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COVID-19 Growth Curve Forecasting for India Using Deep Learning Techniques

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System Design for Epidemics Using Machine Learning and Deep Learning

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

Due to sudden evolution and spread of COVID-19, the entire community in the globe is at risk. The covid has affected the health and economy and caused loss of life. In India, due to social economic factors, several thousands of people are infected, and India is seen as one of the top countries seriously impacted by the pandemic. Despite of having a modern medical instruments, drugs, and technical technology, it is very difficult to contain the spread of virus and save people from risk. Healthcare system and government personnel need to get an insight of covid outbreaks in the near future to decide on step** up the healthcare facilities, to take necessary actions and to implement prevention policies to minimize the spread. In order to help the government, this study aims to build model a forecast COVID-19 model to foretell growth curve by predicting number of confirmed cases. Three variant models based on long short-term memory (LSTM) were built on the Indian COVID-19 dataset and are compared using the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings have revealed that the proposed stacked LSTM model outperforms the other proposed LSTM variants and is suitable for forecasting COVID-19 progress in India.

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Authors and Affiliations

  1. Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India

    V. Vanitha

  2. Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, Puducherry, India

    P. Kumaran

Authors
  1. V. Vanitha
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  2. P. Kumaran
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Correspondence to V. Vanitha .

Editor information

Editors and Affiliations

  1. Department of CSE, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, India

    G. R. Kanagachidambaresan

  2. Department of Biomedical Engineering, North Eastern Hill University, Shillong, Meghalaya, India

    Dinesh Bhatia

  3. Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D, Institute of Science and Technology, Chennai, TN, India

    Dhilip Kumar

  4. North Eastern Indira Gandhi Regional Institute, Shillong, India

    Animesh Mishra

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Vanitha, V., Kumaran, P. (2023). COVID-19 Growth Curve Forecasting for India Using Deep Learning Techniques. In: Kanagachidambaresan, G.R., Bhatia, D., Kumar, D., Mishra, A. (eds) System Design for Epidemics Using Machine Learning and Deep Learning. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-19752-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-19752-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19751-2

  • Online ISBN: 978-3-031-19752-9

  • eBook Packages: EngineeringEngineering (R0)

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