Integrating Cognitive Factors in Network Models of Epidemiology with Applications to Disease Control

Abstract

Understanding the interplay between information dissemination, behavioural responses, and disease dynamics remains a critical challenge in network-based epidemiological modelling. While network models offer a powerful framework for capturing individual-level interactions across both physical and virtual spaces, important knowledge gaps persist—particularly in how misinformation and behavioural adaptation jointly shape epidemic outcomes. This dissertation addresses these gaps by developing a novel three-layer network model that integrates information diffusion, cognitive processing, and epidemic transmission. In the first part, we show that protective behaviours driven by information-based decision-making are significantly more effective at suppressing disease spread than imitation--based strategies. We also find that educating and warning individuals to counter misinformation is more effective than network-based sanctions, such as suspending gossip spreaders. The second part explores the structural complexity of the information network, focusing on higher-order interactions represented through hyper-edge topologies. We demonstrate that scale-free information structures sustain prolonged and periodic waves of misinformation, in contrast to the more transient dynamics observed in small-world networks. In the third part, we extend our analysis to vaccination behaviour. Our results highlight the importance of timely misinformation correction in enhancing vaccine uptake and reducing disease burden. We also show that preemptive vaccination strategies significantly improve coverage and mitigate attack rates, even in environments saturated with disinformation. Notably, targeted vaccination approaches, which prioritise highly connected individuals (hubs), consistently outperform random strategies in reducing infections and severe disease outcomes. Together, this dissertation offers a comprehensive framework for examining how complex information-behaviour-epidemic feedbacks shape public health outcomes, and provides actionable insights for designing robust interventions against misinformation and infectious disease spread.