This is a bit old but still worth noting: a piece from the James Martin 21st Century blog reports on Alessandro Vespignani's talk on "mobility, pandemics and the challenges of predicting the behaviour of techno-social systems."
Vespignani uses data networks to model the spread of diseases like H1N1; patterns of human mobility are key to understanding those of viruses as well. His group uses multiple sources of data – census data on commuter movements, demographics of population density, and airline schedules for long distance travel – to model and project travel patterns looking forward.
While our notions of predictions are often based on ones we know, such as weather forecasts, techno-social systems like the ones Vespignani studies are harder to predict. The knowledge that a storm is coming may cause us to stay inside that day, but the weather arrives regardless. Not so with infectious diseases, where a change in people's behaviour can alter the outcome – for both better and worse.
One of the slightly depressing things in the article was that while "containment may seem like a logical strategy during an outbreak, Vespignani presented mathematical models to show why it doesn't work. In order for it to be effective, travel would need to be restricted by more than 90%, which is simply not feasible in the modern world."