FastSIR algorithm: A fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible–infected–recovered compartment model

Abstract

The epidemic spreading on arbitrary complex networks is studied in SIR (Susceptible Infected Recovered) compartment model. We propose our implementation of a Naive SIR algorithm for epidemic simulation spreading on networks that uses data structures efficiently to reduce running time. The Naive SIR algorithm models full epidemic dynamics and can be easily upgraded to parallel version. We also propose novel algorithm for epidemic simulation spreading on networks called the FastSIR algorithm that has better average case running time than the Naive SIR algorithm. The FastSIR algorithm uses novel approach to reduce average case running time by constant factor by using probability distributions of the number of infected nodes. Moreover, the FastSIR algorithm does not follow epidemic dynamics in time, but still captures all infection transfers. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. Average case running time of both algorithms has also been derived and experimental analysis was made on five different empirical complex networks.

Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

“…One of the most prevalent types of dynamic processes of public interest characteristic for the real-life complex networks are contagion processes [1][2][3][4][5][6][7]. Epidemiologists detect the epidemic source or the patient-zero either by analysing the temporal genetic evolution of virus strains [8][9][10], which can be time-demanding or try to do a contact backtracking [11] from the available observed data.…”

An environment aware epidemic spreading model and immune strategy in complex networks

“…The SIR model is a cornerstone model in epidemic spreading modeling where each individual in a population can be in one of three different compartments. There are extensive studies on the epidemic spreading in networks based on the SIR model [7][8][9][10].…”

Simulating SIR processes on networks using weighted shortest paths

“…Furthermore, our method takes into account dynamical correlations between node states overcoming the assumptions of mean-field like approximations 12,14 . In contrast to the well known historical Gillespie or kinetic Monte Carlo methods [23][24][25][26][27][28]36 , we do not have to specify initial conditions upfront and we can sample new realizations from the previous ones by making local random perturbations on the weighted networks. We have shown that for some non-Markovian processes, the average propagation time can scale as a polynomial with the system size, even if the underlying network is small world.…”