In this work, we use a classical SIR model to study COVID-19 pandemic. We aim to deal with the SIR model tting to COVID-19 data by using dierent technics and tools. We particularly use two ways: the rst one start by tting the total number of the conrmed cases and the second use a parametric solver tool. Finally a comparative forecasting, machine learning tools, is given.

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In this study, we present a new epidemiological model, with contamination from confirmed and unreported. We also compute equilibria and study their stability without intervention strategies. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal disease intervention strategies. We investigate the impact of distancing, case finding, and case holding controls while at the same time, we minimize the number of infected and dead individuals. The method consists of minimizing the cost functional related to infectious, death, and controls through some strategies to reduce the spread of the COVID19 epidemic.