Epidemic dynamics driven by spatio-temporal heterogeneity of individual decision-making behaviour

Heterogeneity is widespread in the evolution of individual behaviour and would thereby significantly influence the course of disease transmission. To further understand coupled behaviour-disease dynamics, we adopt the Microscopic Markov chain approach to establish an epidemic model on multiplex networks containing behaviour and contact layers. The behaviour layer employs a time-varying topology constructed from activity-driven methods and a diffusion mechanism under the evolutionary game framework, aiming to characterise the heterogeneous nature of individuals’ decision-making behaviours at both the temporal and spatial levels in the face of government responses during an epidemic. By comparing the two models in the evolutionary game and homogeneous diffusion scenarios, the results show that the higher the government response strength, the superiority of the evolutionary game mechanism in controlling outbreaks becomes more obvious. Moreover, we discover that there is a mutual reinforcement between different epidemic prevention initiatives, which will open up new possibilities for achieving outbreak containment with less effort. The accuracy and practicality of the proposed model are validated by real-world network data. Our results have made new progress in clarifying the interaction between behavioural heterogeneity and disease prevalence, which is an important theoretical guide for the formulation of epidemic control policies.