How can we evaluate an interrelation of symptoms?

ABSTRACT

A pandemic of 2019 novel coronavirus (COVID-19) is an international problem and factors associated with increased risk of mortality have been reported. However, there exists limited statistical method to estimate a comprehensive risk for a case in which a patient has several characteristics and symptoms concurrently. We applied Boolean Monte Carlo method (BMCM) to the Novel Corona Virus 2019 Dataset to determine interrelation of patient's characteristics and symptoms. In the analyses, age, fever as an onset symptom, and sex were used as explanatory variables, and death as the objective variable. Among 265 patients included in the analysis, the interrelations for estimating death were determined as age "and" fever "and" sex (p < 0.0001 for both operators). This result indicates that satisfying the three conditions of age, fever, and sex concurrently may be associated with an increased risk of mortality.