Open AccessLong short-term memory stacking model to predict the number of cases and deaths caused by COVID-19
Author(s) -
Filipe Fernandes,
Stéfano Frizzo Stefe,
Laio Oriel Seman,
Ademir Nied,
Fernanda Cristina Silva Ferreira,
Maria Cristina Mazzetti Subtil,
Anne Carolina Rodrigues Klaar,
Valderi Reis Quietinho Leithardt
Publication year - 2022
Publication title -
journal of intelligent and fuzzy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.331
H-Index - 57
eISSN - 1875-8967
pISSN - 1064-1246
DOI - 10.3233/jifs-212788
Subject(s) - covid-19 , long short term memory , term (time) , pandemic , stacking , computer science , volume (thermodynamics) , series (stratigraphy) , control (management) , econometrics , statistics , artificial intelligence , medicine , mathematics , virology , biology , recurrent neural network , artificial neural network , paleontology , physics , outbreak , infectious disease (medical specialty) , disease , quantum mechanics , nuclear magnetic resonance
The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.