Name
Abstract | ||
|---|---|---|
Even though transitivity is a central structural feature of social networks, its influence on epidemic spread on coevolving networks has remained relatively unexplored. Here we introduce and study an adaptive susceptibleinfected-susceptible (SIS) epidemic model wherein the infection and network coevolve with nontrivial probability to close triangles during edge rewiring, leading to substantial reinforcement of network transitivity. This model provides an opportunity to study the role of transitivity in altering the SIS dynamics on a coevolving network. Using numerical simulations and approximate master equations (AMEs), we identify and examine a rich set of dynamical features in the model. In many cases, AMEs including transitivity reinforcement provide accurate predictions of stationary-state disease prevalence and network degree distributions. Furthermore, for some parameter settings, the AMEs accurately trace the temporal evolution of the system. We show that higher transitivity reinforcement in the model leads to lower levels of infective individuals in the population, when closing a triangle is the dominant rewiring mechanism. These methods and results may be useful in developing ideas and modeling strategies for controlling SIS-type epidemics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1103/PhysRevE.99.062301 | PHYSICAL REVIEW E |
Field | DocType | Volume |
Population,Epidemic model,Social network,Theoretical computer science,Artificial intelligence,Cluster analysis,Master equation,Machine learning,Mathematics,Transitive relation | Journal | 99 |
Issue | ISSN | Citations |
6 | 2470-0045 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsuan-Wei Lee | 1 | 0 | 0.34 |
| Nishant Malik | 2 | 8 | 2.17 |
| Feng Shi | 3 | 19 | 4.15 |
| Peter J. Mucha | 4 | 909 | 64.78 |