Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models

24 Apr 2020Babacar Mbaye NdiayeLena TendengDiaraf Seck

In this paper, we propose a machine learning technics and SIR models (deterministic and stochastic cases) with numerical approximations to predict the number of cases infected with the COVID-19, for both in few days and the following three weeks. Like in [1] and based on the public data from [2], we estimate parameters and make predictions to help on how to find concrete actions to control the situation... (read more)

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