## 2 Material and Methods  
The term \"agent\" refers to an entity capable of perceiving, thinking, and acting autonomously within a given environment. [PERSON] (1997) defined an agent as \"a software entity that operates continuously and autonomously in a particular environment, typically inhabited by other agents, capable of intervening in its environment flexibly and intelligently without requiring constant human intervention\" [PERSON] (1997). Agent-based models (ABMs) address the limitations of approaches that overlook individual variations by explicitly considering discrete units or agents with unique characteristics and behavioural rules. These models can represent various entities, such as microorganisms, people, animals, families, or other units that can possess individual attributes. According to [PERSON] (1997), some researchers distinguish between individual-based models, where entities follow behavioural rules, and agent-based models, where agents can learn new information from their environment as it changes over time, share information among themselves, and change their behaviours accordingly. However, it was pointed out in [PERSON] (1997) that this distinction is becoming less common in the modelling literature. Generally, ABMs aim to gain insights into how system-level outcomes emerge from individual-level behaviours, regardless of how individuals or agents are conceptualised. At the most fundamental level, these models are composed of three components: a set of agents, the environment, and behaviour rules. Agents exist in an environment, and rules determine how agents interact with each other and the environment throughout a simulation. According to [PERSON] (1997), researchers can expand these essential components by increasing their complexity, variety, and number. For example, the world can range from a simple observation to one that includes detailed geographic information, such as realistic topography. Thus, different agents can be modelled, each with its own rules. Additionally, techniques used for agent-based modelling allow for the incorporation of various forms of stochasticity [PERSON] (1997). Based on this premise, several anthropological studies have used agent-based models to explore disease-related processes. Consequently, agent-based models for multi-scale epidemiological issues began to be developed. Some of these studies focused on large urban populations with broad geographic regions ([PERSON], 1997; [PERSON], 1998). In contrast, others modelled hospital infections ([PERSON], 1998) or even scenarios where the agents are biological components of the immune system ([PERSON], 1998). The 1918 pandemic and influenza have been analysed regarding community structure, mobility, and significant social institutions in disease spread ([PERSON], 1998; [PERSON], 1999). Various other diseases and transmission modes have been studied using agent-based models. For example, during the 2014 Ebola outbreak, a team from Virginia Tech, as part of the Ebola forecasting challenge ([PERSON], 1998), built an agent-based model to provide policy support to decision-makers. The team's methodology used a synthetic population with consistent demographic attributes and family structure to create the model. Over 24 h, each individual in the synthetic population was assigned a sequence of activities with geographic locations. Additionally, a social contact network was obtained to consider the duration of co-location, through weighting the co-location of individuals and edges in the network. The disease model was appropriately chosen to translate the edge weights in the social contact network into the probability of edge infection over a day. Thus, the agent-based approach modelled the evolution of the system state by encoding the actions and interactions of individual agents. Bringing this into the context of infectious diseases, the evolution of disease spread can be modelled through the activity patterns and resulting social contacts of individuals, leading to the spread of infection.