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We present an empirical algorithm to forecast the evolution of the number ofCOVID-19 symptomatic patients in the early stages of the pandemic spread andafter strict social distancing interventions. The algorithm is based on a lowdimensional model for the variation of the exponential growth rate thatdecreases after the implementation of strict social distancing measures. Fromthe observable data given by the number of tested positive, our model estimatesthe number of infected hindcast introducing in the model formulation theincubation time. We also use the model to follow the number of infectedpatients who later die using the registered number of deaths and thedistribution time from infection to death. The relationship of the proposedmodel with the SIR models is studied. Model parameters fitting is done byminimizing a quadratic error between the data and the model forecast. Anextended model is also proposed that allows a longer term forecast. An onlineimplementation of the model is avalaible at www.ctim.es/covid19

An empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after social distancing interventions