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    Volume 50, Issue 1 (July 2021)

    Deep Learning–Based Forecasting of COVID-19 in India

    (Received 18 September 2020; accepted 25 May 2021)

    Published Online: 22 July 2021

    CODEN: JTEVAB

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    Abstract

    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 distancing, 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 confirmed, 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 learning 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 forecasting 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.

    Author Information:

    Pillai, Punitha Kumaresa
    Department of Electrical and Electronics Engineering, PSR Engineering College, Virudhunagar, Tamil Nadu

    Durairaj, Devaraj
    Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu

    Samivel, Kanthammal
    Department of Biotechnology, PSR Engineering College, Virudhunagar, Tamil Nadu


    Stock #: JTE20200574

    ISSN:0090-3973

    DOI: 10.1520/JTE20200574

    Author
    Title Deep Learning–Based Forecasting of COVID-19 in India
    Symposium ,
    Committee E50