly suggest that findings in the research on risk-perception and visualizations apply to real-life judgment and decision-making. We discuss the findings from these studies in the context of best practices for creating data visualizations.