Introduction:

In recent months, researchers have been using mathematical methods to forecast the number of COVID-19 cases worldwide.

Objective:

To estimate an Autoregressive integrated moving average (ARIMA) to analyze a series of COVID-19 cases in Peru.

Methods:

The present study was based on a univariate time series analysis; The data used refers to the number of new accumulated cases of COVID-19 from March 6 to June 11, 2020. For the analysis of the fit of the model, the autocorrelation coefficients (ACF), the unit root test of Augmented Dickey-Fuller (ADF), the Normalized Bayesian Information Criterion (Normalized BIC), the mean absolute percentage error (MAPE), and the Box-Ljung test.

Results:

The prognosis for COVID-19 cases, between June 12 and July 11, 2020, ranges from 220 596 to 429 790.

Conclusions:

The results obtained with the ARIMA model, compared with the observed data, show an adequate adjustment of the values. Although this model is easy to apply and interpret, it does not simulate the exact behavior over time. It can be considered a simple and immediate tool to approximate the number of cases.

        · abstract in Spanish     · text in English | Spanish     · English ( pdf ) | Spanish ( pdf )