Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Abstract

The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.

Keywords: Global Pandemic; Kuhn-tucker; Linear Regression; Machine Learning; Nonlinear; Partial Derivative; Progressive.

Fig. 1

Block diagram of Partial Derivative Regression and Nonlinear Machine Learning (PDR-NML) method.

Fig. 2

Block diagram of Progressive Partial Derivative Linear Regression model.

Fig. 3

Flow diagram of Nonlinear Global Pandemic Machine Learning model.

Fig. 4

Graphical representation of prediction time.

Fig. 5

Graphical representation of prediction accuracy.