Random time transformation analysis of Covid19 2020

Abstract

AbstractThe SIR epidemiological equations model new affected and removed cases as roughly proportional to the current number of infected cases. The present report adopts an alternative that has been considered in the literature, in which the number of new affected cases is proportional to the α ≤ 1 power of the number of infected cases. After arguing that α = 1 models exponential growth while α < 1 models polynomial growth, a simple method for parameter estimation in differential equations subject to noise, the random-time transformation RTT of Bassan, Meilijson, Marcus and Talpaz 1997, will be reviewed and compared with stochastic differential equations. Both methods are applied in an attempt to uncover the growth pattern of Covid19.