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

Abstract

The power-law distribution is ubiquitous and seems to have various mechanisms. 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 one degree of freedom (log-CS χ1), which reaches asymptotically a power-law distribution, or by a lognormal distribution, which has an infinite asymptotic slope, at the upper limit. For the log-CS χ1, 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 χ1 as a unique approximation when the system should have a singular initial distribution. The mechanism shows comprehensiveness to be applicable to wide practice. We derive a simple formula for 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.