Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells
| Autor: | K.E. ArunKumar, Timothy M. Brenza, Ch. Mohan Sai Kumar, Masahiro Kawaji, Dinesh V. Kalaga |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Forecasting COVID-19 pandemic Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) General Mathematics General Physics and Astronomy Time series analysis 01 natural sciences World health Article 010305 fluids & plasmas Long short term memory 0103 physical sciences Pandemic 010301 acoustics biology business.industry Applied Mathematics Deep learning Statistical and Nonlinear Physics Grus (genus) biology.organism_classification Geography Recurrent neural network Gated Recurrent Units (GRUs) Artificial intelligence business Demography |
| Zdroj: | Chaos, Solitons, and Fractals |
| ISSN: | 0960-0779 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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