Nontrivial epidemic dynamics induced by information-driven awareness-activity-resource coevolution
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.
Authors
- Jie Chen (ORCID: https://orcid.org/0000-0002-4642-5464)
- Mao-Bin Hu (ORCID: https://orcid.org/0000-0001-5903-1678)
- Ming Li (ORCID: https://orcid.org/0000-0002-8545-0726)
- Fulong Chen (ORCID: https://orcid.org/0000-0003-0557-5084)
- Zhang Yan-tao (ORCID: https://orcid.org/0000-0003-3642-0587)
Institutions
- Wannan Medical College (CN)
- University of Science and Technology of China (CN)
- Hefei University of Technology (CN)
- Anhui Normal University (CN)
Publication Details
- Journal
- Physical review. E
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1103/physreve.111.044301
- Citations
- 6
- Primary Topic
- Complex Network Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 7.94
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation