ABSTRACT This paper presents a graphical interface called EPIGUI, which allows the study of the dynamics of an infectious disease spread using compartmental models in combination with complex networks. This interface aims at considering stochastic factors that govern the evolution of an infection in a network. Moreover, it provides simple tools to create networks of agents and to define the epidemiological parameters of outbreaks. There are six common infectious disease models (SI, SIS, SIR, SIRS, SEIR, and SEIRS) or a user can provide another model, combining compartments. Moreover, in the simulations the user can either include a synthetic network generated according to a network model (random, small-world, scalefree, modular, or hierarchical) or a real network. This approach can help understand the paths followed by outbreaks in a given community and design new strategies to prevent and to control them. EPIGUI Moreover SI, SI (SI SIS SIR SIRS SEIR SEIRS compartments random, random (random smallworld, smallworld small world, world small-world scalefree modular hierarchical them

RESUMO A teoria de redes complexas constitui um suporte natural para o estudo da propagação de uma doença infecciosa. Neste trabalho, apresentamos um estudo da propagação de uma doença infecciosa com o uso de tal teoria em conjunto com o Modelo Baseado em Indivíduos. Mais especificamente, utilizamos diversos modelos de redes complexas amplamente conhecidos na literatura para verificar seus efeitos topológicos na propagação de uma doença. De modo geral, observamos que redes complexas com propriedades distintas resultam em curvas de indivíduos infectados com comportamentos também distintos, e desta forma, que a proliferação de uma dada doença é altamente sensível ao modelo de rede utilizado. Observamos a erradicação da doença quando adotada a estratégia de vacinação de 10% da população em combinação com os modelos de redes aleatória, mundo pequeno ou modular. Desta forma, estratégias de controle com enfoque na topologia da rede em estudo podem ser utilizadas com êxito como forma de redução ou até mesmo de erradicação de uma doença infecciosa.

ABSTRACT The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.