EPIDEMIC INDIVIDUAL-BASED MODELS APPLIED IN RANDOM AND SCALE-FREE NETWORKS

This work proposes a version of the Individual-Based Model (IBM) that converges, on average, to the result of the SIR (Susceptible-Infected-Recovered) model, and studies the effect of this IBM in two types of networks: random and scale-free. A numerical computational case study is considered, using large scale networks implemented by an efficient framework. Statistical tests are performed to show the similarities and differences between the network models and the deterministic model taken as a baseline. Simulation results verify that different network topologies alter the behavior of the epidemic propagation in the following aspects: temporal evolution, basal reproducibility and the number of infected in the final.