IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920306238.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920306238
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110227?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    4. 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).
    5. 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).
    6. 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.
    7. 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).
    8. 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).
    9. 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.
    10. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Di Bartolomeo, Giovanni & D'Imperio, Paolo & Felici, Francesco, 2022. "The fiscal response to the Italian COVID-19 crisis: A counterfactual analysis," Journal of Macroeconomics, Elsevier, vol. 73(C).
    4. Emmanuel Apergis & Nicholas Apergis, 2021. "The impact of COVID-19 on economic growth: evidence from a Bayesian Panel Vector Autoregressive (BPVAR) model," Applied Economics, Taylor & Francis Journals, vol. 53(58), pages 6739-6751, December.
    5. Zaremba, Adam & Bianchi, Robert J. & Mikutowski, Mateusz, 2021. "Long-run reversal in commodity returns: Insights from seven centuries of evidence," Journal of Banking & Finance, Elsevier, vol. 133(C).
    6. Afees A. Salisu & Abdulsalam Abidemi Sikiru & Philip C. Omoke, 2023. "COVID-19 pandemic and financial innovations," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3885-3904, August.
    7. Joe Yazbeck & John B. Rundle, 2023. "A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers," Land, MDPI, vol. 12(11), pages 1-22, October.
    8. Fan, Victoria Y & Jamison, Dean T & Summers, Lawrence H, 2018. "Pandemic risk: how large are the expected losses?," Scholarly Articles 35014363, Harvard Kennedy School of Government.
    9. Robert H. Beach & Christine Poulos & Subhrendu K. Pattanayak, 2007. "Farm Economics of Bird Flu," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 55(4), pages 471-483, December.
    10. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    11. Ahmed, Syud Amer & Barış, Enis & Go, Delfin S. & Lofgren, Hans & Osorio-Rodarte, Israel & Thierfelder, Karen, 2018. "Assessing the global poverty effects of antimicrobial resistance," World Development, Elsevier, vol. 111(C), pages 148-160.
    12. Ghasemi, Abdolrasoul & Boroumand, Yasaman & Shirazi, Masoud, 2020. "How do governments perform in facing COVID-19?," MPRA Paper 99791, University Library of Munich, Germany, revised 20 Apr 2020.
    13. Ghanbari, Behzad, 2020. "On forecasting the spread of the COVID-19 in Iran: The second wave," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Ferchiou, Ahmed & Lhermie, Guillaume & Raboisson, Didier, 2021. "New standards in stochastic simulations of dairy cow disease modelling: Bio-economic dynamic optimization for rational health management decision-making," Agricultural Systems, Elsevier, vol. 194(C).
    15. Brahmbhatt, Milan & Dutta, Arindam, 2008. "On SARS type economic effects during infectious disease outbreaks," Policy Research Working Paper Series 4466, The World Bank.
    16. Kholodilin, Konstantin A. & Rieth, Malte, 2023. "Viral shocks to the world economy," European Economic Review, Elsevier, vol. 158(C).
    17. Roshen Fernando & Warwick J. McKibbin, 2021. "Macroeconomic policy adjustments due to COVID-19: Scenarios to 2025 with a focus on Asia," CAMA Working Papers 2021-17, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Aurora COSMA & Laura Agata TOC & Andreea Daniela TUDOR & Alina Georgiana PETRE, 2020. "Pandemic Costs: Comparative Study Between Countries And Socio-Economic Areas," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 994-1000, November.
    19. Kafieh, Rahele & Saeedizadeh, Narges & Arian, Roya & Amini, Zahra & Serej, Nasim Dadashi & Vaezi, Atefeh & Javanmard, Shaghayegh Haghjooy, 2020. "Isfahan and Covid-19: Deep spatiotemporal representation," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    20. Beirne, John Beirne & Renzhi, Nuobu & Sugandi, Eric Alexander & Volz, Ulrich, 2020. "Financial Market and Capital Flow Dynamics During the COVID-19 Pandemic," ADBI Working Papers 1158, Asian Development Bank Institute.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306238. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.