The power-law distribution in the geometrically growing system: Statistic of the COVID-19 pandemic

TL;DR

This paper proposes a general mechanism explaining power-law distributions in geometrically growing systems, such as COVID-19 pandemic data, based on a log-CSχ1 distribution approximation, highlighting the role of system parameters and correlations.

Contribution

It introduces a unified framework linking geometrically growing systems to power-law distributions via the log-CSχ1 model, applicable to real-world phenomena like pandemics.

Findings

The distribution approximates a power-law or log-normal at the upper limit.

The asymptotic exponent depends on variances and correlations of system parameters.

The approach explains COVID-19 statistical patterns.

Abstract

The power-law distribution is ubiquitous and its mechanism seems to be various. We find a general mechanism for the distribution. The distribution of a geometrically growing system can be approximated by a log - completely squared chi distribution with 1 degree of freedom (log-CS$\chi_1$), which reaches asymptotically a power-law distribution, or by a log-normal distribution, which has an infinite asymptotic slope, at the upper limit. For the log-CS$\chi_1$, the asymptotic exponent of the power-law or the slope in a log-log diagram seems to be related only to the variances of the system parameters and their mutual correlation but independent of an initial distribution of the system or any mean value of parameters. We can take the log-CS$\chi_1$ as a unique approximation when the system should have a singular initial distribution. The mechanism shows a comprehensiveness to be applicable to wide practice. We derive a simple formula for the Zipf's exponent, which will probably demand that the exponent should be near -1 rather than exactly -1. We show that this approach can explain statistics of the COVID-19 pandemic.