论文标题

分布的时间和空间演变与19.19大流行有关的数量

Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic

论文作者

Liu, Peng, Zheng, Yanyan

论文摘要

这项工作是根据累计和每日确认的190案件和死亡人数在2020年4月至2022年6月的全球200多个国家 /地区的时间范围内系统地进行数据分析。此类研究功能旨在揭示在19009年大流行中观察到的国家级分布的时间和空间演化,并获得了一些有趣的结果。 (1)累积性案件和死亡人数的分布在COVID-19的早期阶段服从了幂律,并在随后的过程中伸展指数功能。 (2)每天确认的病例和死亡人数的分布在Covid-19的早期和晚期遵守幂律,并在中间阶段伸展指数功能。幂律和拉伸指数行为之间的跨界区域似乎取决于“感染”事件和“死亡”事件的演变。这种观察结果意味着一种与COVID-19的动力学过程相关的重要对称性。 (3)每个度量标准的归一数字的分布显示在2年内的时间缩放行为,并通过拉伸指数函数很好地描述。在此类国家层面的分布中,观察幂律和伸展指数行为表明,人类相互联系的社会中病毒传播过程的固有动力学基础。因此,这对于理解和数学对COVID-19的大流行很重要。

This work systematically conducts a data analysis based on the numbers of both cumulative and daily confirmed COVID-19 cases and deaths in a time span through April 2020 to June 2022 for over 200 countries around the world. Such research feature aims to reveal the temporal and spatial evolution of the country-level distribution observed in COVID-19 pandemic, and obtains some interesting results as follows. (1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course. (2) The distributions of the numbers for daily confirmed cases and deaths obey power-law in early and late stages of COVID-19 and stretched exponential function in middle stages. The crossover region between power-law and stretched exponential behaviour seems to depend on the evolution of "infection" event and "death" event. Such observation implies a kind of important symmetry related to the dynamics process of COVID-19 spreading. (3) The distributions of the normalized numbers for each metric show a temporal scaling behaviour in 2-year period, and are well described by stretched exponential function. The observation of power-law and stretched exponential behaviour in such country-level distributions suggests underlying intrinsic dynamics of a virus spreading process in human interconnected society. And thus it is important for understanding and mathematically modeling the COVID-19 pandemic.

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