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Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study

Author

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  • Shastri, Sourabh
  • Singh, Kuljeet
  • Kumar, Sachin
  • Kour, Paramjit
  • Mansotra, Vibhakar

Abstract

Covid-19 is a highly contagious virus which almost freezes the world along with its economy. Its ability of human-to-human and surface-to-human transmission turns the world into catastrophic phase. In this study, our aim is to predict the future conditions of novel Coronavirus to recede its impact. We have proposed deep learning based comparative analysis of Covid-19 cases in India and USA. The datasets of confirmed and death cases of Covid-19 are taken into consideration. The recurrent neural network (RNN) based variants of long short term memory (LSTM) such as Stacked LSTM, Bi-directional LSTM and Convolutional LSTM are used to design the proposed methodology and forecast the Covid-19 cases for one month ahead. Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries. Upward/downward trend of forecasted Covid-19 cases are also visualized graphically, which would be helpful for researchers and policy makers to mitigate the mortality and morbidity rate by streaming the Covid-19 into right direction.

Suggested Citation

  • Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306238
    DOI: 10.1016/j.chaos.2020.110227
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    References listed on IDEAS

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    1. Arora, Parul & Kumar, Himanshu & Panigrahi, Bijaya Ketan, 2020. "Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Warwick McKibbin & Alexandra Sidorenko, 2006. "Global Macroeconomic Consequences of Pandemic Influenza," CAMA Working Papers 2006-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Pathan, Refat Khan & Biswas, Munmun & Khandaker, Mayeen Uddin, 2020. "Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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    2. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Önder Çoban & Musa Eşit & Sercan Yalçın, 2024. "ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(2), pages 989-1021, January.
    4. Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Emerson Vilar de Oliveira & Dunfrey Pires Aragão & Luiz Marcos Garcia Gonçalves, 2024. "A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics," IJERPH, MDPI, vol. 21(4), pages 1-19, April.
    6. Ali, Furqan & Ullah, Farman & Khan, Junaid Iqbal & Khan, Jebran & Sardar, Abdul Wasay & Lee, Sungchang, 2023. "COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    7. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    8. Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    9. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Shruti Sharma & Yogesh Kumar Gupta & Abhinava K. Mishra, 2023. "Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods," IJERPH, MDPI, vol. 20(11), pages 1-23, May.
    11. Erick Giovani Sperandio Nascimento & Júnia Ortiz & Adhvan Novais Furtado & Diego Frias, 2023. "Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-34, April.
    12. Rakesh Kumar & Meenu Gupta & Aman Agarwal & Anustup Mukherjee & Sardar M N Islam, 2023. "Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
    13. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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