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Time Series Analysis and Forecast of COVID-19 Pandemic

In: Decision Sciences for COVID-19

Author

Listed:
  • Pawan Thapa

    (Kathmandu University)

Abstract

Background: The coronavirus has killed over 80 million individuals globally. Thus, the linear regression and autoregressive integrated moving average (ARIMA) model analyze the pattern of COVID-19 and identify the future confirmed cases. Methods: In this study, the dataset was used from the Johns Hopkins University (JHU CSSE) data repository in COVID-19 analytics package and prophet library. The time series analysis creates a simulating linear regression and ARIMA model for COVID-19 confirmed cases. The best fit model is select by Akaike information criteria (AIC) and predicts short-term issues validated by Ljung-Box Q test using RStudio Cloud. Results: The linear regression and ARIMA model identifies a best-fit model for time series data. From this model, forecast of more than 300,000 to 1,500,000 from 2020 to 2022. In addition, it depicts a significant increasing trend in the future predictions of confirmed cases. Conclusion: This forecast can help estimate the number of cases that information can provide control measures for an epidemic outbreak. It can suggest the government plan the policies regarding the control of the spread of the virus.

Suggested Citation

  • Pawan Thapa, 2022. "Time Series Analysis and Forecast of COVID-19 Pandemic," International Series in Operations Research & Management Science, in: Said Ali Hassan & Ali Wagdy Mohamed & Khalid Abdulaziz Alnowibet (ed.), Decision Sciences for COVID-19, chapter 0, pages 97-106, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-87019-5_6
    DOI: 10.1007/978-3-030-87019-5_6
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