Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence

Renato Machado Cotta; Carolina Palma Naveira-Cotta; pierre magal

on this pandemic, we have chosen to use the information from China in terms of the accumulated confirmed infectious cases. The data for China was extracted from [6] , complemented by the most recent data from [13] up to March 25 th , 2020. The exponential fit for the early phase of the China CR(t) dataset provided the estimates of the three parameters, 1 = 0.14936, 2 = 0.37680, 3 = 1.0, from which we have estimated 0 = 5.046. The remaining data for the initial conditions, 0 and 0 , and the early stage transmission rate, 0 , are in fact recalculated from within the MCMC algorithm, since the changing values of f will affect them, according to eqs. (7.c-e). The average times in the model were taken as 1/ν=7 and 1/η=7 days and the isolation measures were taken at N=29 days [6] . First, experimental data from China from the period of January 19 th up to February 17 th was employed in demonstrating the estimation of three parameters, 0 , , and 0 , assuming there is no significant time variation in the function f(t) ( = 0).