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In this study, we reproduce the first four waves of Covid-19 in Brazil and present a comprehensive analysis. Using a Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with breakpoints, we estimated the Basic Reproduction Number (R0), Infection Fatality Rate (IFR), and immunity period. Our model inferred an R0 of 1.8, decreasing to 1.02 during the first wave, aligned with early outbreak stages characterized by reduced human mobility. Our model suggests that approximately half of the Brazilian population was exposed in the first two waves, indicating substantial underreporting (1:5) for this period. For the third and fourth waves, the model estimated 17 and 19 times more infections than reported cases, suggesting increasing underreporting in these waves. Furthermore, our model estimates an IFR of 0.57% for the first two waves and 0.03% for the third and fourth waves. In conclusion, Brazil successfully reduced the R0 during the first wave; however, the absence of public health measures, such as the reduction of human mobility and a most effective vaccine campaign, during the second wave resulted in higher mortality rates. The third and fourth waves exhibited lower mortality rates, which we attribute to vaccination coverage, prior exposure to the virus, and lower lethality of the Omicron variant.

The Covid-19 pandemic affected Brazil with severity. Brazil is a large country characterized by significant socioeconomic inequalities among its regions. This study aimed to check the sociodemographic factors associated with the Covid-19 mortality rate in Brazilian municipalities. This paper is an ecological study that analyzed data of the first three death waves, with Brazilian municipalities as a unit of analysis. Municipalities were clusterized based on their vulnerabilities in the face of Covid-19. The results show that, from the second death wave in the country, the municipalities with lower social vulnerability were more likely to have a higher mortality level than the other municipalities. The results also show that, among the attributes analyzed, the urban population percentage was the one that most strongly contributed to a municipality being more likely to have a higher level of mortality. Finally, this work demonstrated that using municipality clustering contributes to a more thoughtful and accurate analysis of correlations in ecological studies.