标题： 推文与病原体传播：美国各州新冠疫情案例研究 显示英文标题

标题： Tweets vs Pathogen Spread: A Case Study of COVID-19 in American States

摘要： 意识和疾病之间相互影响的概念最近带来了重大挑战。 个人为防止感染疾病所采取的行动及其意识水平会深刻影响其传播动力学。 同时，疾病爆发会影响人们的意识形成。 作为回应，我们首先提出一个将两种易感-感染-恢复（SIR）动力学耦合的零模型，并使用平均场方法对其进行分析。 随后，我们探索参数空间，以量化这种相互影响对各种可观测量的影响。 最后，基于这个零模型，我们对与美国各州的 COVID-19 相关的推特数据和确诊案例进行了实证分析。 我们的研究结果表明，在参数空间的特定区域，可以通过提高意识来抑制流行病，并我们研究了相变。 此外，我们的模型展示了在疫情过程中通过调整参数来改变主导人口群体的能力。 此外，使用该模型，我们为每个州分配一组参数，揭示了这些参数在不同疫情高峰时发生变化。 值得注意的是，从实证数据中收集的各州推特活动排名与我们模型为每个州分配的免疫参数之间出现了一个强相关性。 这一观察结果强调了持续意识在疾病发展初期到后续高峰之间过渡的关键作用。 显示英文摘要

摘要： The concept of the mutual influence that awareness and disease may exert on each other has recently presented significant challenges. The actions individuals take to prevent contracting a disease and their level of awareness can profoundly affect the dynamics of its spread. Simultaneously, disease outbreaks impact how people become aware. In response, we initially propose a null model that couples two Susceptible-Infectious-Recovered (SIR) dynamics and analyze it using a mean-field approach. Subsequently, we explore the parameter space to quantify the effects of this mutual influence on various observables. Finally, based on this null model, we conduct an empirical analysis of Twitter data related to COVID-19 and confirmed cases within American states. Our findings indicate that in specific regions of the parameter space, it is possible to suppress the epidemic by increasing awareness, and we investigate phase transitions. Furthermore, our model demonstrates the ability to alter the dominant population group by adjusting parameters throughout the course of the outbreak. Additionally, using the model, we assign a set of parameters to each state, revealing that these parameters change at different pandemic peaks. Notably, a robust correlation emerges between the ranking of states' Twitter activity, as gathered from empirical data, and the immunity parameters assigned to each state using our model. This observation underscores the pivotal role of sustained awareness transitioning from the initial to the subsequent peaks in the disease progression.