Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study

ABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 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 all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. 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..

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:23

Enthalten in:

Revista Brasileira de Epidemiologia - 23(2020)

Sprache:

Englisch ; Portugiesisch

Beteiligte Personen:

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

Links:

doi.org [kostenfrei]
doaj.org [kostenfrei]
www.scielo.br [kostenfrei]
www.scielo.br [kostenfrei]
Journal toc [kostenfrei]

Themen:

Brazil
Coronavirus infections
Epidemiology
Mathematical modeling
Pandemics
Public aspects of medicine

doi:

10.1590/1980-549720200081

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

DOAJ074450913