Application of the Integrated Autoregressive Method of Moving Averages for the analysis of series of cases of COVID-19 in Peru

Abstract

Introducción: In recent months, researchers have been using mathematical methods to predict the number of COVID-19 cases worldwide. Objective: To estimate an Integrated Autoregressive Moving Average model (ARIMA) for the analysis of series of COVID-19 cases, in Perul. Methods: The present study was based on a univariate time series analysis; The data used refer 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 absolute mean 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. Conclusion: The results obtained with the ARIMA model, compared with the observed data, show an adequate adjustment of the values; And although this model, easy to apply and interpret, does not simulate the exact behavior over time, it can be considered a simple and immediate tool to approximate the number of cases.