  Individual-based model (IBM) has been used to simulate and to design control
strategies for dynamic systems that are subject to stochasticity and
heterogeneity, such as infectious diseases. In the IBM, an individual is
represented by a set of specific characteristics that may change dynamically
over time. This feature allows a more realistic analysis of the spread of an
epidemic. This paper presents a literature survey of IBM applied to biomedical
and epidemiology research. The main goal is to present existing techniques,
advantages and future perspectives in the development of the model. We
evaluated 89 articles, which mostly analyze interventions aimed at endemic
infections. In addition to the review, an overview of IBM is presented as an
alternative to complement or replace compartmental models, such as the SIR
(Susceptible-Infected-Recovered) model. Numerical simulations also illustrate
the capabilities of IBM, as well as some limitations regarding the effects of
discretization. We show that similar side-effects of discretization scheme for
compartmental models may also occur in IBM, which requires careful attention.
