  We present an empirical algorithm to forecast the evolution of the number of
COVID-19 symptomatic patients in the early stages of the pandemic spread and
after strict social distancing interventions. The algorithm is based on a low
dimensional model for the variation of the exponential growth rate that
decreases after the implementation of strict social distancing measures. From
the observable data given by the number of tested positive, our model estimates
the number of infected hindcast introducing in the model formulation the
incubation time. We also use the model to follow the number of infected
patients who later die using the registered number of deaths and the
distribution time from infection to death. The relationship of the proposed
model with the SIR models is studied. Model parameters fitting is done by
minimizing a quadratic error between the data and the model forecast. An
extended model is also proposed that allows a longer term forecast. An online
implementation of the model is avalaible at www.ctim.es/covid19
