ABSTRACT Chile has become one of the countries most affected by Covid-19, a pandemic that has generated a large number of cases worldwide, which if not detected and treated in time can cause multi-organic failure and even death. The social determinants of health such as education, work, social security, housing, environment, support networks and social cohesion are important aspects to consider for the control and intervention of this pathology. Therefore, it is essential to have information about the progress of the infections at the national level and thus apply effective public health interventions. Objectives: In this paper, we compare different time series methodologies to predict the number of confirmed cases and deaths from Covid-19 in Chile and thus support the decisions of health agencies. Methods: We modeled the confirmed cases and deaths from Covid-19 in Chile by using ARIMA models, exponential smoothing techniques, Poisson models for time-dependent counting data. In addition, we evaluated the accuracy of the predictions by using a training set and test set. Results: The database used in this paper allows us to say that for the confirmed Covid-19 cases the best model corresponds to a well-known Autoregressive Integrated Moving Average (ARIMA) time-series model, whereas for deaths from Covid-19 in Chile the best model resulted in damped trend method. Conclusions: ARIMA models are an alternative to model the behavior of the spread of Covid-19, however, and depending on the characteristics of the data set, other methodologies can better capture the behavior of these records, for example, Holt-winter is method and time-dependent counting models.