Agent-based models have been an emerging approach in epidemiological modelling, specifically in investigating the COVID-19 virus. However, there are challenges to its validation due to the absence of real data on specific socio-economic and cognitive aspects. Therefore, this work aims to present a strategy for updating, verifying and validating these models based on applying the particle swarm optimization algorithm to better model a real case. For such application, this work also presents a new framework based on multi-agents that allows estimating the evolution of the pandemic, predicting hospital resources, and estimating adaptive population immunization and population density in specific areas. Evaluation metrics such as the data's Shape Factor (SF), Mean Square Error (RMSE), and statistical and sensitivity analyses of the responses obtained were applied for comparison with the real data. The Brazilian municipality of Passa Vinte, located in the State of Minas Gerais (MG), was used as a case study. The model was updated in cumulative cases until the 365th day of the pandemic. The results obtained in the statistical and sensitivity analysis showed similar patterns around the actual data up to the 500th day of the pandemic. Their mean values of SF and RMSE were 0.96 and 7.22, respectively, showing good predictability and consistency, serving as an adequate tool for decision-making in health policies.