Résumé
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. [ FROM AUTHOR] Copyright of Statistical Journal of the IAOS is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)