demirel2017dynamics
Dynamics of epidemic diseases on a growing adaptive network
The structure of social contact networks strongly influences the dynamics of epidemic diseases. In particular the scale-free structure of real-world social networks allows unlikely diseases with low infection rates to spread and become endemic. However, in particular for potentially fatal diseases, also the impact of the disease on the social structure cannot be neglected, leading to a complex interplay. Here, we consider the growth of a network by preferential attachment from which nodes are simultaneously removed due to an SIR epidemic. We show that increased infectiousness increases the prevalence of the disease and simultaneously causes a transition from scale-free to exponential topology. Although a transition to a degree distribution with finite variance takes place, the network still exhibits no epidemic threshold in the thermodynamic limit. We illustrate these results using agent-based simulations and analytically tractable approximation schemes.
![The disease prevalence increases monotonically with infectiousness p and fraction of infected arrivals w. In agent-based simulations (circles), \([I]^*\) is calculated over the surviving runs among \(10^3\) total realizations. Homogeneous approximation (solid lines) is the analytical solution of Eq. (8). Heterogeneous approximation (dashed lines) is the stationary value of the numerical integration of Eq. (3).](https://biond.org/files/upload/figure/ZCS6Rx7blMAv.jpg)
Figure 1: The disease prevalence increases monotonically with infectiousness p and fraction of infected arrivals w. In agent-based simulations (circles), \([I]^*\) is calculated over the surviving runs among \(10^3\) total realizations. Homogeneous approximation (solid lines) is the analytical solution of Eq. (8). Heterogeneous approximation (dashed lines) is the stationary value of the numerical integration of Eq. (3).