Evolution of COVID-19 pandemic: Power-law growth and saturation

Abstract

In this paper, we analyze the real-time infection data of COVID-19 epidemic for 21 nations up to June 30, 2020. For most of these nations, the total number of infected individuals exhibits a succession of exponential growth and power-law growth before the flattening of the curve. In particular, we find a universal Embedded Image growth before they reach saturation. However, at present, India, which has I(t) ~ t2, and Russia and Brazil, which have I(t) ~ t, are yet to flatten their curves. Thus, the polynomials of the I(t) curves provide valuable information on the stage of the epidemic evolution, thus on the life cycle of COVID-19 pandemic. Besides these detailed analyses, we compare the predictions of an extended SEIR model and a delay differential equation-based model with the reported infection data and observed good agreement among them, including the Embedded Image behaviour. We argue that the power laws in the epidemic curves may be due to lockdowns.