COVID-19 epidemic curve in Brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological study

• Autor(a) principal: Pinto,Airandes de Sousa
• Data de Publicação: 2022
• Outros Autores: Rodrigues,Carlos Alberto, Nascimento Sobrinho,Carlito Lopes, Cruz,Lívia Almeida da, Santos Junior,Edval Gomes dos, Nunes,Paulo Cesar, Costa,Matheus Gomes Reis, Rocha,Manoel Otávio da Costa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista da Sociedade Brasileira de Medicina Tropical
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822022000100300
• topic: COVID-19; SARS-CoV-2; Polynomial interpolation; Growth rate; Acceleration; Epidemic curve

ABSTRACT Background: The epidemic curve has been obtained based on the 7-day moving average of the events. Although it facilitates the visualization of discrete variables, it does not allow the calculation of the absolute variation rate. Recently, we demonstrated that the polynomial interpolation method can be used to accurately calculate the daily acceleration of cases and deaths due to COVID-19. This study aimed to measure the diversity of epidemic curves and understand the importance of socioeconomic variables in the acceleration, peak cases, and deaths due to COVID-19 in Brazilian states. Methods: Epidemiological data for COVID-19 from federative units in Brazil were obtained from the Ministry of Health’s website from February 25 to July 11, 2020. Socioeconomic data were obtained from the Instituto Brasileiro de Geografia e Estatística (https://www.ibge.gov.br/). Using the polynomial interpolation methods, daily cases, deaths and acceleration were calculated. Moreover, the correlation coefficient between the epidemic curve data and socioeconomic data was determined. Results: The combination of daily data and case acceleration determined that Brazilian states were in different stages of the epidemic. Maximum case acceleration, peak of cases, maximum death acceleration, and peak of deaths were associated with the Gini index of the gross domestic product of Brazilian states and population density but did not correlate with the per capita gross domestic product of Brazilian states. Conclusions: Brazilian states showed heterogeneous data curves. Population density and socioeconomic inequality were correlated with a more rapid exponential growth in new cases and deaths.