Manjarrez Elias, Delfin Erick F, Dominguez-Nicolas Saul M, Flores Amira
Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla, 14 Sur 6301, Colonia San Manuel, Apartado Postal 406, CP 72570, Puebla, Puebla, Mexico.
Centro de Investigación de Micro y Nanotecnología, Universidad Veracruzana, Calzada Ruiz Cortines 455, Boca del Rio, Veracruz, 94294, Mexico.
Heliyon. 2024 Jul 31;10(15):e35546. doi: 10.1016/j.heliyon.2024.e35546. eCollection 2024 Aug 15.
During the COVID-19 pandemic, the Johns Hopkins University Center for Systems Science and Engineering (CSSE) established a comprehensive database detailing daily mortality rates across countries. This dataset revealed fluctuating global mortality trends attributable to COVID-19; however, the specific differences and similarities in mortality patterns between countries remain insufficiently explored. Consequently, this study employs Fourier and similarity analyses to examine these patterns within the frequency domain, thereby offering novel insights into the dynamics of COVID-19 mortality waves across different nations.
We employed the Fast Fourier transform to calculate the power spectral density (PSD) of COVID-19 mortality waves in 199 countries from January 22, 2020, to March 9, 2023. Moreover, we performed a cosine similarity analysis of these PSD patterns among all the countries.
We identified two dominant peaks in the grand averaged PSD: one at a frequency of 1.15 waves per year (i.e., one wave every 10.4 months) and another at 2.7 waves per year (i.e., one wave every 4.4 months). We also found a cosine similarity index distribution with a skewness of -0.54 and a global median of cosine similarity index of 0.84, thus revealing a remarkable similarity in the dominant peaks of the COVID-19 mortality waves.
These findings could be helpful for planetary health if a future pandemic of a similar scale occurs so that effective confinement measures or other actions could be planned during these two identified periods.
在新冠疫情期间,约翰霍普金斯大学系统科学与工程中心(CSSE)建立了一个详细记录各国每日死亡率的综合数据库。该数据集揭示了因新冠疫情导致的全球死亡率波动趋势;然而,各国死亡率模式的具体差异和相似性仍未得到充分探索。因此,本研究采用傅里叶分析和相似性分析来研究频域内的这些模式,从而为不同国家新冠死亡率波动的动态变化提供新的见解。
我们使用快速傅里叶变换来计算2020年1月22日至2023年3月9日期间199个国家新冠死亡率波动的功率谱密度(PSD)。此外,我们对所有国家的这些PSD模式进行了余弦相似性分析。
我们在总体平均PSD中识别出两个主要峰值:一个频率为每年1.15个波动(即每10.4个月一个波动),另一个为每年2.7个波动(即每4.4个月一个波动)。我们还发现余弦相似性指数分布的偏度为 -0.54,余弦相似性指数的全球中位数为0.84,从而揭示了新冠死亡率波动主要峰值之间的显著相似性。
如果未来发生类似规模的大流行,这些发现可能有助于全球健康,以便在这两个确定的时期内规划有效的隔离措施或其他行动。