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，尤其鉴于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意愿。然而，由于缺乏数据驱动的整体性流行病模型，探究错误信息与疫情结果之间的相互作用存在困难。本文提出一种融合大规模移动性物理接触网络与基于社交媒体数据构建的县级错误信息分布特征的流行病模型。该模型可模拟并评估多种情景，以揭示错误信息对疫情传播的影响。通过该模型估算，在最坏情景下错误信息可能导致美国新增4700万例COVID-19感染病例。