Perceiving a pandemic: global-local incompatibility and COVID-19 superspreading events
Superspreading events are the primary mode of infection driving the COVID-19 pandemic, but their effect on risk judgments is currently unknown. More than half a million people in the U.S. died from COVID-19 in one year, yet public risk perceptions of infection and mortality remain variable. Using a combination of epidemiological models and the psychological theory of global-local incompatibility, we theorize that superspreading diseases create a large variance in infections across geographic localities, leading to highly variable and inaccurate risk perceptions. This is problematic because these local infection rates fail to reveal the overall severity of the pandemic, which determines the personal risk of infection at any location in the near future. We test our predictions with a simulation study and a nationally representative study of U.S. citizens (N=3956) conducted in April 2020. Supporting our theory, we find that localized county-level infection rates of COVID-19 are unreliable predictors of national infection rates. However, they explain a significant proportion of variance in judgments of national infection rates, contributing to judgment errors. These results support our theoretical approach for modeling this unique judgment context as an incompatibility between global and local information, providing a framework to predict how citizens will react to novel large scale (global) risks. Our results also help explain the extreme polarization witnessed in the U.S. regarding perceptions of the risks of the COVID-19 pandemic. Accounting for the variability of local experiences with a pandemic can help future generations prepare for how to respond to similar threats more effectively.