### ABMS in Epidemic Modeling  
Mathematical modeling is a fundamental tool for analyzing strategies to control the effects of an epidemic outbreak [20; 21; 22; 23; 24]. It offers a formal structure that allows modelers to develop practical solutions to real-world problems. Numerous authors have successfully applied these methods to model outbreaks since the 16 th century [25; 26; 27; 28]. Integrating a modeling-based approach for epidemic management has improved general well-being conditions and assisted government agencies in developing effective public health policies [28; 29]. Research on epidemiology models has focused on the development of compartment-based models [25; 28]. In essence, a compartmental model divides the population into subgroups by fitting the disease to a natural history structure and estimating related parameters from available data [25; 30]. Most of these compartment-based models can be solved and analyzed using simple differential equation techniques, and, as a consequence, these have proven to be very useful in real epidemic scenarios [25; 31; 32; 33]. However, these approaches are generally very rough simplifications of an epidemic event with strong assumptions on the internal processes and homogeneous mixing of people. Additionally, these studies have commonly ignored individual heterogeneity and multifaceted interactions between individuals. They commonly assume that contacts are static and interactions are not necessarily spatially related. Moreover, there is evidence of a consensus among agent-based modelers about traditional modeling methods lacking the necessary tools to understand such complex systems thoroughly [20; 34; 35]. Another approach for epidemics modeling focuses on the use of ABMS. Many researchers have worked to explain the emergent epidemic behavior of simple interactions within a community [26; 36; 37; 38; 39; 40; 41]. According to [PERSON] et al. [26], a significant benefit of agent-based epidemic modeling is that it allows exceptional flexibility to design very elaborate epidemic processes. For instance, [PERSON] and [PERSON] [35] implemented a GIS-enabled ABMS to simulate a generic city-wide epidemic incorporating attributes like gender, age, ethnicity, and many others to determine the susceptibility of different community groups. As noted by the authors, traveling individuals are more likely to be exposed, and as a consequence, the infection tends to concentrate in places like schools and universities. Later, [PERSON] and [PERSON] [37] applied ABMS to explore the dynamics of cholera transmission in a refugee camp in Kenya. They modeled factors in family and friendship relationships and goal-oriented agent behavior for determining where to move. Additionally, [PERSON] and [PERSON] [37] performed a set of experiments to determine the effects of geographical interactions and concluded that geospatial setups truly determine the outcomes of an epidemic. Another example is [PERSON] et al. [41], where the authors introduce the Pandemic Disease Simulation (PDSIM) framework, an innovative ABMS that addresses the impact of the COVID-19 pandemic on diverse communities. PDSIM incorporates attributes like gender, age, ethnicity, and other individual characteristics to assess the susceptibility of different community groups to the COVID-19 pandemic. The framework allows the simulation of disease propagation, the identification of vulnerable groups, and the assessment of containment measures' effectiveness, offering valuable insights for informed decision-making and the development of resilient and sustainable societies. All these studies support the notion that individual attributes and interactions admittedly affect the course of an epidemic. A substantial number of studies have applied ABMS to reproduce compartmental-like behavior using the SEIR scheme. The SEIR scheme is an equation-based model that divides the population into susceptibles, exposed, infected and recovered to model how an infectious disease spreads in a closed community.