DeVerna Matthew R, Pierri Francesco, Ahn Yong-Yeol, Fortunato Santo, Flammini Alessandro, Menczer Filippo
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN USA.
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Npj Complex. 2025;2(1):11. doi: 10.1038/s44260-025-00038-y. Epub 2025 Apr 2.
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.
了解错误信息如何影响疾病传播对公共卫生至关重要，特别是考虑到最近的研究表明错误信息会增加疫苗犹豫情绪并阻碍疫苗接种。然而，由于缺乏基于数据的整体疫情模型，很难研究错误信息与疫情结果之间的相互作用。在此，我们采用一种疫情模型，该模型纳入了一个基于流动性的大型物理接触网络，以及从社交媒体数据得出的各县错误信息传播者的分布情况。该模型使我们能够模拟各种情景，以了解通过一个特定社交媒体平台传播的错误信息如何影响疫情传播。使用这个模型，我们将一个最坏情况情景（即个体在单次接触低可信度内容后就被误导）与一个最佳情况情景（即人群对错误信息具有高度抵抗力）进行比较。我们估计在最坏情况情景下，美国人口在新冠疫情期间会额外感染的比例。这项工作可以为政策制定者提供有关接触在线疫苗错误信息潜在危害的见解。
