Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.

ABSTRACT: Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months. However, the rate of increase in cases was not equal for every country. The measures implemented by the countries changed the daily spreading speed of the disease. This was determined by changes in the number of daily cases. In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results. The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) an

SUBMITTER: YeSIlkanat CM 

PROVIDER: S-EPMC7439995 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature