Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

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 increas...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2022-01, Vol.42 (6), p.6221-6234
Main Authors: Fernandes, Filipe, Stefenon, Stéfano Frizzo, Seman, Laio Oriel, Nied, Ademir, Ferreira, Fernanda Cristina Silva, Subtil, Maria Cristina Mazzetti, Klaar, Anne Carolina Rodrigues, Leithardt, Valderi Reis Quietinho
Format: Article
Language:English
Subjects:
Contamination
Coronaviruses
COVID-19
Decision making
Disease transmission
Fatalities
Mathematical models
Pandemics
Short term
Stacking
Time series
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Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-212788