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2023 | OriginalPaper | Chapter

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

Authors : V. Vanitha, P. Kumaran

Published in: System Design for Epidemics Using Machine Learning and Deep Learning

Publisher: Springer International Publishing

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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 stepping 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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Title: COVID-19 Growth Curve Forecasting for India Using Deep Learning Techniques
Authors: V. Vanitha
P. Kumaran
Publisher: Springer International Publishing
Book: System Design for Epidemics Using Machine Learning and Deep Learning
Print ISBN: 978-3-031-19751-2

Electronic ISBN: 978-3-031-19752-9

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-19752-9_18

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