  Compartmental epidemiological models categorize individuals based on their
disease status, such as the SEIRD model
(Susceptible-Exposed-Infected-Recovered-Dead). These models determine the
parameters that influence the magnitude of an outbreak, such as contagion and
recovery rates. However, they don't account for individual characteristics or
population actions, which are crucial for assessing mitigation strategies like
mask usage in COVID-19 or condom distribution in HIV. Additionally, studies
highlight the role of citizen solidarity, interpersonal trust, and government
credibility in explaining differences in contagion rates between countries.
Agent-Based Modeling (ABM) offers a valuable approach to study complex systems
by simulating individual components, their actions, and interactions within an
environment. ABM provides a useful tool for analyzing social phenomena. In this
study, we propose an ABM architecture that combines an adapted SEIRD model with
a decision-making model for citizens. In this paper, we propose an ABM
architecture that allows us to analyze the evolution of virus infections in a
society based on two components: 1) an adaptation of the SEIRD model and 2) a
decision-making model for citizens. In this way, the evolution of infections is
affected, in addition to the spread of the virus itself, by individual behavior
when accepting or rejecting public health measures. We illustrate the designed
model by examining the progression of SARS-CoV-2 infections in A Coru\~na,
Spain. This approach makes it possible to analyze the effect of the individual
actions of citizens during an epidemic on the spread of the virus.
