PREDICTION OF CUMULATIVE RATE OF COVID-19 DEATHS IN BRAZIL: A MODELING STUDY

Géssyca Cavalcante de  Melo; Irena Penha  Duprat; Karina Conceição Gomes Machado de  Araújo; Frida Marina  Fischer; Renato Américo de  Araújo Neto

doi:10.1590/1980-549720200081

##article.authors##

Géssyca Cavalcante de Melo State University of Health Sciences https://orcid.org/0000-0002-6774-857X
Irena Penha Duprat State University of Health Sciences, Alagoas, Brazil
Karina Conceição Gomes Machado de Araújo Department of Health Sciences, Federal University of Sergipe, Brazil.
Frida Marina Fischer Department of Environmental Health, School of Public Health, University of São Paulo, Brazil
Renato Américo de Araújo Neto Mauricio de Nassau University Center, Alagoas, Brazil

DOI:

https://doi.org/10.1590/1980-549720200081

Keywords:

COVID-19, Epidemiology, Mathematical modeling, Pandemic, Brazil

Abstract

Purpose: We estimated the potential number of COVID-19 deaths for Brazil for the next months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17 until May 15, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding to all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15, 14,817 COVID-19 deaths were confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). We estimated that Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco will experience a substantial increase in the rate of cumulative cases until July 15. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.