Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media

TL;DR

This paper develops a model integrating social media misinformation spread with epidemic dynamics to assess its impact on disease transmission, providing insights for public health policy.

Contribution

It introduces a holistic epidemic model that incorporates social media misinformation and mobility data to simulate its effects on disease spread.

Findings

Misinformation can significantly increase infection rates.

Resilience to misinformation reduces epidemic impact.

Estimated additional infections in worst-case misinformation scenario.

Abstract

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 various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.