Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

Abstract

SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

“…Benvenuto et al [23] applied an ARIMA model to predict the epidemiological trend of COVID-2019. Also, Saba and Elsheikhb [21] used this model to forecast the outbreak of COVID-19 in Egypt.…”

“…Mohammadinia et al [20] employed geographically weighted regression (GWR), generalized linear model (GLM), SVM, and ANN to develop a forecast map of leptospirosis; GWR and SVM produced highly accurate predictions. Saba and Elsheikh [21], also used the nonlinear autoregressive ANN model to forecast COVID-19 outbreak. Another statistical-based model that recently has been applied to forecast the behaviour of COVID-19 outbreak and death cases is ARIMA in which the forecast process is as a function of time.…”

“…Another statistical-based model that recently has been applied to forecast the behaviour of COVID-19 outbreak and death cases is ARIMA in which the forecast process is as a function of time. Recently, the significant ability of this model to forecast COVID-19 outbreak in Egypt [21] and coronavirus related deaths in Iran [22] has been reported. Benvenuto et al [23] performed ARIMA model on the Johns Hopkins epidemiological data and they found that the spread of virus tends to be slightly decreasing.…”

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

“…Saba and Elsheikh [7] studied statistical based method namely autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN) for modeling and forecasting the prevalence of this epidemic in Egypt. The confirmed cases are considered as time series data to train the proposed models.…”

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

“…A NARANN model was applied to forecast the total COVID-19 cases in Egypt ( Saba and Elsheikh, 2020 ). The model has a better accuracy than that of conventional ARIMA model.…”