SIS-SEIQR Adaptive Network Model for Pandemic Influenza

摘要

This paper aims to present an SIS-SEIQR network model for pandemic influenza. We propose a network algorithm to generate an adaptive social network with dynamic hub nodes to capture the disease transmission in a human community. Effects of visiting probability on the spread of the disease are investigated. The results indicate that high visiting probability increases the transmission rate of the disease.