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.

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，尤其是考虑到近期研究表明错误信息可能加剧疫苗犹豫并降低接种意愿。然而，由于缺乏基于数据的整体流行病模型，研究错误信息与疫情结果之间的相互作用十分困难。本文采用一种流行病模型，该模型整合了大规模、基于移动轨迹的物理接触网络，以及通过社交媒体数据得出的各县错误信息接触者分布。该模型使我们能够模拟多种情景，以理解通过特定社交媒体平台传播的错误信息如何影响疫情扩散。利用该模型，我们比较了两种极端情景：最坏情景（个体仅需接触一次低可信度内容即被误导）与最佳情景（群体对错误信息具有高度抵抗力）。我们估算了在COVID-19疫情期间，最坏情景下美国可能增加的感染人口比例。这项工作可为政策制定者提供关于接触网络疫苗错误信息潜在危害的参考依据。