## 3 Results
### Adaptive Learning Integration with ABMS
This involves blending expert insights, university data, and our own system knowledge. This multifaceted approach ensures that our model realistically represents how people behave during a pandemic in a campus setting, considering various influential factors. The literature demonstrates the importance of geolocalized policies, particularly within university settings, in effectively managing disease transmission during pandemics. During the COVID-19 pandemic, universities had to create detailed policies for closing and reopening, addressing specific challenges like social distancing on campuses. For instance, [PERSON] et al. [2] research emphasizes the need for targeted measures, such as geolocalized policies, acknowledging the intricate dynamics of COVID-19 transmission within universities. Additionally, innovative methodologies, like geolocalized analysis of social media data, provide insights into the impact and effectiveness of interventions, including geolocalized ones. The studies by [PERSON] and [PERSON] [3] and [PERSON] et al. [4] highlight the broader significance of government actions and individual human mobility in shaping infectious disease spread, underlining the importance of understanding and incorporating these dynamics into policy design for effective disease control. The collective literature underscores the necessity of a comprehensive approach that integrates both general guidelines and specific patterns of movement and interaction, particularly in dynamic environments like university campuses. Building upon the importance of geolocalized policies found in the literature, our research aligns with existing studies highlighting the critical role of geolocalized policies in managing infectious disease outbreaks within specific environments. Our ABMS incorporates the spatial structure of the university campus, a key determinant that significantly influences the dynamics of disease transmission. The transmission dynamics within a university setting are not homogenous, varying across different regions, buildings, and common spaces. The actions of individuals, including students, staff, and faculty, are intrinsically linked to their respective locations within the campus. This understanding prompted us to embed geospatial elements into our model, enabling us to distinguish the behavior of individuals in crowded communal areas, such as libraries or dining halls, from those in less crowded areas, like open spaces or outdoor courtyards. Deciphering the true dynamics of the university campus, which are inherently geolocalized, is vital for crafting accurate control measures. Our ABMS contributes to the evolving field of epidemic modeling, aligning with recent studies advocating for more sophisticated models. [PERSON] [20], [PERSON] et al. [34], [PERSON] and [PERSON] [35] emphasize traditional compartmental models' limitations and endorse Agent-Based Modeling's advantages in capturing individual-level behaviors and spatial dynamics during disease outbreaks. The SEIR model, a compartmental model, simplifies the population into discrete health states, and differential equations govern transitions between these states. While the ODE-based SEIR model provides a broad overview of disease spread, it operates under the assumption of homogeneity within each compartment, and it does not explicitly model interactions among individuals. The limitations of simpler models, suchas the SEIR model, have been recognized in the literature, as they may struggle to capture intricate and localized behaviors crucial for understanding the transmission of diseases like COVID-19. Notably, studies by [PERSON] and [PERSON] [35] and [PERSON] [37] and [PERSON] et al. [4] emphasize the need for more nuanced models that consider spatial factors and individual behaviors to capture the dynamics of disease spread better. In contrast, our ABMS is a more intricate and nuanced model that simulates the actions and interactions of independent agents to evaluate their collective impact on the entire system.