A simple method to describe the COVID-19 trajectory and dynamics in any country based on Johnson cumulative density function fitting

A simple method is utilised to study and compare COVID-19 infection
dynamics between countries based on curve fitting to publicly shared data
of confirmed COVID-19 infections. The method was tested using data from 80
countries from 6 continents. We found that Johnson cumulative density
functions (CDFs) were extremely well fitted to the data (R2 > 0.99)
and that Johnson CDFs were much better fitted to the tails of the data
than either the commonly used normal or lognormal CDFs. Fitted Johnson
CDFs can be used to obtain basic parameters of the infection wave, such as
the percentage of the population infected during an infection wave, the
days of the start, peak and end of the infection wave, and the duration of
the wave’s increase and decrease. These parameters can be easily
interpreted biologically and used both for describing infection wave
dynamics and in further statistical analysis. The usefulness of the
parameters obtained was analysed with respect to the relation between the
gross domestic product (GDP) per capita, the population density, the
percentage of the population infected during an infection wave, the
starting day and the duration of the infection wave in the 80 countries.
We found that all the above parameters were significantly associated with
GDP per capita, but only the percentage of the population infected was
significantly associated with population density. If used with caution,
this method has a limited ability to predict the future trajectory and
parameters of an ongoing infection wave.