Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures - Moulay A. Lmater; Mohamed Eddabbah; Tariq Elmoussaoui; Samia Boussaa (Elsevier, 2021)
Background & objective: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for predicting risks and decision-making processes. The purpose of this paper is to provide an effective mathematical model for predicting the spread of Covid-19 pandemic. Methods: Our mathematical model is performed according to a SIDR model for infectious diseases. Epidemiological data from four countries; Belgium, Morocco, Netherlands and Russia, are used to validate this model. Also, we have evaluated the efficiency of Morocco's Covid-19 countermeasures and simulated the different relaxation plans in order to predict the effects of relaxation countermeasures. Results and conclusions: In this paper, we developed and validated a new way of data aggregation, modeling and interpretation to predict the spread of Covid-19, evaluate the efficiency of countermeasures and suggest potential scenarios. Our results will be used to keep the spread of Covid-19 under control in the world.