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Abstract

Early identification of the trends in the number of newly confirmed COVID-19 cases is essential. Weekly periodicity and irregular fluctuations affect the time series of the number of newly confirmed COVID-19 cases. However, a 7-day moving average and a rolling 7-day total delay identifying the trend changes because of assigning a low weight to the most recent day. Additionally, they cannot adjust for the fluctuations due to moving holidays.

For the first time, this study shows that X-13ARIMA-SEATS (X-13), one of the seasonal adjustment methods, can apply to the analysis of the changes in the number of newly confirmed COVID-19 cases with examples of seven countries: Germany, Indonesia, Iran, Russia, the United Kingdom, the United States, and Japan.

This study successfully extracts trend components, calendar-induced components (weekly periodicity and fluctuations due to moving holidays), and irregular components from the time series of seven countries by X-13. Thus, compared to a 7-day moving average and a rolling 7-day total, the method in this study can facilitate more rapid and accurate assessment and strategic responses to the spread of COVID-19. Furthermore, the method could be effective for analyzing other daily data with weekly periodicity.

Keywords

COVID-19 periodicity trend time series analysis seasonal adjustment method X-13ARIMA-SEATS

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Assessment of the spread of COVID-19 in seven countries using a seasonal adjustment method

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