Article: Developing a machine learning framework to determine the spread of COVID-19 in the USA using meteorological, social, and demographic factors Journal: International Journal of Data Mining, Modelling and Management (IJDMMM) 2022 Vol.14 No.2 pp.89 - 109 Abstract: Coronavirus disease of 2019 (COVID-19) has become a 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 nonlinear nature of the problem, the radial kernel SVM performs the best and explains 95% more variation than the existing methods. In line 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. Inderscience Publishers - linking academia, business and industry through research

Title: Developing a machine learning framework to determine the spread of COVID-19 in the USA using meteorological, social, and demographic factors

Authors: Akash Gupta; Amir Gharehgozli

Addresses: David Nazarian College of Business and Economics, California State University, 18111 Nordhoff Street, Northridge, CA 91330-8378, USA ' David Nazarian College of Business and Economics, California State University, 18111 Nordhoff Street, Northridge, CA 91330-8378, USA

Abstract: Coronavirus disease of 2019 (COVID-19) has become a 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 nonlinear nature of the problem, the radial kernel SVM performs the best and explains 95% more variation than the existing methods. In line 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.

Keywords: COVID-19; disease spread; social and demographic factors; machine learning; epidemiology; predictive modelling.

DOI: 10.1504/IJDMMM.2022.123360

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.2, pp.89 - 109

Received: 02 Jun 2020
Accepted: 20 Nov 2020
Published online: 11 Jun 2022 *

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Title: Developing a machine learning framework to determine the spread of COVID-19 in the USA using meteorological, social, and demographic factors

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