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An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India

Author

Listed:
  • Rupinder Katoch
  • Arpit Sidhu

Abstract

The swiftly growing and overwhelming epidemic in India has intensified the question: What will the trend and magnitude of impact of the novel coronavirus disease 2019 (COVID-19) be in India in the near future? To answer the present question, the study requires ample historical data to make an accurate forecast of the blowout of expected confirmed cases. All at once, no prediction can be certain as the past seldom reiterates itself in the future likewise. Besides, forecasts are influenced by a number of factors like reliability of the data and psychological factors like perception and reaction of the people to the hazards arising from the epidemic. The present study presents a simple but powerful and objective, that is, autoregressive integrated moving average (ARIMA) approach, to analyse the temporal dynamics of the COVID-19 outbreak in India in the time window 30 January 2020 to 16 September 2020 and to predict the final size and trend of the epidemic over the period after 16 September 2020 with Indian epidemiological data at national and state levels. With the assumption that the data that have been used are reliable and that the future will continue to track the same outline as in the past, underlying forecasts based on ARIMA model suggest an unending increase in the number of confirmed COVID-19 cases in India in the near future. The present article suggests varying epidemic’s inflection point and final size for underlying states and for the mainland, India. The final size at national level is expected to reach 25,669,294 in the next 230 days, with infection point that can be expected to be projected only on 23 April 2021. The study has enormous potential to plan and make decisions to control the further spread of epidemic in India and provides objective forecasts for the confirmed cases of COVID-19 in the coming days corresponding to the respective COVID periods of the underlying regions.

Suggested Citation

  • Rupinder Katoch & Arpit Sidhu, 2025. "An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India," Global Business Review, International Management Institute, vol. 26(2), pages 332-345, April.
  • Handle: RePEc:sae:globus:v:26:y:2025:i:2:p:332-345
    DOI: 10.1177/0972150920988653
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Arindam Banik & Tirthankar Nag & Sahana Roy Chowdhury & Rajashri Chatterjee, 2020. "Why Do COVID-19 Fatality Rates Differ Across Countries? An Explorative Cross-country Study Based on Select Indicators," Global Business Review, International Management Institute, vol. 21(3), pages 607-625, June.
    4. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    5. Yan-Ling Zheng & Li-Ping Zhang & Xue-Liang Zhang & Kai Wang & Yu-Jian Zheng, 2015. "Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
    6. 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.
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