Open AccessArtificial Neural Networks to access curve behavior of COVID-19 in Brazil: A learning experience based on other countries
Author(s) -
Vera Lúcia Milani Martins,
Fernando Henrique Lermen,
Ruane Fernandes de Magalhães,
Gustavo de Souza Matias
Publication year - 2020
Publication title -
produto and produção
Language(s) - English
Resource type - Journals
eISSN - 1983-8026
pISSN - 1516-3660
DOI - 10.22456/1983-8026.103261
Subject(s) - autoregressive integrated moving average , covid-19 , artificial neural network , population , pandemic , estimation , econometrics , statistics , learning curve , geography , curve fitting , demography , computer science , mathematics , artificial intelligence , economics , time series , medicine , virology , sociology , disease , management , pathology , outbreak , infectious disease (medical specialty) , operating system
The COVID-19 is considered a pandemic due to global contamination. Brazil lacks precision in estimating the virus’s behavior because it has been in the early stages, underestimating notifications of confirmed cases. This study aimed to diagnose the curve behavior of the confirmed cases of COVID-19 in Brazil, based on infected rates, considering the total population and the contaminated population in other countries. For greater accuracy in estimating the Brazilian curve of infected, the Artificial Neural Network structure estimates the population with confirmed cases, combined by the arithmetic mean with SEIR and other estimation methods including ARIMA, SARIMA, trend Holt, and additive Winter. The results showed that, despite maintaining the adopted restriction policies, Brazil tends to face a crisis with a contagion curve below that registered by critical cases such as Spain and the United States, indicating the possibility of the occurrence of 1,000,000 confirmed cases on the 82nd day.