IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920305877.html
   My bibliography  Save this article

Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling

Author

Listed:
  • Vaishnav, Vaibhav
  • Vajpai, Jayashri

Abstract

Ever since the outbreak of novel coronavirus in December 2019, lockdown has been identified as the only effective measure across the world to stop the community spread of this pandemic. India implemented a complete shutdown across the nation from March 25, 2020 as lockdown I and went on to extend it by giving timely partial relaxations in the form of lockdown II, III & IV. This paper statistically analyses the impact of relaxation during Lockdown III and IV on coronavirus disease (COVID) spread in India using the Group Method of Data Handling (GMDH) to forecast the number of active cases using time series analysis and hence the required medical infrastructure for the period of next six months. The Group Method of Data Handling is a novel self organized data mining technique with data driven adaptive learning capability which grasps the auto correlative relations between the samples and gives a high forecasting accuracy irrespective of the length and stochasticity of a time series. The GMDH model has been first validated and standardized by forecasting the number of active and confirmed cases during lockdown III-IV with an accuracy of 2.58% and 2.00% respectively. Thereafter, the number of active cases has been forecasted for the rest of 2020 to predict the impact of lockdown relaxation on spread of COVID-19 and indicate preparatory measures necessary to counter it.

Suggested Citation

  • Vaishnav, Vaibhav & Vajpai, Jayashri, 2020. "Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
  • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305877
    DOI: 10.1016/j.chaos.2020.110191
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920305877
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2020.110191?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. Amira Rachah & Delfim F. M. Torres, 2015. "Mathematical Modelling, Simulation, and Optimal Control of the 2014 Ebola Outbreak in West Africa," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-9, May.
    2. Xingyu Zhang & Yuanyuan Liu & Min Yang & Tao Zhang & Alistair A Young & Xiaosong Li, 2013. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    3. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    4. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    5. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    6. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Yue Teng & Dehua Bi & Guigang Xie & Yuan Jin & Yong Huang & Baihan Lin & Xiaoping An & Dan Feng & Yigang Tong, 2017. "Dynamic Forecasting of Zika Epidemics Using Google Trends," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-10, January.
    8. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    9. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

    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. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    3. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    4. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    6. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    8. Han, Zhimin & Wang, Yi & Cao, Jinde, 2023. "Impact of contact heterogeneity on initial growth behavior of an epidemic: Complex network-based approach," Applied Mathematics and Computation, Elsevier, vol. 451(C).
    9. Ashwin Muniyappan & Balamuralitharan Sundarappan & Poongodi Manoharan & Mounir Hamdi & Kaamran Raahemifar & Sami Bourouis & Vijayakumar Varadarajan, 2022. "Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series Solutions by Using HPM," Mathematics, MDPI, vol. 10(3), pages 1-27, January.
    10. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    11. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    12. Konstantinos Demertzis & Dimitrios Tsiotas & Lykourgos Magafas, 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
    13. Michał Wieczorek & Jakub Siłka & Dawid Połap & Marcin Woźniak & Robertas Damaševičius, 2020. "Real-time neural network based predictor for cov19 virus spread," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.
    14. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    15. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    16. Rafael Pérez Abreu C. & Samantha Estrada & Héctor de-la-Torre-Gutiérrez, 2021. "A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    17. Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
    18. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    19. Mandal, Manotosh & Jana, Soovoojeet & Nandi, Swapan Kumar & Khatua, Anupam & Adak, Sayani & Kar, T.K., 2020. "A model based study on the dynamics of COVID-19: Prediction and control," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    20. Jose M. Martin-Moreno & Antoni Alegre-Martinez & Victor Martin-Gorgojo & Jose Luis Alfonso-Sanchez & Ferran Torres & Vicente Pallares-Carratala, 2022. "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-16, May.

    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:s0960077920305877. 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.