Automatic method for identification of cycles in COVID-19 time-series data
Miaotian Li, Ciprian Doru Giurcăneanu, Jiamou Liu · Data Science and Management · 2025
All previous methods identify cycles in COVID-19 daily and weekly data based on a subjective interpretation of the results. This poses difficulties for researchers interested in conducting comprehensive studies to investigate the presence of cycles in country/territory/area (CTA). Hence, we propose an algorithm that automatically detects the fundamental period T 0 and its harmonics. Based on previous literature, we used T 0 = 7 days for daily data and T 0 = 52 weeks for weekly data. The new algorithm was applied to the time series from 236 CTAs collected by the WHO. The detection results are reported by considering the WHO region to which the CTA belongs or the latitudinal position of the CTA capital. Our results confirm the findings of other researchers in WHO and latitude-based groups. Concurrently, the results provide new information about CTAs for which COVID-19 time-series data have not been carefully examined.