ISSN: 2639-2038
Authors: Raghul Gandhi V* and Bagavandas M
Corona Virus disease (Covid-19) is a widespread pandemic disease across the world. The death rates in the case of Covid-19 strongly depend on the comorbidity of an individual. The primary objective is to identify and examine the comorbidity patterns in 137 deceased patients of Chennai City, India and to detect the patterns among the co-morbidities using Social Network Analysis by considering co-occurrence of diseases as relational data. Network metrics such as degree which measures the importance of a node and betweenness which identifies the well-connected node are used to determine occurrence of comorbidities among the covid-19 deceased patients and another metric modularity is used to detect the patterns among the comorbidities. Diabetic and Hypertension are the commonly occurred comorbidities in the deceased patients due to Covid-19 by degree metrics and 3 comorbidity patterns were found by modularity. This study establishes that Social Network Analysis can be used as a potential tool in epidemiological research by identifying co-morbidity patterns among covid-19 deceased patients. Also it establishes the possibility of visualizing networks and making inference on patterns of among co-morbidities based on values of different metric measurements.
Keywords: Covid-19; Epidemiology; Comorbidity; Social Networks; Degree; Betweenness; Modularity; Density
