Statistical analysis and a social network model based on the SEIQR framework

Understanding, the spread of infectious diseases is an important key to efficiently control them. In this study, a susceptible-exposed-infectious-quarantined-recovered (SEIQR) model incorporated with adynamic social network is proposed to investigate the disease transmission dynamics in the human population and how the number of individual's neighbor (degree of a node), and the longest distance between any two neighboring nodes (the contact radius) influence the number of infectious individuals. Our results indicate that(l) the larger contact radius of an individual node leads to the higher number of infectious individuals (2) the degree of a node has significant effect on individual infection (the higher the degree of the node, the higher the possibility that individuals represented by those nodes spread the disease) and (3) the probability of successful infection can be estimated as a function of the degree of a node by the binary logistic regression model and we found that it may affect the outbreak period.