Background and objectives: The current pandemic of SARS-CoV-2 has not only changed, but also affected the lives of tens of millions of people around the world in these last nine to ten months. Although the situation is stable to some extent within the developed countries, approximately one million have already died as a consequence of the unique symptomatology that these people displayed. Thus, the need to develop an effective strategy for monitoring, restricting, but especially for predicting the evolution of COVID-19 is urgent, especially in middle-class countries such as Romania. Material and Methods: Therefore, autoregressive integrated moving average (ARIMA) models have been created, aiming to predict the epidemiological course of COVID-19 in Romania by using two statistical software (STATGRAPHICS Centurion (v.18.1.13) and IBM SPSS (v.20.0.0)). To increase the accuracy, we collected data between the established interval (1 March, 31 August) from the official website of the Romanian Government and the World Health Organization. Results: Several ARIMA models were generated from which ARIMA (1,2,1), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (2,2,2) and ARIMA (1,2,1) were considered the best models. For this, we took into account the lowest value of mean absolute percentage error (MAPE) for March, April, May, June, July, and August (MAPEMarch = 9.3225, MAPEApril = 0.975287, MAPEMay = 0.227675, MAPEJune = 0.161412, MAPEJuly = 0.243285, MAPEAugust = 0.163873, MAPEMarch - August = 2.29175 for STATGRAPHICS Centurion (v.18.1.13) and MAPEMarch = 57.505, MAPEApril = 1.152, MAPEMay = 0.259, MAPEJune = 0.185, MAPEJuly = 0.307, MAPEAugust = 0.194, and MAPEMarch - August = 6.013 for IBM SPSS (v.20.0.0) respectively. Conclusions: This study demonstrates that ARIMA is a useful statistical model for making predictions and provides an idea of the epidemiological status of the country of interest.
Keywords: COVID-19; SARS-CoV-2; epidemiology; forecasting; incidence; mortality rate; prevalence.