Adaptive elastic‐net sliced inverse regression to identify risk factors affecting COVID‐19 disease fatality rate

Abstract

AbstractIn this article, we proposed a plan based on Adaptive Elastic‐net Sliced Inverse Regression to identify risk factors for the coronavirus disease (Covid‐19) disease in the presence of collinearity between explanatory variables. Considering the penalty of elastic‐net and sliced inverse regression, this method leads to sufficient dimension reduction and the presentation of a more stable and accurate model for variable selection.We applied the proposed method to simulated data and a new real‐world Covid‐19 disease dataset. We observed that the proposed method reduced the experimental standard error of bootstrapping by 12\% and 13\% compared to the previous superior methods in this approach, respectively, for both datasets. According to the results, during the outbreak of the Covid disease and its re‐intensification, countries should quickly implement the following policies: declaring quarantine with minimal exceptions, making vaccines available by prioritizing specific groups, declaring a ban on gatherings, especially gatherings of more than 1000 people, closing schools at all levels, closing some works or declaring remote work, and holding information campaigns. Especially countries with more 0‐14‐year‐old population, higher life expectancy, lower human development index, and colder weather should make more serious decisions in their implementation because they are more at risk.