Identifying some factors associated with death due to COVID- 19 in Babies and Children by using Binary Logistic Regression

Despite the direct effect of the COVID-19 virus on children is uncommon so far, the indirect effect of the global COVID-19 pandemic can be catastrophic for children, causing considerable death and suffering. Many major causes of poor health and mortality in children were increased this year as a result of the pandemic and the response. At the same time, the capacity of governments, health frameworks, and humanitarian associations to respond to child health was decreased.

In this paper, the Binary Logistic Regression Analysis technique has been employed and applied to identify the factors leading to the babies and children’s deaths and building the best model for coronavirus disease data. A random sample size consists of 50 patients has been selected which 10 of them have died and the other 40 have survived. The results of the analysis showed that two variables out of eight variables were statistically significant which are Type of test and Complete Blood Count. The percentage of correct classifications was 85.7 %, which indicates the approximately high ability of the model for classification.