Developing a Machine Learning Framework to Determine the Spread of COVID-19

Coronavirus disease of 2019 (COVID-19) has become pandemic in the matter of a few months, since the outbreak in December 2019 in Wuhan, China. We study the impact of weather factors including temperature and pollution on the spread of COVID-19. We also include social and demographic variables such as per capita Gross Domestic Product (GDP) and population density. Adapting the theory from the field of epidemiology, we develop a framework to build analytical models to predict the spread of COVID-19. In the proposed framework, we employ machine learning methods including linear regression, linear kernel support vector machine (SVM), radial kernel SVM, polynomial kernel SVM, and decision tree. Given the non-linear nature of the problem, the radial kernel SVM performs the best and explains 95% more variation than the existing methods. In align with the literature, our study indicates the population density is the critical factor to determine the spread. The univariate analysis shows that a higher temperature, air pollution, and population density can increase the spread. On the other hand, a higher per capita GDP can decrease the spread.