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.

翻译：理解错误信息如何影响疾病传播对公共卫生至关重要，尤其是近期研究表明错误信息可能加剧疫苗犹豫并抑制疫苗接种率。然而，由于缺乏数据驱动的整体流行病模型，研究错误信息与疫情结果之间的相互作用仍存在困难。本文提出了一种流行病模型，该模型融合了基于人口流动的大规模物理接触网络，以及通过社交媒体数据获取的各县错误信息人群分布。该模型使我们能够模拟并评估多种场景，从而理解错误信息对流行病传播的影响。应用该模型，我们估算在最坏情况下，错误信息可能导致美国新增4700万例COVID-19感染病例。