The effects of community interactions and quarantine on a complex network

• Main Authors: Benjamas Chimmalee, Wannika Sawangtong, Benchawan Wiwatanapataphee
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2016-12-01
• Series: Cogent Mathematics
• Subjects: complex network; SEIQR model; disease transmission; quarantine
• Online Access: http://dx.doi.org/10.1080/23311835.2016.1249141

An adaptive complex network based on a susceptible–exposed–infectious–quarantine–recovered (SEIQR) framework is implemented to investigate the transmission dynamics of an infectious disease. The topology and its relations with network parameters are discussed. Using the primary outbreak data from the SEIQR network model, a regression model is developed to describe the relation between the quarantine rate and the key epidemiological parameters. We approximate the quarantine rate as a function of the number of individuals visiting communities and hubs, then incorporate it into the adaptive complex network to investigate the disease transmission dynamics. The proposed quantity can be estimated from the outbreak data and hence is more approachable as compared to the constant rate. Our results suggest that contact radius, hub radius, number of hubs, hub capacity and transmission rate may be important determinants to predict the disease spread and severity of outbreaks. Moreover, our results also suggest that during an outbreak, closing down certain public facilities or reducing their capacities and reducing the number of individuals visiting communities or public places, may significantly slow down the disease spread.