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

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  • 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
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    References listed on IDEAS

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    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).
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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