Metadata about: We study the SIR epidemiological model, with a variable contagion rate, applied to the evolution of COVID19 in Cuba. It is highlighted that an increase in the predictive character depends on understanding the dynamics for the temporal evolution of the rate of contagion $/beta^*$. A semi-empirical model for this dynamics is formulated, where reaching $/beta^*/approx0$ due to isolation is achieved after the mean duration of the disease $/tau=1//gamma$, in which the number of infected in the confined families has decreased. It is considered that $/beta^*(t)$ should have an abrupt decrease on the day of initiation of confinement and decrease until canceling at the end of the interval $/tau$. The analysis describes appropriately the infection curve for Germany. The model is applied to predict an infection curve for Cuba, which estimates a maximum number of infected as less than 2000 in the middle of May, depending on the rigor of the isolation. This is suggested by the ratio between the daily detected cases and the total. We consider the ratio between the observed and real infected cases (k) less than unity. The low value of k decreases the maximum obtained when $/beta^*-/gamma>0$. The observed evolution is independent of k in the linear region. The value of $/beta^*$ is also studied by time intervals, adjusting to the data of Cuba, Germany and South Korea. We compare the extrapolation of the evolution of Cuba with the contagion rate until 16.04.20 with that obtained by a strict quarantine at the end of April. This model with variable $/beta^*$ correctly describes the observed infected evolution curves. We emphasize that the desired maximum of the SIR infected curve is not the maximum standard with constant $/beta^*$, but one achieved due to quarantine when $/tilde R_0=/beta^*//gamma<1$. For the countries controlling the epidemic the maxima are in the region in which SIR equations are linear.

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