Forecasting of COVID-19 Epidemic Process by Random Forest Method

Abstract

The new coronavirus has changed the life of the planet and continues to spread around the world. Mathematical modeling allows the development of effective scientifically substantiated preventive and anti-epidemic measures. Machine learning methods have the highest accuracy when constructing the predicted incidence of infectious diseases. In this work, a model of a random forest was built to calculate the predicted incidence of COVID-19. To verify the model, data on the incidence of coronavirus in Ukraine, Great Britain, Germany and Japan were used. These countries were chosen because have different dynamics of the epidemic process and different control measures.