With the exponential growth in the world population and the constant increase in human mobility, the possible impact of outbreaks of epidemics on cities is increasing, especially in high-density urban areas such as public transportation and transfer points. The volume and proximity of people in these areas can lead to an observed dramatic increase in the transmission of airborne viruses and related pathogens. Due to the critical role these areas play in transmission, it is vital that we have a comprehensive understanding of the ‘transmission highways’ in these areas to predict or prevent the spreading of infectious diseases in general. The principled approach of this paper is to combine and utilize as much information as possible from relevant sources and to integrate these data in a simulated environment that allows for scenario testing and decision support. In this paper, we describe a novel approach to study the spread of airborne diseases in cities by combining traffic information with geo-spatial data, infection dynamics and spreading characteristics. The system is currently being used in an attempt to understand the outbreak of influenza in densely populated cities in China. 
Simulating city-level airborne infectious diseases