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Prediction of COVID Patients using Gated Recurrent Unit Neural Networks

Since January 2020 the world has witnessed a new pandemic that has put the humanity at a grave risk. The COVID-19 disease originated from People's Republic of China has affected 215 nations and has put the life and economy to a standstill. In this scenario, it is very important to predict the patients affected by COVID, so that the administration and the health professionals can take suitable decisions regarding enforcing lockdown, creating isolation and medical facilities and other tasks. In this paper deep learning is utilized as a method of prediction of patients in five countries. Day wise prediction is performed for a week for evaluation that can be extended for more time. It is a type of time series prediction method using deep learning approach. Three different methods are used for prediction and have been found excellent result using the Gated Recurrent Units (GRUs). The results obtained by GRUs are very accurate and establish its supremacy over other networks. Thus, it can be used as a tool for prediction of number of COVID-19 patients by the administrators and health officials. © 2021 Karadeniz Technical University. All rights reserved.