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An LSTM-Based Forecast Of COVID-19 For Bangladesh

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Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 437))

Abstract

Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected; however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh’s national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh.

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Authors and Affiliations

  1. Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh

    Ashifur Rahman, Md. Akbar Hossain & Mohasina Jannat Moon

Authors
  1. Ashifur Rahman
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  2. Md. Akbar Hossain
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  3. Mohasina Jannat Moon
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Corresponding author

Correspondence to Ashifur Rahman .

Editor information

Editors and Affiliations

  1. CSE, University Grants Commission of Banglade, Dhaka, Bangladesh

    Sazzad Hossain

  2. Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh

    Md. Shahadat Hossain

  3. Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Department of EEE, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh

    Satya Prasad Majumder

  5. Department of Physics, AMITY University Rajasthan, Jaipur, India

    Kanad Ray

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Rahman, A., Hossain, M.A., Moon, M.J. (2022). An LSTM-Based Forecast Of COVID-19 For Bangladesh. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 . Lecture Notes in Networks and Systems, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-19-2445-3_38

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