Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness

The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.