An empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after social distancing interventions
Luis Álvarez, Miguel Colom, Jean-Michel Morel
⚠ This is a preprint. It may change before it is accepted for publication.

Abstract

In this work we present an empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after 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 the social distancing measures. In addition, the incubation time of COVID-19 is taken into account in the analysis. An experimental work is carried out adjusting the model parameters to different countries such as China, South Korea, Italy, France and Spain, obtaining consistent results but which may be subject to significant errors due to the lack of reliability in the data used.

Download