COVID-19 Fatality: A Cross-Sectional Study using Adaptive Lasso Penalized Sliced Inverse Regression
Volume 18, Issue 3 (2020): Special issue: Data Science in Action in Response to the Outbreak of COVID-19, pp. 483–495
Pub. online: 4 August 2022
Type: Research Article
Open Access
Published
4 August 2022
4 August 2022
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronvirus, which was declared as a global pandemic by the World Health Organization on March 11, 2020. In this work, we conduct a cross-sectional study to investigate how the infection fatality rate (IFR) of COVID-19 may be associated with possible geographical or demographical features of the infected population. We employ a multiple index model in combination with sliced inverse regression to facilitate the relationship between the IFR and possible risk factors. To select associated features for the infection fatality rate, we utilize an adaptive Lasso penalized sliced inverse regression method, which achieves variable selection and sufficient dimension reduction simultaneously with unimportant features removed automatically. We apply the proposed method to conduct a cross-sectional study for the COVID-19 data obtained from two time points of the outbreak.