Critical properties of the SIS model on the clustered homophilic network

Abstract

The spreading of epidemics in complex networks has been a subject of renewed interest of several scientific branches. In this regard, we have focused our attention on the study of the susceptible–infected–susceptible (SIS) model, within a Monte Carlo numerical simulation approach, representing the spreading of epidemics in a clustered homophilic network. The competition between infection and recovery that drives the system either to an absorbing or to an active phase is analyzed. We estimate the static critical exponents , and , through finite-size scaling (FSS) analysis of the order parameter and its fluctuations, showing that they differ from those associated with the contact process on a scale-free network, as well as those predicted by the heterogeneous mean-field theory.

A stochastic model as a survival strategy for an infected population

“…These systems are characterized by presenting a) discrete mesh formed by cells: one, two, three, or more dimensions; b) homogeneity: all cells are equivalent; c) interactions are local: each cell interacts only with cells in its vicinity; d) discrete dynamics: at each discrete time, each cell updates its current state according to a transition rule, taking into account the states of neighboring cells [20][21][22][23][24][25][26][27][28].…”

“…The study of critical phenomena in epidemic models makes it possible to study fluctuations, considering correlations and clustering effects. In this context, phase transitions in out-of-equilibrium systems, that is, systems that have absorbing states, have been widely studied in two different types of cases: with and without recovery [25].…”

“…From a computer science perspective, they are computable models for complex phenomena with large-scale correlations that result from very simple short-range interactions, for example, fluids, neural networks, ecological systems, molecular dynamics, economics, military command networks, modeling of epidemics, etc. [14][15][16][17][18][19][20][21][22][23][24][25]. These systems can undergo phase transitions.…”

“…Several models describe aspects related to an epidemic situation, including models that address the evolution of densities of population groups, and the existence of threshold values for the spread of the infection. The use of Stochastic Cell Automata to study the dynamics of infectious diseases is an important tool to understand the epidemiological process [23][24][25].…”