A Surveillance and Spatiotemporal Visualization Model for Infectious Diseases Using Social Network

Abstract

In this paper, the authors propose a surveillance and spatiotemporal visualization system to simulate the infectious diseases spread which enables users to make decisions during a simulated pandemic. This system is based on compartment Susceptible, Exposed, Infected, and Removed (SEIR) model within a Small World network and Geographic Information System. The main advantage of this system is that it allows not only to understand how epidemic spreads in the human population and which risk factors promote this transmission but also to visualize epidemic outbreaks on the region's map. Experiments results reflect significantly the dynamical behavior of the influenza epidemic and the system can provide significant guidelines for decision makers when coping with epidemic diffusion controlling problems.