Solving the Dynamic Correlation Problem of the Susceptible-Infected-Susceptible Model on Networks

Abstract

The Susceptible-Infected-Susceptible model is a canonical model for emerging disease outbreaks. Such outbreaks are naturally modeled as taking place on networks. A theoretical challenge in network epidemiology is the dynamic correlations coming from that if one node is occupied, or infected (for disease spreading models), then its neighbors are likely to be occupied. By combining two theoretical approaches---the heterogeneous mean-field theory and the effective degree method---we are able to include these correlations in an analytical solution of the SIS model. We derive accurate expressions for the average prevalence (fraction of infected) and epidemic threshold. We also discuss how to generalize the approach to a larger class of stochastic population models.

arXiv comment24 pages, many figure. Some relevant notes and remarks are added

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Cited by in corpus (27)

• Percolation on complex networks: Theory and application
• Coevolution spreading in complex networks
• Effective approach to epidemic containment using link equations in complex networks
• The impact of nodes of information dissemination on epidemic spreading in dynamic multiplex networks
• Metastable Localization of Diseases in Complex Networks
• Optimal Allocation of Resources for Suppressing Epidemic Spreading on Networks
• Robustness and fragility of the susceptible-infected-susceptible epidemic models on complex networks
• Contact-based model for epidemic spreading on temporal networks
• An SIS epidemic model with vaccination in a dynamical contact network of mobile individuals with heterogeneous spatial constraints
• Concurrency measures in the era of temporal network epidemiology: A review
• An overview of epidemic models with phase transitions to absorbing states running on top of complex networks
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• Quenched mean-field theory for the majority-vote model on complex networks
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