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  .
We model and forecast the early evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February 25, 2020 to March 30, 2020. This early period accounts for unawareness of the epidemiological characteristics of the disease in a new territory, sub-notification of the real numbers of infected people and the timely introduction of social distancing policies to flatten the spread of the disease. We use two variations of the SIR model and we include a parameter that comprises the effects of social distancing measures. Short and Long terms forecasts show that the social distancing policy imposed by the government is able to flatten the pattern of infection of the COVID-19 . However, our results also 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 . Furthermore, our long term simulations forecast the optimal date to end the policy. Finally, we show that the proportion of asymptomatic individuals affects the amplitude of the peak of symptomatic infected, suggesting that it is important to test the population .
A
early
D
,
R
, 28,
30,
A
This early period accounts for unawareness of the epidemiological characteristics of the disease in a new territory, sub-notification of the real numbers of infected people and the timely introduction of social distancing policies to flatten the spread of the disease.
R
in this model that accounts for
that comprises
R
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.
social distancing measures. Short and
R
confinement
social distancing
D
and we are able to find the optimal date to end the policy
A
also
A
. Furthermore, our long term simulations forecast the optimal date to end the policy. Finally, we show that the proportion of asymptomatic individuals affects the amplitude of the peak of symptomatic infected, suggesting that it is important to test the population