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

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Since the outbreak in Wuhan, COVID-19 has rapidly spread worldwide, resulting in over 41.39 million confirmed cases and over 1.13 million deaths. Utilizing deep learning models to predict the future transmission trends of COVID-19 can help countries better prepare and control the pandemic.