Self-isolation vs frontiers closing: What prevents better of epidemic spread?.

We discuss combined effects of network clustering and adaptivity on epidemic spread. We address the question which mechanism is more effective for prohibiting disease propagation in a connected network: adaptive clustering, which mimics self-isolation (SI) in local communities, or sharp instant clustering, which looks like frontiers closing (FC) between cities and countries? Since in reality cross-community connections always survive, we can wonder how efficient is the excitation (illness) propagation through the entire clustered network which has some density of inter-cluster connections. Crucial difference between SI- and FC-networks is as follows: SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created" by \emph{ad hoc} imposed borders. We found that SI model has scale-free property for degree distribution $P(k)\sim k^{\eta}$ with surprisingly small critical exponent $-2<\eta<-1$. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing.