Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
This work addresses the need for accurate pandemic forecasting to inform public health actions, but it appears incremental as it builds on existing methods and data.
The authors tackled the problem of predicting COVID-19 case numbers by comparing machine learning techniques with deterministic and stochastic SIR models, using numerical approximations to forecast cases for short-term and three-week periods, with results indicating that the pandemic might end soon in some countries but persist until early May for most.
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.