Study of several key parameters responsible for COVID-19 outbreak using multiple regression analysis and multi-layer feed forward neural network. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Kumar, Ashish; Sinwar, Deepak; Saini, Monika
• Is Part Of: Journal of interdisciplinary mathematics. Volume 24:Issue 1(2021)
• Page Start: 53
• Page End: 75
• Publication Date: 2021-01-02
• Subjects: 62J05 -- 62H12; COVID-19 -- Multiple regression analysis -- Feed forward neural network -- Parameters; Mathematics -- Periodicals; Mathematics; Periodicals; 510.5
• DOI: 10.1080/09720502.2020.1833443
• Languages: English
• ISSNs: 0972-0502

Abstract

Abstract: The novel Corona Virus Disease (COVID-19) has critically influenced millions of human lives and economies globally. Governments around the globe are trying to cope with this pandemic and its adverse effects. Total symptomatic cases of the 2019 novel coronavirus (2019-nCoV) are growing at an exponential rate. There are several key parameters that are responsible for this COVID-19 outbreak which needs to be studied carefully. In this regard, this paper has presented a study of those parameters for finding out their impacts in calculating the total COVID-19 affected cases and deceases. Multiple Linear Regression and Multi-Layer Feed Forward Neural Network are used for this purpose. Analysis based on experimental study shown a strong correlation of age with total number of deaths; and population, diabetic prevalent with total number of cases. It is foremost required to focus on these parameters for minimizing the total affected cases and subsequent deaths due to COVID-19.