I first learnt the power of stochastic models when I had lectures by the late Professor David Kendall (famed for the Kendall notation for queue models http://en.wikipedia.org/wiki/Kendall%27s_notation). He spoke about a simple model of an epidemic, and how that model gave rise to a statistical distribution for the number of people who would be infected. To add variety to his lectures, he spoke of the way that the model could be modified to describe the spread of a rumour (when someone spreads the message to someone who has already heard it, he realises it is a rumour and stops spreading it).
(Incidentally, I wonder whether David Kendall and I come from the same branch of the Kendall family, as my mother was a Kendall before she married.)
Since then, models of epidemics have become very much more sophisticated, but the essentials remain the same. As I write, the UK government is concerned with the spread of influenza in the population. The Labour opposition claims that the advertising about risk should have been started much earlier. In response, the government have spoken about the need to time the adverts for greatest effectiveness. I suspect that there are O.R. models in the background.
Advertising too long in advance is of limited value -- it will be forgotten. It needs to be at a time when it may affect the behaviour of people, who may be infected or susceptible. So the timing needs to be linked to models of the spread of the current 'flu virus. Hence two models need to be linked -- one about the effectiveness of advertising, the other an epidemiological one. But these models are bound to be in the background and, once again, O.R. is the "hidden science".