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Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up

Author

Listed:
  • Vidhi Vig

    (University of Delhi)

  • Anmol Kaur

    (University of Delhi)

Abstract

COVID-19 has spread around the world since it begun in December 2019. The pandemic has created an unprecedented global health emergency since World War II. This paper studies the impact of pandemic and predicts the anticipated casualty rise in India. The data has been extracted from the API provided by https://www.covid19india.org/ and covers up the time period from 30th January 2020 when the first case occurred in India till 13th January 2021. The paper provides a comparative study of six machine learning algorithms namely SMOreg, Random Forest, lBk, Gaussian Process, Linear Regression, and Autoregressive Integrated Moving Average (ARIMA) in forecasting deceased COVID 19 cases, via the data mining tool such as Weka and R. The major findings show that the best predictor model for anticipating the frequency of deceased cases in India is ARIMA (5,2,0). Utilizing this model, we estimated the propagation rate of deceased cases for the next month. The findings reveal that the fatal cases in India could rise from 151,174 to 157,179 within one month with an average of 190 death reports every day. This study will be helpful for the Indian Government and Medical Practitioners in assessing the spread of pandemic in India and devising a combat plan to mitigate the pandemic.

Suggested Citation

  • Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," 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(6), pages 2920-2933, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:6:d:10.1007_s13198-022-01762-7
    DOI: 10.1007/s13198-022-01762-7
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Adrija Roy & Arvind Kumar Singh & Shree Mishra & Aravinda Chinnadurai & Arun Mitra & Ojaswini Bakshi, 2021. "Mental health implications of COVID-19 pandemic and its response in India," International Journal of Social Psychiatry, , vol. 67(5), pages 587-600, August.
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