Deep Learning-Based Forecasting of COVID-19 in India

During the past two decades, the world has confronted many pandemic disease outbreaks. Ebola, severe acute respiratory syndrome, Middle East respiratory syndrome, and, recently, coronavirus disease (COVID-19) have had a massive global impact in terms of stress on local and global human health, economic destruction, and, above all, damage to usual human life. Analyzing past similar infections will help in drawing inferences such as maintaining social dis-tancing, herd immunity, and vaccinating massively to go forward beyond this pandemic. The development of a forecasting model of COVID-19 infectious disease spreading rate plays a vital role in the future preparation of hospital facilities, such as setting up isolated wards, oxygen cylinders, and ventilators, etc., for future patients by the government. Also, the forecasting technique and model is in immediate need for us to understand and face the effect of this and future pandemics. The main objective of this work is to develop an intelligent model based on deep learning for forecasting or estimating COVID-19 future spreading rate in terms of con-firmed, recovered, and deceased cases of 85 days in 4 states in India and India overall. Deep learning neural networks, a kind of machine learning technique, are a powerful tool to predict the future because of their nature of discovering complex nonlinear dependencies. A deep learn-ing long short-term memory (LSTM) network, which is explicitly designed for learning long-term dependencies, is utilized in this work. Hence, one can predict 1 day ahead to any number of (up to 400) days ahead by using this model. To evaluate the performance of the deep learning fore -casting model and to endorse its forecasting accuracy, the criteria of mean absolute error, mean square error, root mean square error, mean absolute percentage error, and Ro are used. The results of the proposed deep learning-based LSTM model are validated by statistical analysis and graphical analysis. Moreover, the proposed model exhibited superior forecasting accuracy.