Simulating epidemiological processes using community-structured scale-free networks

The transmission mechanisms of infectious diseases are well known and treated in the literature, but considering that the interactions during the transmission are complex, they may be simulated by computational models. The understanding of the disease dynamics is of great interest, since it makes possible to avoid implications and consequences of a possible outbreak. In this sense, the objective of this work is to present a model that employs scale-free networks with community structures to represent the interactions between individuals of a population. According to the results, the tool simulated successfully the spread of Influenza in a population of 6,500 individuals and proved consistent with parameters found in the literature. As main conclusion, we argue that the community structure in a contact network can significantly affects the disease dynamics