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

• URL: http://arxiv.org/abs/2004.13489v1
• Date: Fri, 24 Apr 2020 22:54:10 GMT
• Title: Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
• Authors: Babacar Mbaye Ndiaye, Lena Tendeng, Diaraf Seck
• Abstract summary: We propose a machine learning technics and SIR models to predict the number of cases infected with the COVID-19. Under optimistic estimation, the pandemic in some countries will end soon, while for most of the countries in the world, the hit of anti-pandemic will be no later than the beginning of May.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Under optimistic estimation, the pandemic in some countries will end soon, while for most of the countries in the world, the hit of anti-pandemic will be no later than the beginning of May.

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