Modeling the amplification of epidemic spread by misinformed populations

• URL: http://arxiv.org/abs/2402.11351v2
• Date: Sat, 9 Mar 2024 16:52:54 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 propose an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties. Our model allows us to simulate and estimate various scenarios to understand the impact of misinformation on epidemic spreading. Using this model, we estimate that misinformation could have led to 47 million additional COVID-19 infections in the U.S. in a worst-case scenario.
• Score: 43.389128992784045
• 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 propose 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. Our model allows us to simulate and estimate various scenarios to understand the impact of misinformation on epidemic spreading. Using this model, we estimate that misinformation could have led to 47 million additional COVID-19 infections in the U.S. in a worst-case scenario.

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