The spread of infectious and bacterial diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of long range human mobility as well as the behavior of individuals on adopting policy driven or otherwise initiated mitigation strategies. Here, we study contagion dynamics through the air transportation network in a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. We develop a computational model to analyze and quantify the impact of different interventions against the diffusion of infections and bacterial diseases. We first develop an epidemic model to examine the impact of hand-washing in disease spreading scenarios and we then run Monte-Carlo simulations to assess the cost and effectiveness of hand-washing intervention in relation to mobility-driven interventions such as airport closures and individual travel rerouting. Furthermore, we rank the airports according to their influence on disease spreading, we identify the most influential global spreaders and we examine the changes in rankings under the different scenarios of mitigation strategies.
