## 2 Materials and Methods
### COVID-19 Transmission Process
An essential aspect of the COVID-19 transmission process is the probability of exposure due to close contact with an infected individual. As suggested by [PERSON] et al. [66], this component is included in our model as the likelihood of a _Gamma_ distribution with parameters \(\alpha\), \(\beta\), and \(\lambda\) described in Equation (2). The proposed approach designates that the exposure progression rate \(g(t)\) is a function of the time the infected subject has been able to spread the virus. The recommended distribution uses a shift parameter \(\lambda\), because evidence shows that virus carriers can spread the virus up to 2.4 days before symptoms' onset [66]. \[g(t)\sim Gamma(\alpha=2.11,\beta=1.3,\lambda=-2.4) \tag{2}\]  
\[f(t)\sim Lognormal(\mu=1.621,\sigma=0.418) \tag{3}\]  
Another critical feature in the representation of the epidemic process is the latency period. The latency period is defined as the time interval between an individual being exposed and later being capable of spreading the virus to others [67]. [PERSON] et al. [66] indicate that spreadability is proportional to the appearance of the first symptoms. Accordingly, we assumed in our model that this period can be specified in terms of the incubation period (time between the exposition and symptoms' onset) by shifting it 2.4 days to the past. In addition, [PERSON] et al. [68] estimate the distribution of the incubation period as a _Lognormal_ distribution with parameters \(\mu\) and \(\sigma\), as depicted in Equation (3), which is the distribution that we considered in our model. As previously mentioned, at the time of this study, it was assumed that an infected individual would either die or make a full recovery, with the latter resulting in complete immunity. Additionally, when an agent becomes infected, a random process is employed to classify the agent into a specific patient type. This classification plays a crucial role in determining future outcomes, considering the well-established fact that COVID-19 affects individuals differently. In our model, we use the patient classification based on the severity proposed by [PERSON] et al. (1996). Following the cited grading, infected agents are categorized according to empirical probability \(P(type)\), as shown in Equation (4). \[P(type)=\left\{ \begin{matrix}0.30,&type=&\text{No symptoms}\\ 0.55,&type=&\text{Moderate symptoms}\\ 0.10,&type=&\text{Severe symptoms}\\ 0.05,&type=&\text{Critical symptoms}\end{matrix} \right\} \tag{4}\]  
Once a patient type is assigned, the simulation must determine whether the agent will survive or not, and schedule the corresponding event. In fact, [PERSON] et al. (2007) state that the severity of symptoms influences mortality. Considering that, we assumed, based on [PERSON] et al. (2007), that the death rates are 15% for patients with severe symptoms and 50% for critical subjects, as shown in Equation (5). \[P(dying\mid type)=\left\{ \begin{matrix}0.15,&type=&\text{Severe symptoms}\\ 0.50,&type=&\text{Critical symptoms}\\ 0&\text{otherwise}\end{matrix} \right\} \tag{5}\]  
It is assumed that neither death nor recovery follows a constant time duration after infection. Instead, the time until discharge is considered a random variable with positive values. To address  
Figure 1: COVID-19 transmission process ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020). this, we conducted a goodness-of-fit test using the Kolmogorov-Smirnov test based on patient records from [PERSON] et al. (2017). The resulting distribution is presented in Equation (6). \[d(t)\sim Gamma(\alpha=1.99,\beta=7.77) \tag{6}\]  
Certainly, understanding the disease dynamics of COVID-19 highlights ABMS potential for modeling disease transmission. ABMS ability to represent social interactions, individual decision-making, and system-level behaviors makes it an excellent tool in epidemic modeling. In developing our ABMS and detailing its components, we have aimed for a comprehensive representation of a micro-community during a COVID-19 outbreak.