Amazonas suffered greatly during the COVID-19 pandemic. The mortality and fatality rates soared and scarcity of oxygen and healthcare supplies led the health system and funerary services to collapse. Thus, we analyzed the trends of incidence, mortality, and lethality indicators of COVID-19 and the dynamics of their main determinants in the state of Amazonas from March 2020 to June 2021. This is a time-series ecological study. We calculated the lethality, mortality, and incidence rates with official and public data from the Health Department. We used the Prais–Winsten regression and trends were classified as stationary, increasing, or decreasing. The effective reproduction number (Rt) was also estimated. Differences were considered significant when p < 0.05. We extracted 396,772 cases of and 13,420 deaths from COVID-19; 66% of deaths were in people aged over 60; 57% were men. Cardiovascular diseases were the most common comorbidity (28.84%), followed by diabetes (25.35%). Rural areas reported 53% of the total cases and 31% of the total deaths. The impact of COVID-19 in the Amazon is not limited to the direct effects of the pandemic itself; it may present characteristics of a syndemic due to the interaction of COVID-19 with pre-existing illnesses, endemic diseases, and social vulnerabilities.

Northeast Brazil is a region with great international tourist potential. Among the states that make up this region, Paraíba stands out due to the presence of vulnerable groups and factors that contribute to adverse outcomes of COVID-19. Therefore, the aim of this study was to analyze the epidemiological data on the incidence, mortality, and case fatality of COVID-19 in Paraíba. An ecological, population-based study was performed, with data extracted from the Brazilian Ministry of Health database. All cases and deaths from COVID-19 from March 2020 to December 2022 were included. The time series was built by applying the Prais–Winsten regression model, and the daily percent change was calculated to analyze the trends. The highest case fatality of the entire period was in April 2020 (7.8%), but in March 2021, the state broke the dismal record of 1248 deaths and the highest mortality rate (30.5 deaths per 100,000 inhabitants). Stationary mortality and case fatality were better in 2022; however, in February 2022, the mortality rate was at levels similar to the same month of the previous year. These results illustrate that COVID-19 is evolving and needs to be constantly monitored.

The north region of Brazil is characterized by significant vulnerabilities, notably surpassing national poverty indicators. These disparities exacerbated the impact of respiratory illnesses on the healthcare system during the COVID-19 pandemic, particularly in areas with limited healthcare resources, inadequate infrastructure, and barriers to healthcare access. The crisis was further influenced by multiple lineages that emerged as significant virus variants associated with increased transmissibility. Within this context, our ecological study focused on analyzing the epidemiological evolution of COVID-19 in the state of Acre. We constructed time-series trends in incidence, lethality, and mortality from March 2020 to December 2022 using the Prais–Winsten regression model. Our findings revealed that in 2020, there was an increasing trend in incidence, while mortality and lethality continued to decrease (p < 0.05). In the following year, both incidence and mortality decreased, while lethality increased at a rate of 1.02% per day. By the end of 2022, trends remained stationary across all rates. These results underscore the importance of ongoing surveillance and adaptive public health measures to bolster the resilience of healthcare systems in remote and vulnerable regions. Indeed, continuous monitoring of the most predominant SARS-CoV-2 lineages and their dynamics is imperative. Such proactive actions are essential for addressing emerging challenges and ensuring effective responses to adverse situations.

Introduction: COVID-19 is a respiratory disease caused by the SARS-CoV-2 virus, which belongs to the coronavirus family. SARS-CoV-2 is related to other viruses that cause severe acute respiratory syndrome. The emergence of cases of pneumonia of unknown origin triggered the largest viral pandemic in modern times, presenting major challenges to global public health. Objective: To analyze the evolution of the COVID-19 pandemic in the state of São Paulo from 2020 to 2023, focusing on trends in incidence, mortality, and lethality. Methods: Ecological study of time series of incidence, mortality and lethality by COVID-19 in the state of São Paulo using Prais-Winsten regression considering the Weekly Percentage Change (WPC) and probability values (p), considering a significance level of 95% (95% CI). To ensure the reliability of the entered data, double-blind typing was performed by different researchers in the same database extracted from the 2024 Ministry of Health Coronavirus dashboard. Results: From February 2020 and the end of December 2023, 6,763,310 accumulated cases and 182,254 deaths were recorded. Stationary trends were observed for the year 2022, with a reduction in incidence and mortality in the year 2023. However, the epidemiological variable lethality showed a stationary trend. Conclusion: The analysis of the trends in incidence, mortality, and lethality revealed variable dynamics over time, with emphasis on the significant reduction of these indicators in 2023.

Background/Objectives: COVID-19 placed substantial pressure on health systems and showed marked spatial and temporal heterogeneity in Brazil. We analyzed monthly and annual patterns of incidence, mortality, and period-specific case fatality in Pernambuco from 2020 to 2023. Methods: This ecological time-series study used 48 monthly surveillance observations. Monthly case and death counts were modeled with calendar year as a categorical predictor. Negative binomial generalized linear models with log offsets were selected as the primary models because Poisson models showed marked overdispersion; heteroscedasticity—and autocorrelation-consistent (HAC) standard errors were used to reduce sensitivity to serial dependence. A log-linear model estimated Monthly Percent Change (MPC) using a four-month moving-block bootstrap. A quasi-Poisson generalized additive model (GAM) with k = 10 was examined as an exploratory description of non-linearity. Results: Annual incidence increased from 2310.85 per 100,000 in 2020 to 4905.65 per 100,000 in 2022 and decreased to 908.18 per 100,000 in 2023. Mortality peaked in 2021 and period-specific CFR declined markedly thereafter. In the primary negative binomial models with HAC inference, incidence was lower in 2023 than in 2020 (RR = 0.39; 95% CI: 0.17–0.89; p = 0.0247). Mortality in 2021 did not differ from 2020 (RR = 1.11; 95% CI: 0.43–2.89; p = 0.8270), whereas mortality was lower in 2022 (RR = 0.22; 95% CI: 0.09–0.52; p = 0.0006) and 2023 (RR = 0.06; 95% CI: 0.03–0.14; p < 0.0001). The global MPC was +2.52% (moving-block bootstrap 95% CI: −5.42% to 11.00%; p = 0.5152), providing no evidence of a consistent global log-linear trend. The exploratory GAM suggested a non-linear trajectory, but its basis-dimension check indicated that k = 10 was insufficient (k-index = 0.469; p < 0.001); therefore, no inferential conclusion was based on the GAM. Conclusions: Pernambuco surveillance data show an epidemiological divergence between reported incidence and severe outcomes, particularly from 2022 onward. Because vaccination, hybrid immunity, circulating variants, testing practices, and clinical management were not directly modeled, these findings support epidemiological decoupling but do not by themselves establish its biological mechanisms.