Coupled Dynamics of Partner Adjusting and Infectious Diseases on Complex Networks

Previous studies suggest that epidemic dynamics on networks can be largely altered when individuals can adjust their social partners. However, it remains unclear how the coupled dynamics of partner adjusting and infectious diseases behave if individuals cannot obtain accurate information on individuals beyond their neighborhoods. To address this issue, we propose a mini model of co-evolutionary SIS model in which individuals are allowed to switch their connections from infectious partners to any other ones in dependence on their densities besides the evolution of epidemic processes. Interestingly, we have found that the disease-free equilibrium is still stable for a considerably large and reasonable parameter regions even if the individuals have such limited cognitive abilities on the state of others except their neighbors. Besides, we have also shown that such a networked system also exhibits rich bifurcation behaviors such as hysteresis bifurcation, saddle-node bifurcation, and transcritical bifurcation. Our results may be helpful in understanding the impacts of adaptive human behavior in the propagation of infection diseases in the real world.