Internet-based disease surveillance is a tool providing early warning about infectious disease outbreaks. There are variations, but the common idea is to automatically monitor Internet sources (news, blogs, etc.), searching for articles containing keywords related to infectious diseases. Natural language processing is then used to pinpoint the location being mentioned, eliminate duplicates, etc. Some systems additionally have human input to weed out false positives. In all instances, though, these systems produce a large amount of alerts. I will discuss ongoing work using stochastic metapopulation models for the global spread of infectious pathogens along the global air transportation network. I will show in particular how such models can be used to help filter the large number of alerts generated by Internet-trawling surveillance systems.