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" byad 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 .
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 .
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Pandemic distribution of COVID-19 in the world has motivated us to
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spread
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which mechanism is more effective for
concerning the choice of optimal mechanism for most effective
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? 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:
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by
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ad hoc
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imposed borders.
. 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.
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surprisingly
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-1. Running the standard SIR model on clustered SI- and FC-networks, we demonstrate that
-1 and argue that scale-free behavior emerges due to the randomness in
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adaptive network clustering caused by self-isolation in communities prohibits the epidemic spread better than the clustering due to instant boundaries closing
initial degree distributions and is absent for random regular graphs