  Understanding how misinformation affects the spread of disease is crucial for
public health, especially given recent research indicating that misinformation
can increase vaccine hesitancy and discourage vaccine uptake. However, it is
difficult to investigate the interaction between misinformation and epidemic
outcomes due to the dearth of data-informed holistic epidemic models. Here, we
employ an epidemic model that incorporates a large, mobility-informed physical
contact network as well as the distribution of misinformed individuals across
counties derived from social media data. The model allows us to simulate and
estimate various scenarios to understand the impact of misinformation on
epidemic spreading. Using this model, we present a worst-case scenario in which
a heavily misinformed population would result in an additional 14% of the U.S.
population becoming infected over the course of the COVID-19 epidemic, compared
to a best-case scenario.
