The time series consisting of 800-day COVID-19 spread data from USA, Germany, Brazil, India, Japan, Egypt, Turkey, and world total taken from the Our World in Data database, was analysed within the framework of non-linear time series. Correlogram diagrams, Fourier power spectra and Lyapunov exponents were examined for each series and it was seen that they did not behave linearly. For these non-linear time series, the lag time and embedded dimension were calculated and 3-dimensional phase spaces for each case were constructed. By examining the constructed phase space profiles, the spread dynamics of COVID-19 in each country and the world total is discussed comparatively. As a result of the phase space analysis, it was seen that the spread of COVID-19 was complex and three different complex behaviour patterns emerged according to the examined countries. This behavioral decomposition was also seen in the correlogram diagrams of the countries, the Fourier power spectrum and the Lyapunov exponents. The nonlinear time series method we used will contribute to the understanding of the qualitative characteristics of the complex behaviour of the COVID-19 pandemic.

The time series consisting of 800-day COVID-19 spread data from USA, Germany, Brazil, India, Japan, Egypt, Turkey, and world total taken from the Our World in Data database, was analysed within the framework of non-linear time series. Correlogram diagrams, Fourier power spectra and Lyapunov exponents were examined for each series and it was seen that they did not behave linearly. For these non-linear time series, the lag time and embedded dimension were calculated and 3-dimensional phase spaces for each case were constructed. By examining the constructed phase space profiles, the spread dynamics of COVID-19 in each country and the world total is discussed comparatively. As a result of the phase space analysis, it was seen that the spread of COVID-19 was complex and three different complex behaviour patterns emerged according to the examined countries. This behavioral decomposition was also seen in the correlogram diagrams of the countries, the Fourier power spectrum and the Lyapunov exponents. The nonlinear time series method we used will contribute to the understanding of the qualitative characteristics of the complex behaviour of the COVID-19 pandemic.