Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models

Corona virus (Covid-19) is a great danger for whole world. World health organization (WHO) considered it an epidemic. Data collection was based on the reports of World health organization for Covid-19 in Egypt. The problem of this study is to describe actual behavior of the virus using an appropriate statistical model. As WHO stated, Covid-19 behaves in the form of waves, therefore we thought that we should pay attention to seasonal and periodical models when identifying an appropriate model for this virus. The aim of this article is to introduce and study Periodical Autoregressive integrated Moving Average (PARIMA) models and compare them with the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to find optimal or approximately optimal model helps to predict the epidemiological behavior of the prevalence and so find reliable future forecasts of the number of Covid-19 injuries in Egypt. A numerical study using real data analysis is performed to establish an appropriate PARIMA model. The results supported the reliance of PAR (7) odel and its use for the purpose of forecasting. Extensive comparisons have been made between the estimated PARIMA model and some other advanced time series models. The forecasts obtained from the estimated PARIMA model were compared with the forecasts obtained from ARIMA (2, 2, 2) and SARIMA (1, 2, 1), (0, 0,1) models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.