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.
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Abbreviations
- ARIMA:
-
Auto-Regressive Integrated Moving Average
- ACF:
-
Auto Correlation Function
- ADF:
-
Augmented Dicky-Fuller test
- AIC:
-
Akaike Information Criteria test
- PACF:
-
Partial Auto Correlation Function
- JHU:
-
Johns Hopkins University
- WHO:
-
World Health Organization
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I want to acknowledge the people who directly and indirectly contributed to this study. First, I want to thank Professor Dr. Bharatendra Rai for his YouTube video tutorial related to a similar topic for the USA and India that inspired me to study the same here in Nepal.
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Pawan Thapa, Lecturer, Department of Geomatics Engineering, Kathmandu University, Dhulikhel, Nepal.
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Thapa, P. (2022). Time Series Analysis and Forecast of COVID-19 Pandemic. In: Hassan, S.A., Mohamed, A.W., Alnowibet, K.A. (eds) Decision Sciences for COVID-19. International Series in Operations Research & Management Science, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-030-87019-5_6
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