## 4 Discussion
### Study Limitations  
This study, while providing valuable insights into the dynamics of COVID-19 transmission in socio-economically vulnerable areas, has several limitations that should be considered when interpreting the results. The SEPAI3R3O model, although more detailed than traditional SEIR models, still simplifies the complex dynamics of COVID-19 transmission. Some nuances of disease progression and transmission may not be fully captured, potentially affecting the accuracy of long-term projections.  
A significant limitation is the uncertainty of many of the model's parameters. Transmission rates (\(\beta_{i}\)), recovery rates (\(c_{i}\)), and other vital parameters are based on early estimates from the COVID-19 pandemic. These values may have evolved as our understanding of the virus improved, potentially impacting the model's accuracy. While this study incorporated some socio-economic factors, it may only capture some of the nuances of social vulnerability that could influence disease spread in highly disadvantaged areas. Another limitation is the use of static parameters for aspects such as social distancing adherence and mask usage, as these behaviours may change over time due to various factors such as pandemic fatigue or changing public health messaging. The under-reporting rate (\(P_{sub}\)) is a critical parameter significantly impacting the model's projections. However, accurately estimating this rate is challenging and may vary over time and across different population segments. Due to the nature of this study and the ongoing pandemic, comprehensive validation of the model against real-world data from Joana Bezerra Island may be limited.  
While innovative, the model's economic aspects still represent a simplified view of complex economic interactions and thus may only capture some of the nuances of economic impact in a highly vulnerable community. Additionally, while the model includes healthcare units (A5), it may not fully capture the complexities of healthcare system capacity and its dynamic nature during a pandemic.  
The model makes assumptions about individual behaviours (e.g., mobility patterns and adherence to measures) that may only partially reflect the diversity of behaviours in a real-world setting, especially in socio-economically challenged areas. This study focused primarily on mask usage and moderate social isolation, with other potential interventions or combinations needing to be extensively explored. The two-month simulation period may not capture the long-term dynamics of the pandemic or potential seasonal variations in transmission. While the model was adapted to Joana Bezerra Island, it may not capture fine-grained spatial variations within the neighbourhood that could influence disease spread. Although the model includes age-specific parameters for severe infections and mortality, it may not fully capture all age-related differences in transmission, susceptibility, and social behaviour. Finally, the model does not include the impact of vaccination, which became a crucial factor in the later stages of the pandemic.  
Therefore, future studies could address these limitations by incorporating more detailed data, refining parameter estimates, and expanding the model to include a broader range of interventions and socio-economic factors. Despite these limitations, this study provides valuable insights into the potential impacts of public health measures in vulnerable communities. It can serve as a foundation for future research in this critical area.