Amaral, Fabio [UNESP]Casaca, Wallace [UNESP]Oishi, Cassio M. [UNESP]Cuminato, Jose A.2022-04-282022-04-282021-01-01IEEE Access, v. 9, p. 126011-126022.2169-3536http://hdl.handle.net/11449/222390The vaccine roll-out has currently established a new trend in the fight against COVID-19. In many countries, as vaccination cover rises, the economic and social disruptions are being progressively reduced, bringing more confidence and hope to the population. However, a crucial debate is related to fair access to vaccines, which would lead to stepping up the pace of vaccination in developing countries. Another important issue is how immunization has influenced the control of the infection, deaths, and transmissibility of the new coronavirus in these countries. In this work, we investigate the effects of the rate of vaccination on the COVID-19 epidemic curves, by employing a new data-driven methodology, formulated on the basis of a modified Susceptible-Infected-Recovered model and Machine Learning designs. The data-driven methodology is applied to assess the influence of the vaccines administered in Brazil on the fight against the virus. The impacts of vaccine efficacy and immunization speed are also investigated in our study. Finally, we have found that the use of anti-SARS-CoV-2 vaccines with a low/moderate efficacy can be offset by immunizing a larger proportion of the population more quickly.126011-126022engartificial intelligenceCOVID-19data-drivenSIRvaccinationArtigo10.1109/ACCESS.2021.31120362-s2.0-85114727530