Chen Jie, Zhang Yantao, Hu Maobin, Li Ming, Chen Fulong
Anhui Normal University, School of Computer and Information, Wuhu 241003, People's Republic of China.
Wannan Medical College, School of Medical Information, Wuhu 241000, People's Republic of China.
Phys Rev E. 2025 Apr;111(4-1):044301. doi: 10.1103/PhysRevE.111.044301.
During epidemic outbreaks, information dissemination plays a pivotal role in shaping individual perceptions, which in turn influence contact behavior and resource acquisition, collectively determining infection risk. To capture this intricate interplay, we propose a comprehensive coevolutionary dynamics model that integrates information, awareness, activity, resources, and epidemic within a multiplex network framework. Through the development of a theoretical analysis coupled with extensive numerical verifications, we uncover the nonmonotonic effects of information dissemination on epidemic dynamics. Paradoxically, excessive information flow can intensify resource competition among individuals, leading to inefficient allocation and ultimately exacerbating the epidemic. Our findings highlight the importance of optimized resource allocation, showing that moderately prioritizing aware individuals with resources can effectively reduce infection rates, especially as information dissemination increases. Additionally, we explore the optimal balance between information dissemination and resource allocation, emphasizing its strong dependence on resource availability, while activity frequency experts a comparatively minor impact. This study advances the modeling of epidemic dynamics, providing valuable insights and practical strategies for effective epidemic management and control.
在疫情爆发期间,信息传播在塑造个人认知方面起着关键作用,而个人认知又会反过来影响接触行为和资源获取,共同决定感染风险。为了捕捉这种复杂的相互作用,我们提出了一个全面的协同进化动力学模型,该模型在多重网络框架内整合了信息、认知、活动、资源和疫情。通过开展理论分析并结合广泛的数值验证,我们揭示了信息传播对疫情动态的非单调影响。矛盾的是,过多的信息流会加剧个体之间的资源竞争,导致资源分配效率低下,最终加剧疫情。我们的研究结果凸显了优化资源分配的重要性,表明适度优先考虑有资源的有认知个体可以有效降低感染率,尤其是随着信息传播的增加。此外,我们探讨了信息传播与资源分配之间的最佳平衡,强调其对资源可用性的强烈依赖,而活动频率的影响相对较小。本研究推进了疫情动态建模,为有效的疫情管理和控制提供了有价值的见解和实用策略。