Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting
This work addresses pandemic forecasting for public health planning, but it appears incremental as it combines standard models without clear methodological innovation.
The authors tackled forecasting the COVID-19 pandemic by applying the SIR model and machine learning techniques to public data, predicting inflection points and potential end times, with optimistic estimates suggesting some countries might end soon while others like the US and Italy could see impacts by late April.
This work is a trial in which we propose SIR model and machine learning tools to analyze the coronavirus pandemic in the real world. Based on the public data from \cite{datahub}, we estimate main key pandemic parameters and make predictions on the inflection point and possible ending time for the real world and specifically for Senegal. The coronavirus disease 2019, by World Health Organization, rapidly spread out in the whole China and then in the whole world. Under optimistic estimation, the pandemic in some countries will end soon, while for most part of countries in the world (US, Italy, etc.), the hit of anti-pandemic will be no later than the end of April.