Row and Column Effects Modelling of Elderly Age Groups and Chronic Health Problem on COVID-19
Abstract
Statistical analysis of COVID-19 data from China and NYC, using log-linear models, helps identifying high-risk groups like those aged over 65 and individuals with chronic health issues. According to the results of row effects model applied to the COVID-19 data set of China, we conclude that when the age group increases by one unit, the risk of getting COVID-19 disease is approximately 8 times higher for the patients having Chronic Obstructive Pulmonary Disease (COPD) than patients having hypertension, 9.37 times higher than patients with coronary heart disease, 13.37 times higher than patients having diabetes and cerebrovascular diseases and 10.16 times higher than patients having other diseases. According to the results of column effects model applied to the COVID-19 data set of NYC, we conclude that when the age group increases by one unit, the risk of death from the COVID-19 disease is approximately 2 times higher for the patients having choric health problem than the patients not having a chronic health problem. We believe that the empirical findings of the presented study will guide the policymakers to make provision for these disadvantageous groups for COVID-19 disease
Keywords
Categorical data analysis, Row effects model, Column effects model, COVID-19 data
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