Additional file 2: Figure S2 . Surveillance model.  The impact of media coverage on the transmission dynamics of human influenza BACKGROUND: There is an urgent need to understand how the provision of information influences individual risk perception and how this in turn shapes the evolution of epidemics. Individuals are influenced by information in complex and unpredictable ways. Emerging infectious diseases, such as the recent swine flu epidemic, may be particular hotspots for a media-fueled rush to vaccination; conversely, seasonal diseases may receive little media attention, despite their high mortality rate, due to their perceived lack of newness. METHODS: We formulate a deterministic transmission and vaccination model to investigate the effects of media coverage on the transmission dynamics of influenza. The population is subdivided into different classes according to their disease status. The compartmental model includes the effect of media coverage on reporting the number of infections as well as the number of individuals successfully vaccinated. RESULTS: A threshold parameter (the basic reproductive ratio) is analytically derived and used to discuss the local stability of the disease-free steady state. The impact of costs that can be incurred, which include vaccination, education, implementation and campaigns on media coverage, are also investigated using optimal control theory. A simplified version of the model with pulse vaccination shows that the media can trigger a vaccinating panic if the vaccine is imperfect and simplified messages result in the vaccinated mixing with the infectives without regard to disease risk. CONCLUSIONS: The effects of media on an outbreak are complex. Simplified understandings of disease epidemiology, propogated through media soundbites, may make the disease significantly worse. Infectious diseases are responsible for a quarter of all deaths in the world annually, the vast majority occurring in low-and middle-income countries [1] . There are diseases such as SARS and flu that exhibit some distinct features such as rapid spatial spread and visible symptoms [2] . These features, associated with the increasing trend of globalization and the development of information technology, are expected to be shared by other emerging/ re-emerging infectious diseases. It is therefore important to refine classical mathematical models to reflect these features by adding the dimensions of massive news coverage that have great influence not only on the individual behaviours but also on the formation and implementation of public intervention and control policies [2] .