Modeling the amplification of epidemic spread by misinformed populations

• URL: http://arxiv.org/abs/2402.11351v3
• Date: Tue, 30 Jul 2024 17:24:26 GMT
• Title: Modeling the amplification of epidemic spread by misinformed populations
• Authors: Matthew R. DeVerna, Francesco Pierri, Yong-Yeol Ahn, Santo Fortunato, Alessandro Flammini, Filippo Menczer,
• Abstract summary: 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. 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.
• Score: 41.31724592098777
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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