A STUDY ON COVID-19 DATA WITH LOG-LINEAR MODEL APPROACH

Abstract

The researchers have done several studies to explore the important variables on the novel coronavirus (COVID-19) disease. However, there is no direct research to examine the interaction between the variables affecting the COVID-19 disease. In this study, the log-linear models are used to explore the significant interactions between the country, gender and age variables. Empirical findings of the presented study show that the main effects are found statistically significant for country, gender and age variables. The two-way interactions between the gender and country and between the interaction of the country and age are found statistically significant. The risk of the China for catching the COVID-19 disease is 1.7 times higher than the risk of the South Korea for catching the COVID-19 disease. The risk of male individuals is 1.2 times higher than the risk of female individuals for catching the COVID-19 disease. Additionally, the individuals having the 41-60 age group has higher risk than the individuals having 70 and above age group. We believe that the empirical results of the presented study will be helpful to the policymakers for decreasing the spread of the COVID-19 disease.

Keywords

• Categorical data analysis
• Log-linear models
• COVID-19
• Outbreak

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