流行病与新闻传播过程相互作用的综合建模与分析
Integrative Modeling and Analysis of the Interplay Between Epidemic and News Propagation Processes
AI总结：
本文提出一个综合框架，将饱和分支过程新闻传播模型与SIRS流行病模型耦合为双时间尺度动力系统，揭示线性行为影响下周期性感染模式，并用Twitter和COVID-19数据重现历史感染曲线。
摘要：
COVID-19大流行见证了在线社交网络（OSNs）在传染病传播中的作用。流行病严重程度的上升增加了对适当指导方针的需求，同时也促进了虚假新闻条目的传播。新闻条目的流行程度可以重塑公共卫生行为并影响流行病过程。流行病过程与新闻条目传播之间存在明显的相互依赖关系。本工作创建了一个综合框架来理解这种相互作用。我们首先开发了一个依赖群体的“饱和分支过程”，以持续跟踪OSNs上热门新闻条目的传播。通过将新闻传播模型与SIRS流行病模型相结合，获得了一个双时间尺度动力系统，以分析整体系统。观察到在线性行为影响下会出现周期性感染模式，这解释了我们在大流行中经历的感染和再感染浪潮。我们使用数值实验来证实这些结果，并使用Twitter和COVID-19数据集，利用综合模型重现历史感染曲线。
英文摘要：
The COVID-19 pandemic has witnessed the role of online social networks (OSNs) in the spread of infectious diseases. The rise in severity of the epidemic augments the need for proper guidelines, but also promotes the propagation of fake news-items. The popularity of a news-item can reshape the public health behaviors and affect the epidemic processes. There is a clear inter-dependency between the epidemic process and the spreading of news-items. This work creates an integrative framework to understand the interplay. We first develop a population-dependent `saturated branching process' to continually track the propagation of trending news-items on OSNs. A two-time scale dynamical system is obtained by integrating the news-propagation model with SIRS epidemic model, to analyze the holistic system. It is observed that a pattern of periodic infections emerges under a linear behavioral influence, which explains the waves of infection and reinfection that we have experienced in the pandemic. We use numerical experiments to corroborate the results and use Twitter and COVID-19 data-sets to recreate the historical infection curve using the integrative model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
