Sumi Ayako, Koyama Masayuki, Katagiri Manato, Ohtomo Norio
Division of Physics, Department of Liberal Arts and Sciences, Center for Medical Education, Sapporo Medical University, Sapporo, Hokkaido, Japan.
Department of Public Health, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan.
PLoS One. 2025 Jan 13;20(1):e0314233. doi: 10.1371/journal.pone.0314233. eCollection 2025.
We have carried out spectral analysis of coronavirus disease 2019 (COVID-19) notifications in all 47 prefectures in Japan. The results confirm that the power spectral densities (PSDs) of the data from each prefecture show exponential characteristics, which are universally observed in the PSDs of time series generated by nonlinear dynamical systems, such as the susceptible/exposed/infectious/recovered (SEIR) epidemic model. The exponential gradient increases with the population size. For all prefectures, many spectral lines observed in each PSD can be fully assigned to a fundamental mode and its harmonics and subharmonics, or linear combinations of a few fundamental periods, suggesting that the COVID-19 data are substantially noise-free. For prefectures with large population sizes, PSD patterns obtained from segment time series behave in response to the introduction of public and workplace vaccination programs as predicted by theoretical studies based on the SEIR model. The meaning of the relationship between the exponential gradient and the population size is discussed.
我们对日本47个都道府县的2019冠状病毒病（COVID-19）通报进行了频谱分析。结果证实，各都道府县数据的功率谱密度（PSD）呈现指数特征，这在非线性动力系统（如易感/暴露/感染/康复（SEIR）流行病模型）生成的时间序列的PSD中普遍观察到。指数梯度随人口规模的增加而增大。对于所有都道府县，在每个PSD中观察到的许多谱线都可以完全归因于一个基模及其谐波和次谐波，或者几个基本周期的线性组合，这表明COVID-19数据基本无噪声。对于人口规模较大的都道府县，从分段时间序列获得的PSD模式会根据基于SEIR模型的理论研究所预测的公共和工作场所疫苗接种计划的引入而做出反应。文中讨论了指数梯度与人口规模之间关系的意义。
