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Recurrent neural network and reinforcement learning model for COVID-19 prediction.

Ramasamy, Lakshmana K.; Khan, Firoz; Din, Sadia; Band, Shahab S.; Mosavi, Amir; Ibeke, Ebuka


Lakshmana K. Ramasamy

Firoz Khan

Sadia Din

Shahab S. Band

Amir Mosavi

Ebuka Ibeke


Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.


RAMASAMY, L.K., KHAN, F., DIN, S., BAND, S.S., MOSAVI, A. and IBEKE, E. [2021]. Recurrent neural network and reinforcement learning model for COVID-19 prediction. Frontiers in public health [online], (accepted). To be made available from:

Journal Article Type Article
Acceptance Date Sep 2, 2021
Deposit Date Sep 2, 2021
Publicly Available Date Sep 2, 2021
Journal Frontiers in public health
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Keywords COVID-19; Deep learning; LSTM:; RNN; Prediction; Reinforcement learning
Public URL


RAMASAMY 2021 Recurrent neural network (AAM) (868 Kb)

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Copyright Statement
© 2021 RAMASAMY, Khan, Din, Band, Mosavi and Ibeke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

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