Modeling and forecasting the Covid-19 pandemic in Brazil

Bastos, Saulo B.; Cajueiro, Daniel O.

We model and forecast the evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February, 25, 2020 to March, 28, 2020. We use two variations of the SIR model and we include a parameter in this model that accounts for the effects of confinement measures. We do not calibrate our models parameters, but we estimate all of them based on a clear hierarchical procedure of squared error minimization. The estimated parameters of the ratio between symptomatic and asymptomatic individuals, the proportion of infected individuals that die and the usual epidemiological parameters have a great match with the ones provided by the literature. Our final models provide precise forecasts of the number of infected individuals. We use these models to discuss different scenarios of public policies. Long terms forecasts show that the confinement policy imposed by the government is able to flatten the pattern of infection of the COVID-19 and we are able to find the optimal date to end the policy. However, our results show that if this policy does not last enough time, it is only able to shift the peak of infection into the future keeping the value of the peak in almost the same value.