Examining Heterogeneity in Information Diffusion and Its Impact on Disease Spread in a Multilayer Network

Congjie Shi (), Silvio C. Ferreira (), Hugo P. Maia () and Seyed M. Moghadas ()
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Congjie Shi: York University, Agent-Based Modelling Laboratory
Silvio C. Ferreira: Universidade Federal de Viçosa, Departamento de Física
Hugo P. Maia: Universidade Federal de Viçosa, Departamento de Física
Seyed M. Moghadas: York University, Agent-Based Modelling Laboratory

A chapter in Trends in Biomathematics: Modeling Health Across Ecology, Social Interactions, and Cells, 2025, pp 259-273 from Springer

Abstract: Abstract Complex networks effectively capture contact heterogeneity, making them valuable for modelling physical and virtual connections, such as disease transmission, information dissemination, and behavioural patterns. While prior research has investigated the direct impact of information flow on disease spread, the intricate interactions between information, behavioural responses, and disease dynamics remain underexplored. This chapter introduces a three-layer network framework—information, cognition, and epidemics (ICE)—to study the effects of heterogeneities on information diffusion, protection decision-making, and their subsequent influence on disease transmission. We focus on the role of network structure in higher-order interactions within the information layer. Our findings reveal that misinformation originating from low-degree nodes leads to only slightly higher adoption of protective measures. Moreover, hyper-edge group topologies accelerate misinformation spread from smaller to larger groups, exerting a greater influence than individual gossip sources. Scale-free structures in the information layer produce prolonged and periodic peaks in gossip spreading compared to small-world structures, directly affecting epidemic incidence. Finally, informed knowledge-based decision-making is more effective in mitigating disease spread than imitative behaviours, particularly in heterogeneous information networks where the hyper-edge group sizes follow a power-law distribution.

Keywords: Epidemic dynamics; Hyper-edge networks; Information diffusion; Behavioural responses (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-97461-8_14

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