IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v138y2020ics0960077920304215.html
   My bibliography  Save this article

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model

Author

Listed:
  • Singhal, Amit
  • Singh, Pushpendra
  • Lall, Brejesh
  • Joshi, Shiv Dutt

Abstract

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

Suggested Citation

  • Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920304215
    DOI: 10.1016/j.chaos.2020.110023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920304215
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110023?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. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(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. Dharmaraja Selvamuthu & Deepak Khichar & Priyanka Kalita & Vidyottama Jain, 2023. "Estimation of Mortality Rate of COVID-19 in India using SEIRD Model," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 539-553, March.
    2. Zubair Ahmad & Zahra Almaspoor & Faridoon Khan & Mahmoud El-Morshedy, 2022. "On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data," Mathematics, MDPI, vol. 10(11), pages 1-26, May.
    3. Yanbing Bai & Lu Sun & Haoyu Liu & Chao Xie, 2021. "Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase," IJERPH, MDPI, vol. 18(11), pages 1-16, June.
    4. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.

    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. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    2. Pelinovsky, Efim & Kurkin, Andrey & Kurkina, Oxana & Kokoulina, Maria & Epifanova, Anastasia, 2020. "Logistic equation and COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. 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.
    4. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Jiarui Fan & Haifeng Du & Yang Wang & Xiaochen He, 2021. "The Effect of Local and Global Interventions on Epidemic Spreading," IJERPH, MDPI, vol. 18(23), pages 1-13, November.
    8. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    9. 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).
    10. Huang, Yubo & Wu, Yan & Zhang, Weidong, 2020. "Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    11. 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).
    12. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    13. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Bimal Kumar Mishra, 2022. "Stochastic models on the transmission of novel COVID-19," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 599-603, April.
    15. 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).
    16. 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.
    17. Imdad, Kashif & Sahana, Mehebub & Rana, Md Juel & Haque, Ismail & Patel, Priyank Pravin & Pramanik, Malay, 2020. "The COVID-19 pandemic's footprint in India: An assessment on the district-level susceptibility and vulnerability," MPRA Paper 100727, University Library of Munich, Germany.
    18. Ghanbari, Behzad, 2020. "On forecasting the spread of the COVID-19 in Iran: The second wave," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    19. 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.
    20. Parbat, Debanjan & Chakraborty, Monisha, 2020. "A python based support vector regression model for prediction of COVID19 cases in India," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

    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:138:y:2020:i:c:s0960077920304215. 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.