AN EMPIRICAL MODEL FOR THE SPREAD AND REDUCTION OF THE COVID19 PANDEMIC

Abstract

This document depicts an empirical approach to the dynamics of diagnosed CoVid19 infections at outbreak scenarios. We study empirical daily diagnosed infections. By performing an initial study, based on basic models of infection spreads, we find two distinct exponential regimes in which the CoVid19 displays an infection growth rate. Measures such as household lockdown are critical in order to lower the infection rate. As a result, a crossover point between fast and slow infection rates is found one week after lockdown, which in turn, is the average CoVid19’s incubation period. After this crossover point, and following the slow growth rate, infections reach a maximum after which the infection rate starts to decrease. A possible peak can be found latter to this lockdown critical point, due to a number of households being infected by subjects already sick from the spread periods. However, such peak is a singularity as due to lockdown, the diagnosed infections keep decreasing exponentially. Note that this profile, which we have denoted as Wuhan Quality-Curve (Or Wuhan Q-Curve), is characteristic to the evolution of CoVid19 in China, as infected countries such as Spain or Italy still are at early stages of the Wuhan Q-Curve. However, both countries display such profile up to date. Furthermore, our analysis and the proposal of the Q-Curve as master curve to consider in each CoVid19 outbreak, allows a prediction of outbreak periods, i.e. free spread or lockdown periods, as well as diagnosed cases over time, provided that an initial data analysis is performed at the beginning of the outbreak. We also study the case of South Korea, where early measures were successfully implemented against CoVid19. Finally, data indicates that a soft or hard lockdown result in the same outcome when fighting against CoVid19.