The COVID-19 is the third epidemic reported in the 21st century caused by a virus from the family of Coronaviridae after the SARS (Severe Acute Respiratory Syndrome) in 2003 and MERS (Middle-East Respiratory Syndrome) in 2012 . However, the propagation of this disease is faster than the two others. Since its first report in the city of Wuhan in China on December, 2019, the disease has been widely and rapidly reported in all countries and regions of the planet gaining the statute of a pandemic since March, 2020. The number of cases continues to increase rising a total of more than 84.4 millions and near than 2 millions deaths on January 3rd, 2021 .
In this way, it is important to understand the epidemic evolution and evaluate the protective measures applied by the national authorities. To attempt these objectives, mathematical and statistical models represents an inescapable part not only for prediction but also for planning control and mitigation actions. These models which use various sources of data allow us opportunities to test various strategies in simulations before their application in populations or individuals .
Since the first reports of COVID-19, various models were used to predict the curve evolution and to estimate the final size of the epidemic. Among these approaches, logistic growth models have been previously used to describe epidemics spread [4, 5]. These simpler tools are very popular and have been applied for COVID-19 short-term forecasting [6, 7, 8, 9] and also to predict the final size of the epidemic [10, 11, 12]. Roosa et al.  used a generalized logistic growth model to assess the impact of containment and predict final case numbers in China. Vattay  used the logistic growth model to analyze the similarity in Hubei, China and Italy in term of COVID-19 death numbers, and predicted the end date in Italy. Wu et al.  used four logistic growth models (the classical logistic growth model, the generalized logistic model, the generalized Richards model and the generalized growth model) to analyze the growth of COVID-19 in Chinese provinces using and further applied them to predict the number sizes in other European, American and Asian countries.
Roosa et al  used a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model for a 5- and 10-day prediction of cumulative cases in both Guangdong and Zhejiang (China). They observed that the GLM and Richards model showed comparable predictions, while the sub-epidemic model forecasts showed significantly greater uncertainty.
Balaban, 2020 compared the performance of 5 growth models like logistic growth, Von Bertalanffy growth, exponential growth, Gaussian growth and Richards growth models and used them for short term forecasting of COVID-19 in Turkey. The author showed that Von Bertalanffy model has the best performance but the exponential model has predicted the total deaths and cases better than the others. In the study of Zhou et al. , the authors used the logistic growth and the SEIR models to forecast the spread of COVID-19. They reported that the pandemic size estimated by the logistic model was considerably smaller than the SEIR models.
In another study, Batista  tried to estimate the final size of the epidemic for the whole World using logistic and SIR models. The same authors used the logistic model to forecast daily predictions and the epidemic size for China, South Korea, and the rest of the World .
Malhotra and Kashyap  calibrated the Susceptible-Infected-Recovered (SIR) model and the Logistic Growth model to forecast the endpoint of COVID-19 in India and three states.
Another tool regarded as one of the best tools to analyze and predict the pandemic growth is polynomial regression. This special type of multiple regression method was applied in several studies to analyze the behavior of COVID-19 [20, 21, 22, 23, 24]. It has shown 99.85% accuracy in the study of Yadav .
Prakash  used ANN and regression to model the COVID-19 pandemic in India and other countries like The USA, Italy and Spain. The authors observed that the results of polynomial regression follow the ground truths in India and the USA but not in Italy and Spain. They use also these models to estimate the peak of the epidemic in the cited countries.
Belfin et al, 2002  have used a SEIR and polynomial regression models to estimate the pick of the COVID-19 epidemic and the basic reproduction number in India respectively.
Also, Amar et al, 2020  applied seven regression models for the COVID-19 dataset including exponential polynomial, quadratic, third degree, fourth-degree, fifth-degree, sixth-degree, and logit growth. They reported that, the exponential, fourth-degree, fifth-degree, and sixth-degree polynomial regression models are excellent models (especially fourth-degree model).
In the same way Chakraborty et al, 2020  compared the performance between Linear Regression model, Granular Box Regression (GBR) and the Polynomial Regression model in predicting the spread of COVID-19 in India. The authors reported that the Polynomial regression model surpassed the two other models.
Algeria repotted its first case on February 25th, 2020. Since then, the actual situation shows a total number of 99897 cases and 2762 deaths . Despite the different preventive measures applied since March, 2020 the number of total cases is still increasing. Thus short and long term estimation of the number of cases and prediction of the curve evolution is of great importance to understand the epidemic curve. The current work is conducted to predict the number of case using a logistic growth model.