## 2 Materials and Methods
### Input Analysis  
In this section, some components related to input parameters and data sources used in our ABMS model will be presented. The parameters of the ABMS are listed in Table 4, with each parameter being accompanied by a brief description, measurement unit, and default value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Brief Description** & **Unit** & **Default Value** \\ \hline Susceptible students & This count is the initial number of susceptible students in the simulation. & people & 10,000 \\ \hline Susceptible staffers & This count is the initial number of susceptible staffers in the simulation. & people & 200 \\ \hline Outbreak tick & This input is the number to ticks to activate the outbreak scenario. & hours & 1440 \\ \hline Infected students & This count is the number of students to spontaneously infect once the scheduled outbreak tick is reached. & people & 1 \\ \hline Infected staffers & This count is the number of staffers to spontaneously infect once the scheduled outbreak tick is reached. & people & 0 \\ \hline Infection radius & This radius is the maximum distance in which an effective contact is possible. & meters & 2 \\ \hline Social distancing & This input measures the ideal minimum social distance between two agents. & meters & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Input parameters. In addition to the input parameters, several data files from external sources are also required to run the model. For instance, shapefiles (.shp extension) are needed to build the campus geography inside the GIS projection. Specifically, these files contain a geometrical representation of each one of the facilities in the simulation. Moreover, additional CSV files are expected that characterize each of the available amenities' attributes. In particular, each place holds an ID, an area in square meters, a state that determines if the site is currently active or not, and a link to another location in case it is required. Furthermore, another file is needed to select the areas that correspond to workplaces, as each staffer will be assigned to one of those. Moreover, a supplementary routes file is mandatory, as it defines how facilities are connected to each other in a network structure. As previously mentioned, agents walk around campus to their selected next location, meaning they must pass by different places to reach their final destination. That is why, to simplify the traversal process, a graph-based structure was designated. The regarded graph is built by taking the facilities as nodes and the distance between them as arcs. On top of that, [PERSON]'s algorithm is used to find the shortest route between an origin and a destination. Similarly, a group's file is also compulsory. This file contains the academic schedule to use as a reference for the student's routine. Particularly, the program comprises subject groups, each featuring a subject ID, a day of the week, a start time, an end time, a student capacity, and a facility. The idea is that a student enrolls in several groups, determining what he or she must do during the week. It is important to note that not all agent behaviors in our model rely on data; some involve estimating important parameters based on expert insights and available information about the variables. For instance, certain processes lacked specific data, such as the duration of lunch breaks in the cafeterias and the timing of the lunch period. We gathered estimated values through stakeholder consultations and interviews with logistics personnel in these cases. Stakeholders reported an estimated mean duration of 45 min for lunch breaks, ranging from 15 to 75 min. Logistics personnel interviews indicated a typical midday meal time frame from 11:30 am to 200 pm. A similar approach was applied to model several other secondary processes, utilizing input from Universidad EAFIT's staff. However, when data were available, we conducted fitting procedures using Goodness-of-Fit tests. Table 5 summarizes the main stochastic elements in the model.