Artificial Intelligence Facing COVID-19 Pandemic for Decision Support in Algeria

In this paper, we presents a new mathematical formula for COVID-19 spread virus based on polynomial regression. Although this statistical estimation aims to fits a nonlinear model to the observed data vector (from Covid-19 data series provided from the World Health Organization (WHO)). Moreover, a survey based on the WHO's covid 19 symptoms and recommandations with ten(10) chosen questions is developed, in addition, the person's age is also taken into consideration. It aimed at constituting COVID-19 database from volunteers responses using the OCSS (Open COVID-19 Survey System), that is designed with Qt5 and coded with Python on Ubuntu system. Chosen individuals displacements parameter in the developed formula is based on the best fit correlation from the model and lifestyle state [number of walking per day (confined / unconfined)]. considering the reduced number of volunteers dataset (100), in order to enlarge the Learning database, we generate a 1000 synthetic random response score from OCSS system. The 100 real responses constitute the test database. Finally, using python Keras Sequential model Tensorflow backends, an opensource deep learning frameworks, to classify OCSS data and give better accuracy with the obtained model of 99.10 %, Loss:0.03. Test phase gives a high accuracy of 98.00 %, Loss:0.08. We propose a nearly four months (until Oct 14) forecasting based on the obtained model and proposed formula.