Covid-19 spread Forecast with respect to vaccination based on LSTM and GRU

Author:

Sunil Sidharth1,Kumar Vishnu Dinesh1,Babu Akhil1,Thilak Goutham1,Udayan Divya1

Affiliation:

1. Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, India

Publisher

ACM

Reference22 articles.

1. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends

2. S. Arunkumar , G. Sriram , and C. Gnanadesikan . 2020. A review of covid 19 in Tamilnadu with regression and correlation co-efficient . Materials Today: Proceedings(2020) . https://doi.org/10.1016/j.matpr. 2020 .10.777 10.1016/j.matpr.2020.10.777 S. Arunkumar, G. Sriram, and C. Gnanadesikan. 2020. A review of covid 19 in Tamilnadu with regression and correlation co-efficient. Materials Today: Proceedings(2020). https://doi.org/10.1016/j.matpr.2020.10.777

3. Improved Analysis of Stock Market Prediction: (ARIMA-LSTM-SMP)

4. Kyunghyun Cho Bart van Merrienboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:arXiv:1406.1078 Kyunghyun Cho Bart van Merrienboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:arXiv:1406.1078

5. An interactive web-based dashboard to track COVID-19 in real time

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