### ABMS in Epidemic Modeling
The interest in modeling diseases with varying viral loads (like Dengue, Zika, and COVID-19) has increased over the last 20 years [24; 35; 36; 37]. Most of these SEIR-based models hold a standard list of personal attributes such as age, gender, health status, and homeplace. Equally important, some of these studies incorporate common patterns such as close-proximity infections [35], fitting probability distributions to several stages of the transmission process [42], individual daily routines such as working and resting [37], transportation networks [43], and geospatial demography [44]. Several recent ABMS studies have specifically focused on modeling the dynamics of the COVID-19 pandemic and assessing control measures. For instance [PERSON] et al. [45] developed an agent-based model to investigate the effectiveness of measures like buffers, face masks, and capacity limitations in controlling COVID-19 spread during mass-gathering events. Similarly, [PERSON] et al. [46] developed an ABMS tool to analyze the spread of COVID-19 in long-term care facilities. The tool uses a contact matrix based on previous research in these facilities, and accurately predicts resident deaths within a minimal variation of 0.1. Another example is [PERSON] et al. [47], which introduces an agent-based model designed to address the ongoing COVID-19 pandemic, particularly focusing on Shanghai's Huangpu District. The model incorporates real-world geographic and population data, along with details of COVID-19 transmission and WHO data. It aims to simulate the virus's spread and account for factors like population movement, detection, and treatment, as well as the impact of other similar diseases on testing resources. Through validation against official COVID-19 data, the model serves as an epidemiological risk assessment system tailored to China's COVID-19 characteristics. It offers insights into adjusting intervention strategies and individual health behaviors, ultimately aiding in informed decision-making for effective pandemic prevention and control in China. Additionally, [PERSON] et al. [48] developed a dynamic agent-based population model to compare different vaccination strategies. They found that to minimize COVID-19-related hospitalizations and deaths, elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available. [PERSON] et al. [49] introduced an agent-based model together with a particle filter approach as a method for studying the evolution of COVID-19. With this model, they introduced a novel method for evaluating the effective reproduction number. Given the framework of disease spread within university settings, [PERSON] and [PERSON] [50] focused on students in Newcastle University accommodation using geospatial ABMS to demonstrate how measures like face masks, early lockdowns, and self-isolation can significantly reduce infections among students. Both their research and ours use ABMS to investigate the dynamics of COVID-19 transmission within a university campus. Nevertheless, there are differences in their research focuses and principal findings. Their study primarily assesses diverse control measures within the student community at Newcastle University accommodations. Their ABMS approach effectively utilizes spatial data and mathematical epidemiological modeling to replicate disease spread, yielding consistent results with prior studies. Their research highlights the adaptability of their ABMS for regions with accessible geospatial data, offering valuable insights into high-risk locations for effective strategies. In contrast, our research emphasizes integrating adaptive learning, specifically Reinforcement Learning, to model and influence agents' behavior during a pandemic, focusing on university campuses. While our study successfully reduces campus crowding, it emphasizes the need for comprehensive epidemic control strategies considering individual decision-making influenced by adaptive learning and targeted interventions.