Self-isolation or borders closing: what prevents epidemic spreading better?

Abstract

Pandemic distribution of COVID-19 in the world has motivated us to discuss combined effects of network clustering and adaptivity on epidemic spreading. We address the question concerning the choice of optimal mechanism for most effective 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. SI-networks are "adaptively grown" under condition of maximization of small cliques in the entire network, while FC-networks are "instantly created". Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that the adaptive network clustering prohibits the epidemic spreading better than the instant clustering in the network with similar parameters. We found that SI model has scale-free property for degree distribution $P(k)\sim k^{\eta }$ with small critical exponent $-2<\eta <-1$ and argue that scale-free behavior emerges due to the randomness in the initial degree distributions and is absent for random regular graphs.

arXiv comment6 pages, 4 figures

References in corpus (19)

• Community detection in graphs
• Epidemic spreading in scale-free networks
• Epidemic processes in complex networks
• Specificity and stability in topology of protein networks
• Epidemics and percolation in small-world networks
• Epidemic dynamics on an adaptive network
• Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
• Localization from Hilbert space shattering: from theory to physical realizations
• Spectra of "Real-World" Graphs: Beyond the Semi-Circle Law
• Local constraints can globally shatter Hilbert space: a new route to quantum information protection
• Ergodicity-breaking arising from Hilbert space fragmentation in dipole-conserving Hamiltonians
• Spectra and eigenvectors of scale-free networks
• Percolation and Epidemic Thresholds in Clustered Networks
• Scale-free networks with an exponent less than two
• Eigenvalue tunnelling and decay of quenched random networks
• Scale-free networks with exponent one
• Predicting the ultimate outcome of the COVID-19 outbreak in Italy
• Phase transitions in social networks inspired by the Schelling model
• Islands of stability in motif distributions of random networks

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