Short-term predictions of country-specific Covid-19 infection rates based on power law scaling exponents

TL;DR

This study analyzes COVID-19 infection data from 25 countries, revealing power law growth patterns with varying exponents, and demonstrates that strict lockdowns can significantly reduce infection growth rates.

Contribution

It introduces a power law scaling approach to model country-specific COVID-19 infection growth and identifies the impact of lockdown measures on these growth patterns.

Findings

Power law growth behavior observed in all countries analyzed.

Two distinct growth patterns identified: steady and explosive.

Lockdowns effectively reduce growth exponents, as shown in Denmark.

Abstract

The number of corona virus (COVID-19) infections grows worldwide. In order to create short term predictions to prepare for the extent of the global pandemic we analyze infection data from the top 25 affected countries. It is shown that all country-specific infection rates follow a power law growth behavior and the scaling exponents per country are calculated. We find two different growth patterns: either steady power law growth from the very beginning with moderate scaling exponents of 3-5 or explosive power law growth with dramatic scaling exponents of 8-11. In the case of the USA we even find an exponent of 16.59. By means of data analysis we confirm that instituting strict measures of lock-downs combined with a strong adherence by the population are effective means to bring the growth rates down. While many countries have instituted measures there are only three countries (Denmark, Norway, and South Korea) so far where such lock-downs led to a significant reduction of the growth rate. In the case of Denmark we calculate the reduction of the scaling exponents to move from 6.82 to 1.47.