Prediction of the Effective Factors on the Incidence of COVID-19 Disease in Hospitalized Patients using Statistical Modeling

The COVID-19 pandemic has had a significant impact on the well-being of different individuals and nations in terms of socioeconomic, psychological, and public health issues. Regarding the importance of predicting the peak outbreaks and incidence of COVID-19, this study aimed to predict factors that affect the incidence of COVID-19 pandemic in hospitalized patients admitted to a hospital in Khorramabad city of Iran, using a multivariate logistic regression model.

In this descriptive-analytical study, the data from 4,425 COVID-19 patients in the first peak of the disease, who were referred to the Shohada-ye Ashayer Hospital, Khorramabad, Iran, in 2020, were examined, from whom 2,978 people had undergone COVID-19 test. Data were collected using a researcher-made checklist for variables and examination of patients’ daily records. Data analysis was performed using descriptive methods, multivariate logistic regression model, and backup vector machine method in the R software for the modeling of risk factors associated with the disease diagnosis.

The highest and lowest rates of incidence were observed in the age range of 40-49 (19%) and 19-10 years (1%).  The age group 60-69 years accounted for 6% of the population with a mortality rate of 25%. Based on the implemented model, the most effective symptoms associated with the incidence of COVID-19 included age, fever, decreased level of consciousness, blood oxygen level, and a history of heart disease.

The present study showed that identification of the effective variables of the disease led to the identification of high-risk individuals. This method can be used to prevent disease incidence and prevalence in high-risk groups and is a great help in controlling the COVID-19 pandemic.