Time-scale analysis of population dynamics models for the COVID-19 pandemic

The identification of the various factors influencing the spread of the COVID-19 outbreak, especially during the early stages of the pandemics, is critical to determine appropriate interventions to control the outbreak and prevent its resurgence. In this regard, it is demonstrated here that the time scale characterizing various phases of the COVID-19 outbreak provides most useful information. The analysis is based on a number of popular population dynamics models and data from various countries. It is further demonstrated that this characteristic time scale is robust, when considering (i) different population models, (ii) fitting the parameters of a model to data spanning different periods of the growth phase and (iii) different parameters sets resulting from different fittings of the same data sets. This time scale characterizes the largest portion of the epidemic-growth period and is promoted by the infecting paths of the models and is opposed by the recovery ones. This approach provides a robust and systematic framework for the assessment of the control measures of the COVID-19 outbreak.