Impact studies of nationwide measures COVID-19 anti-pandemic: compartmental model and machine learning
This work addresses pandemic forecasting for public health decision-makers, but appears incremental as it applies existing methods to COVID-19 data.
The researchers analyzed the impact of nationwide COVID-19 measures by combining a compartmental model with machine learning to forecast pandemic evolution, showing comparisons between deterministic and machine learning approaches.
In this paper, we deal with the study of the impact of nationwide measures COVID-19 anti-pandemic. We drive two processes to analyze COVID-19 data considering measures. We associate level of nationwide measure with value of parameters related to the contact rate of the model. Then a parametric solve, with respect to those parameters of measures, shows different possibilities of the evolution of the pandemic. Two machine learning tools are used to forecast the evolution of the pandemic. Finally, we show comparison between deterministic and two machine learning tools.