The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements. Author summary Covid-19 has brought the international scientific community into the eye of a storm. Collaboration between researchers, public health workers, and politicians is essential to deal with this challenge. One of the pieces of the puzzle is analysis of epidemiological trends so that both the current and immediate future situation can be carefully evaluated. For this reason we have employed a daily generic growing function to describe the cumulative cases of Covid-19 in several countries and regions around the world, and particularly the European countries during the Covid-19 outbreak. Our model is completely empirical, meaning it relies solely on the daily data update of new cases and does not require assumptions to make predictions. In this manuscript, we detail the methods employed and the degree of confidence we have obtained during this process. We obtain predictions with a success greater than 90%, which means that around 90% of the value of the reported cases are inside the prediction intervals. This can be used for other researchers collaborating with and advising health institutions around the world during the Covid-19 outbreak or any other epidemic that follows the same pattern. We hope it may help facilitate policy decisions, the review of in-place confinement measures, and the development of new protocols.