Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning

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

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