Selected article for: "Absolute shrinkage and Selection operator Absolute shrinkage regression"
Author: Shimul, S. N.; Kadir, F.; Ihsan- Ul- Kabir, M.
Title: Factors Associated with COVID-19 Deaths and Infections: A Cross Country Evidence
  • Cord-id: 6te6l1a4
  • Document date: 2020_11_4
    • ID: 6te6l1a4
    Snippet: Though most of the countries across the world are crippled with COVID-19, there has been substantial variations in death and infection rates. While some countries are overwhelmed, a few are spared. Little is known what explains this variation. This study attempts to understand the covariates of death and infection rates of COVID-19 across countries using multivariate regression analysis and least absolute shrinkage and selection operator (LASSO) regression. The OLS estimates show that the aging
    Document: Though most of the countries across the world are crippled with COVID-19, there has been substantial variations in death and infection rates. While some countries are overwhelmed, a few are spared. Little is known what explains this variation. This study attempts to understand the covariates of death and infection rates of COVID-19 across countries using multivariate regression analysis and least absolute shrinkage and selection operator (LASSO) regression. The OLS estimates show that the aging population and hospital bed per capita are significantly associated with the fatality rate of COVID-19, while urbanization has a positive correlation with the inflection rate. The study suggests that an increase in health systems capacity can significantly reduce the fatality rates due to COVID-19.

    Search related documents:
    Co phrase search for related documents
    • actual outbreak source population flow and machine learning: 1
    • acute ards respiratory distress syndrome and addition hospital: 1
    • acute ards respiratory distress syndrome and long term develop: 1, 2
    • acute ards respiratory distress syndrome and low income: 1, 2, 3, 4, 5, 6, 7, 8
    • acute ards respiratory distress syndrome and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
    • acute ards respiratory distress syndrome and machine learning method: 1
    • addition hospital and low income: 1, 2
    • addition hospital and machine learning: 1, 2
    • lmics middle income low country and low income: 1, 2
    • lmics perform and low income: 1, 2
    • local infrastructure and low income: 1, 2, 3, 4
    • local infrastructure and machine learning: 1
    • london new york and low income: 1
    • london new york and macro economic: 1
    • low income and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • low income and machine learning method: 1
    • low income and macro economic: 1
    • machine learning and macro economic: 1, 2, 3