Epidemic Incidence in Correlated Complex Networks
Yamir Moreno, Javier B. Gomez, Amalio F. Pacheco
Abstract
We introduce a numerical method to solve epidemic models on the underlying topology of complex networks. The approach exploits the mean-field like rate equations describing the system and allows to work with very large system sizes, where Monte Carlo simulations are useless due to memory needs. We then study the SIR epidemiological model on assortative networks, providing numerical evidence of the absence of epidemic thresholds. Besides, the time profiles of the populations are analyzed. Finally, we stress that the present method would allow to solve arbitrary epidemic-like models provided that they can be described by mean-field rate equations.
Create a lesson
Related papers
Towards synergy between theory and experiments: First-passage processes under resetting with stochastic return protocols
Ashutosh Dubey, Arnab Pal
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks
Ali Rayat, Gia-Wei Chern
Extending the mean first-passage-time formalism to the reconstruction of high free-energy barriers: A top-start approach
Ying Jiang, Ivan Saika-Voivod
Exact Microscopic One-Body Reduction in the Symmetric Exclusion Process
Olivier Bénichou, Aurélien Grabsch
Macroscopic fluctuation theory of stochastic systems undergoing monitoring
João Costa, Pedro Ribeiro, Andrea De Luca
Charge-selective topological locking and ultraslow coarsening in magnetoelastic media
Tirthankar Banerjee, Amaresh Jaiswal