Constructing a Multilevel Modeling to High-Resolution CT (HRCT) Lung in Patients with COVID-19 Infection

Abstract

The coronavirus disease, also called COVID-19 is caused by the SARS-CoV-2 virus. Most the people contaminated with the virus will experience mild to moderate symptoms of respiratory diseases. The aim of this paper is constructing a model by multilevel modeling for these patients who sufferers by coronaviruses, we got seven hospitals which totals (636) patients in private and public that 27% from Erbil, 26% from Sulaimani, 23% from Duhok and 24% from Halabja from the period (September 1th, 2019 to February 1th, 2022). In these modelling of multilevel restricted maximum likelihood estimation (RMLE) and full maximum likelihood (FML) acclimate estimate the parameters of multilevel models (fixed and random). The application was on the HRCT lungs of patients, seven hospitals were selected randomly among the county in Kurdistan region of Iraq. The result shows that all three variables are significant at the hospital level, but in the two final models add level-2 predictor (Doctor Experience) that interaction with level-1 predictor (smoker), which is far from significant. However, there is a significant relationship between being a diabetic and having a CT scan, but the relationship between smoking and having a CT scan is not significant.

Keywords

• multilevel modeling
• fixed effect
• random effect
• interclass correlation