Since December 2019, the world is facing a new respiratory disease called coronavirus disease 2019 (COVID-19). This viral respiratory illness was first detected in the China’s city of Wuhan in the province of Hubei [1]. On January 30, 2020, the World health organization (WHO) has declared it as an epidemic of "public health emergency of international concern." Forty days later, on March 11th, it has finally recognized this disease as a global pandemic urging all countries to intensify their efforts to prevent its propagation stressing the importance of detection, testing, treatment, isolation, tracing and people mobilization in the anti-COVID-19 response [2]. As the number of affected persons is more than 34 million cases and 1,029,815 deaths through the world until now [3], this disease has attracted a global interest and enormous numbers of researches are in continuous struggle to understand its epidemiological and clinical characteristics. In addition to medical and experimental studies, researchers are using mathematical and statistical models to predict the number of affected persons, the peak and the ending time of the epidemic. The predicted scenarios can be used to assess the preventive measures which could be of great importance for decision-makers to adopt the best strategies in the anti-COVID-19 battle [4].
In this way, multiple models have been proposed for modeling COVID-19 pandemic in different countries including compartmental models, natural growth model and logistic growth models [5]. The SIR (susceptible–infectious–recovered) and SEIR (susceptible–exposed–infectious–recovered) historical compartmental models and their variants are the most used in forecasting human epidemic diseases [6]. They are also widely used in the case of COVID-19 [7]. However, these models have failed so far to give a good description of the empirical data [8]. Time series models (TSMs) are autoregressive moving average models which attempt to predict future events by means of aggregating recent data [8]. These models have shown many successful implementations in economics, finance, climatology, hydrology engineering and epidemiology [9]. The autoregressive integrated moving average (ARIMA), long short-term memory networks (LSTM) are the most commonly used for forecasting epidemic diseases [10]. These models have recently been widely used to forecast COVID-19 epidemic [11,12,13,14,15,16].
The aim of this study is to identify the data generating process of COVID-19 infections, fatalities and recoveries for Algeria and to forecast future scenarios about these variables. To our knowledge, there is no such study so far to forecast each variable of COVID-19 in Algeria; therefore, the findings of this research may help policy makers to reshape their polices according to the predicted scenario of COVID-19. Further, the results of this study can help various stakeholders including health and education department etc., in making their future’s plans.