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

In 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.