A discrete-time-evolution model to forecast progress of Covid-19 outbreak

Based on well-known infection models, we constructed a new model to forecast the propagation of the Covid-19 pandemic which yields a discrete-time evolution with one day interval. The proposed model can be easily implemented with daily updated data sets of the pandemic publicly available by many sources. It has only two adjustable parameters and is able to predict the evolution of the total number of infected people in a country for the next 14 days, if parameters do not change during this time. The model incorporates the main aspects of the disease such as the the fact that there are asymptomatic and symptomatic phases (both capable of propagating the virus), and that these phases take almost two weeks before the infected person status evolves to the next (asymptomatic becomes symptomatic or symptomatic becomes either recovered or dead). One advantage of the model is that it gives directly the number of total infected people in each day (in thousands, tens of thousands or hundred of thousands). The model was tested with data from Brazil, UK and South Korea, it predicts quite well the evolution of the disease and therefore may be an useful tool to estimate the propagation of the disease.