## 2 Material and Methods
### Multi-Agent Simulation  
In the study by [PERSON] et al. (2020), the COVID-ABS model, an agent-based model grounded in the SEIR framework, was proposed to simulate the dynamics of the COVID-19 pandemic. This innovative model uses agents representing individuals, businesses, and governmental entities, allowing for the analysis of various social distancing strategies and their impacts on public health and the economy. To utilise the essence of such a model and have more support to prove our research hypothesis, we chose the neighbourhood with the lowest human development index in \begin{table}
\begin{tabular}{l c c c} \hline
**Age Group** & \(\eta_{3}\)—\% Symptmatic Cases** & \(\eta_{4}\)—\% Hospitalised Cases** & \(\eta_{5}\)—Infection** \\
**(Years)** & **Requiring Hospitalisation** & **Requiring Critical Care** & **Fatality Ratio** \\ \hline
0–9 & 0.1 & 5.0 & 0.002 \\ \hline
10–19 & 0.3 & 5.0 & 0.006 \\ \hline
20–29 & 1.2 & 5.0 & 0.03 \\ \hline
30–39 & 3.2 & 5.0 & 0.08 \\ \hline
40–49 & 4.9 & 6.3 & 0.15 \\ \hline
50–59 & 10.2 & 12.2 & 0.6 \\ \hline
60–69 & 16.6 & 27.4 & 2.2 \\ \hline
70–79 & 24.3 & 43.2 & 5.1 \\ \hline
80+ & 27.3 & 70.9 & 9.3 \\ \hline \end{tabular}
\end{table} 
Table 1: Rates of medical conditions considering hospitalised (\(\beta_{3}\)), severe (\(\beta_{4}\)), and death (\(\beta_{5}\)) cases grouped by age. Adapted from data specific to Brazil [35; 36]. Recife, Joana Bezerra Island. Located in the heart of Recife, it presents unique characteristics, with a population density of 144.85 per hectare and 12,629 inhabitants. The average nominal monthly household income is BRL 705.83 and 3.5 residents in each of its 3606 households [37]. This study proposes to adapt the COVID-ABS model to reflect this neighbourhood's socioeconomic and demographic reality. Using the SEPAI3R3O model, we extended the traditional SEIR model to capture the dynamics of COVID-19 in greater detail, incorporating additional states such as pre-symptomatic (P), asymptomatic (A), various levels of infection severity (_I_, \(I\), \(I\), _I_), and recovery (_R_, \(R\), _R_), as well as a specific compartment for deaths (O). This simulation considers the pessimistic scenario of the number of cases in this region, even in the face of preventive measures. Thus, the simulation focused exclusively on mask usage with 50% adherence to social isolation, reflecting a viable strategy that balances efficacy in reducing viral transmission with moderate social and economic impacts. This scenario was chosen due to its practical relevance and potential applicability in the Joana Bezerra Island community, given its specific socio-economic characteristics and based on the artefacts demonstrated in the research questions, where it was possible to show the need for citizen mobility even in social isolation measures. For the agent-based system model adapted from COVID-ABS in the context of this research, we used the clinical parameters of COVID-19 adapted from the SEPAI3R3O epidemiological model. It was also necessary to re-formulate the types of agents to focus on including social and demographic characteristics that influence the dynamics of disease transmission as follows:  
Agent A1--Individuals: In addition to primary attributes such as position, age, epidemiological status, and infection status, we included the variable \"Social Vulnerability Level\", which reflects the individual's exposure to risk factors associated with their socio-economic condition. Agent A2--Residences: Residences are characterised by their location and family composition and \"Housing Conditions\", indicating the adequacy and access to basic services which can affect disease transmission within family units. This new variable was identified after dividing the social strata given the distribution of basic sanitation among neighbourhoods. Agent A3--Commercial and Service Establishments: These agents consider \"Implemented Prevention Measures\" such as the availability of sanitation and space for social distancing impacting interactions with Agent A1.