Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells

• Published in: Chaos, solitons and fractals Vol. 146; p. 110861
• Main Authors: ArunKumar, K.E., Kalaga, Dinesh V., Kumar, Ch. Mohan Sai, Kawaji, Masahiro, Brenza, Timothy M
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2021
• Subjects: Forecasting COVID-19 pandemic; Gated Recurrent Units (GRUs); Long Short-Term Memory (LSTM); Recurrent Neural Networks (RNNs); Time series analysis; Forecasting COVID-19 pandemic; Long Short-Term Memory (LSTM); Time series analysis; Gated Recurrent Units (GRUs); Recurrent Neural Networks (RNNs)
• ISSN: 0960-0779; 1873-2887; 0960-0779
• DOI: 10.1016/j.chaos.2021.110861

•60-day forecast of COVID-19 cases and their trends for top -10 countries.•Proposed customized RNN models for each country.•Comparison between LSTM and GRU based RNN deep learning models.•Identified countries whereCOVID-19 cases reach plateau. In December 2019, first case of the COVID-19 was reported in Wuhan, Hubei province in China. Soon world health organization has declared contagious coronavirus disease (a.k.a. COVID-19) as a global pandemic in the month of March 2020. Over the span of eleven months, it has rapidly spread out all over the world with total confirmed cases of ~ 41.39 M and causing a total fatality of ~1.13 M. At present, the entire mankind is facing serious threat and it is believed that COVID-19 may have been around for quite some time. Therefore, it has become imperative to forecast the global impact of COVID-19 in the near future. The present work proposes state-of-art deep learning Recurrent Neural Networks (RNN) models to predict the country-wise cumulative confirmed cases, cumulative recovered cases and the cumulative fatalities. The Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells along with Recurrent Neural Networks (RNN) were developed to predict the future trends of the COVID-19. We have used publicly available data from John Hopkins University's COVID-19 database. In this work, we emphasize the importance of various factors such as age, preventive measures, and healthcare facilities, population density, etc. that play vital role in rapid spread of COVID-19 pandemic. Therefore, our forecasted results are very helpful for countries to better prepare themselves to control the pandemic.